fix: lazy init MLX for quantization and improve library discovery

- Add lazy MLX initialization in quantizeTensor to ensure the library
  is loaded when quantization is requested
- Add exe-relative build path search for dev mode on macOS, so the
  ollama binary can find libmlxc.dylib in build/lib/ollama/ when
  running from the repo root

.github/ISSUE_TEMPLATE/10_bug_report.yml

@@ -13,7 +13,7 @@ body:
     id: logs
     attributes:
       label: Relevant log output
-      description: Please copy and paste any relevant log output. See [Troubleshooting Guide](https://github.com/ollama/ollama/blob/main/docs/troubleshooting.md#how-to-troubleshoot-issues) for details.
+      description: Please copy and paste any relevant log output. See [Troubleshooting Guide](https://github.com/ollama/ollama/blob/main/docs/troubleshooting.mdx#how-to-troubleshoot-issues) for details.
       render: shell
     validations:
       required: false

.github/workflows/release.yaml

@@ -372,13 +372,17 @@ jobs:
           outputs: type=local,dest=dist/${{ matrix.os }}-${{ matrix.arch }}
           cache-from: type=registry,ref=${{ vars.DOCKER_REPO }}:latest
           cache-to: type=inline
+      - name: Deduplicate CUDA libraries
+        run: |
+          ./scripts/deduplicate_cuda_libs.sh dist/${{ matrix.os }}-${{ matrix.arch }}
       - run: |
           for COMPONENT in bin/* lib/ollama/*; do
             case "$COMPONENT" in
-              bin/ollama)                echo $COMPONENT >>ollama-${{ matrix.os }}-${{ matrix.arch }}.tar.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_v*)        echo $COMPONENT >>ollama-${{ matrix.os }}-${{ matrix.arch }}.tar.in ;;
               lib/ollama/vulkan*)        echo $COMPONENT >>ollama-${{ matrix.os }}-${{ matrix.arch }}.tar.in ;;
+              lib/ollama/mlx*)           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 ;;

CMakeLists.txt

@@ -48,9 +48,10 @@ if((CMAKE_OSX_ARCHITECTURES AND NOT CMAKE_OSX_ARCHITECTURES MATCHES "arm64")
     set(GGML_CPU_ALL_VARIANTS ON)
 endif()
 
-if (CMAKE_OSX_ARCHITECTURES MATCHES "x86_64")
+if(APPLE)
     set(CMAKE_BUILD_RPATH "@loader_path")
     set(CMAKE_INSTALL_RPATH "@loader_path")
+    set(CMAKE_BUILD_WITH_INSTALL_RPATH ON)
 endif()
 
 set(OLLAMA_BUILD_DIR ${CMAKE_BINARY_DIR}/lib/ollama)
@@ -189,13 +190,21 @@ if(MLX_ENGINE)
     install(TARGETS mlx mlxc
         RUNTIME_DEPENDENCIES
             DIRECTORIES ${CUDAToolkit_BIN_DIR} ${CUDAToolkit_BIN_DIR}/x64 ${CUDAToolkit_LIBRARY_DIR}
-            PRE_INCLUDE_REGEXES cublas cublasLt cudart nvrtc cudnn nccl
+            PRE_INCLUDE_REGEXES cublas cublasLt cudart nvrtc nvrtc-builtins cudnn nccl openblas gfortran
             PRE_EXCLUDE_REGEXES ".*"
         RUNTIME DESTINATION ${OLLAMA_INSTALL_DIR} COMPONENT MLX
         LIBRARY DESTINATION ${OLLAMA_INSTALL_DIR} COMPONENT MLX
         FRAMEWORK DESTINATION ${OLLAMA_INSTALL_DIR} COMPONENT MLX
     )
 
+    # Install the Metal library for macOS arm64 (must be colocated with the binary)
+    # Metal backend is only built for arm64, not x86_64
+    if(APPLE AND CMAKE_SYSTEM_PROCESSOR STREQUAL "arm64")
+        install(FILES ${CMAKE_BINARY_DIR}/_deps/mlx-build/mlx/backend/metal/kernels/mlx.metallib
+            DESTINATION ${OLLAMA_INSTALL_DIR}
+            COMPONENT MLX)
+    endif()
+
     # Manually install cudart and cublas since they might not be picked up as direct dependencies
     if(CUDAToolkit_FOUND)
         file(GLOB CUDART_LIBS

Dockerfile

@@ -32,7 +32,7 @@ ENV PATH=/${VULKANVERSION}/x86_64/bin:$PATH
 FROM --platform=linux/arm64 almalinux:8 AS base-arm64
 # install epel-release for ccache
 RUN yum install -y yum-utils epel-release \
-    && dnf install -y clang ccache \
+    && dnf install -y clang ccache git \
     && yum-config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/rhel8/sbsa/cuda-rhel8.repo
 ENV CC=clang CXX=clang++
 
@@ -149,6 +149,7 @@ COPY CMakeLists.txt CMakePresets.json .
 COPY ml/backend/ggml/ggml ml/backend/ggml/ggml
 COPY x/ml/backend/mlx x/ml/backend/mlx
 COPY go.mod go.sum .
+COPY MLX_VERSION .
 RUN curl -fsSL https://golang.org/dl/go$(awk '/^go/ { print $2 }' go.mod).linux-$(case $(uname -m) in x86_64) echo amd64 ;; aarch64) echo arm64 ;; esac).tar.gz | tar xz -C /usr/local
 ENV PATH=/usr/local/go/bin:$PATH
 RUN go mod download
@@ -156,11 +157,6 @@ RUN --mount=type=cache,target=/root/.ccache \
     cmake --preset 'MLX CUDA 13' -DBLAS_INCLUDE_DIRS=/usr/include/openblas -DLAPACK_INCLUDE_DIRS=/usr/include/openblas \
         && cmake --build --parallel ${PARALLEL} --preset 'MLX CUDA 13' \
         && cmake --install build --component MLX --strip --parallel ${PARALLEL}
-COPY . .
-ARG GOFLAGS="'-ldflags=-w -s'"
-ENV CGO_ENABLED=1
-ARG CGO_CFLAGS
-ARG CGO_CXXFLAGS
 
 FROM base AS build
 WORKDIR /go/src/github.com/ollama/ollama
@@ -169,12 +165,14 @@ RUN curl -fsSL https://golang.org/dl/go$(awk '/^go/ { print $2 }' go.mod).linux-
 ENV PATH=/usr/local/go/bin:$PATH
 RUN go mod download
 COPY . .
+# Clone mlx-c headers for CGO (version from MLX_VERSION file)
+RUN git clone --depth 1 --branch "$(cat MLX_VERSION)" https://github.com/ml-explore/mlx-c.git build/_deps/mlx-c-src
 ARG GOFLAGS="'-ldflags=-w -s'"
 ENV CGO_ENABLED=1
-ARG CGO_CFLAGS
+ENV CGO_CFLAGS="-I/go/src/github.com/ollama/ollama/build/_deps/mlx-c-src"
 ARG CGO_CXXFLAGS
 RUN --mount=type=cache,target=/root/.cache/go-build \
-    go build -trimpath -buildmode=pie -o /bin/ollama .
+    go build -tags mlx -trimpath -buildmode=pie -o /bin/ollama .
 
 FROM --platform=linux/amd64 scratch AS amd64
 # COPY --from=cuda-11 dist/lib/ollama/ /lib/ollama/

MLX_VERSION

@@ -0,0 +1 @@
+v0.4.1

README.md

@@ -48,7 +48,7 @@ ollama run gemma3
 
 ## Model library
 
-Ollama supports a list of models available on [ollama.com/library](https://ollama.com/library 'ollama model library')
+Ollama supports a list of models available on [ollama.com/library](https://ollama.com/library "ollama model library")
 
 Here are some example models that can be downloaded:
 
@@ -79,7 +79,7 @@ Here are some example models that can be downloaded:
 | Code Llama         | 7B         | 3.8GB | `ollama run codellama`           |
 | Llama 2 Uncensored | 7B         | 3.8GB | `ollama run llama2-uncensored`   |
 | LLaVA              | 7B         | 4.5GB | `ollama run llava`               |
-| Granite-3.3         | 8B         | 4.9GB | `ollama run granite3.3`          |
+| Granite-3.3        | 8B         | 4.9GB | `ollama run granite3.3`          |
 
 > [!NOTE]
 > You should have at least 8 GB of RAM available to run the 7B models, 16 GB to run the 13B models, and 32 GB to run the 33B models.
@@ -260,6 +260,38 @@ Finally, in a separate shell, run a model:
 ./ollama run llama3.2
 ```
 
+## Building with MLX (experimental)
+
+First build the MLX libraries:
+
+```shell
+cmake --preset MLX
+cmake --build --preset MLX --parallel
+cmake --install build --component MLX
+```
+
+When building with the `-tags mlx` flag, the main `ollama` binary includes MLX support for experimental features like image generation:
+
+```shell
+go build -tags mlx .
+```
+
+Finally, start the server:
+
+```
+./ollama serve
+```
+
+### Building MLX with CUDA
+
+When building with CUDA, use the preset "MLX CUDA 13" or "MLX CUDA 12" to enable CUDA with default architectures:
+
+```shell
+cmake --preset 'MLX CUDA 13'
+cmake --build --preset 'MLX CUDA 13' --parallel
+cmake --install build --component MLX
+```
+
 ## REST API
 
 Ollama has a REST API for running and managing models.
@@ -290,6 +322,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
 
 ### Web & Desktop
 
+- [Onyx](https://github.com/onyx-dot-app/onyx)
 - [Open WebUI](https://github.com/open-webui/open-webui)
 - [SwiftChat (macOS with ReactNative)](https://github.com/aws-samples/swift-chat)
 - [Enchanted (macOS native)](https://github.com/AugustDev/enchanted)
@@ -421,7 +454,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [AppFlowy](https://github.com/AppFlowy-IO/AppFlowy) (AI collaborative workspace with Ollama, cross-platform and self-hostable)
 - [Lumina](https://github.com/cushydigit/lumina.git) (A lightweight, minimal React.js frontend for interacting with Ollama servers)
 - [Tiny Notepad](https://pypi.org/project/tiny-notepad) (A lightweight, notepad-like interface to chat with ollama available on PyPI)
-- [macLlama (macOS native)](https://github.com/hellotunamayo/macLlama) (A native macOS GUI application for interacting with Ollama models, featuring a chat interface.) 
+- [macLlama (macOS native)](https://github.com/hellotunamayo/macLlama) (A native macOS GUI application for interacting with Ollama models, featuring a chat interface.)
 - [GPTranslate](https://github.com/philberndt/GPTranslate) (A fast and lightweight, AI powered desktop translation application written with Rust and Tauri. Features real-time translation with OpenAI/Azure/Ollama.)
 - [ollama launcher](https://github.com/NGC13009/ollama-launcher) (A launcher for Ollama, aiming to provide users with convenient functions such as ollama server launching, management, or configuration.)
 - [ai-hub](https://github.com/Aj-Seven/ai-hub) (AI Hub supports multiple models via API keys and Chat support via Ollama API.)
@@ -493,7 +526,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
 ### Database
 
 - [pgai](https://github.com/timescale/pgai) - PostgreSQL as a vector database (Create and search embeddings from Ollama models using pgvector)
-   - [Get started guide](https://github.com/timescale/pgai/blob/main/docs/vectorizer-quick-start.md)
+  - [Get started guide](https://github.com/timescale/pgai/blob/main/docs/vectorizer-quick-start.md)
 - [MindsDB](https://github.com/mindsdb/mindsdb/blob/staging/mindsdb/integrations/handlers/ollama_handler/README.md) (Connects Ollama models with nearly 200 data platforms and apps)
 - [chromem-go](https://github.com/philippgille/chromem-go/blob/v0.5.0/embed_ollama.go) with [example](https://github.com/philippgille/chromem-go/tree/v0.5.0/examples/rag-wikipedia-ollama)
 - [Kangaroo](https://github.com/dbkangaroo/kangaroo) (AI-powered SQL client and admin tool for popular databases)
@@ -636,6 +669,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [llama.cpp](https://github.com/ggml-org/llama.cpp) project founded by Georgi Gerganov.
 
 ### Observability
+
 - [Opik](https://www.comet.com/docs/opik/cookbook/ollama) is an open-source platform to debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive tracing, automated evaluations, and production-ready dashboards. Opik supports native integration to Ollama.
 - [Lunary](https://lunary.ai/docs/integrations/ollama) is the leading open-source LLM observability platform. It provides a variety of enterprise-grade features such as real-time analytics, prompt templates management, PII masking, and comprehensive agent tracing.
 - [OpenLIT](https://github.com/openlit/openlit) is an OpenTelemetry-native tool for monitoring Ollama Applications & GPUs using traces and metrics.
@@ -644,4 +678,5 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [MLflow Tracing](https://mlflow.org/docs/latest/llms/tracing/index.html#automatic-tracing) is an open source LLM observability tool with a convenient API to log and visualize traces, making it easy to debug and evaluate GenAI applications.
 
 ### Security
+
 - [Ollama Fortress](https://github.com/ParisNeo/ollama_proxy_server)

api/client.go

@@ -165,7 +165,7 @@ func (c *Client) do(ctx context.Context, method, path string, reqData, respData
 	return nil
 }
 
-const maxBufferSize = 512 * format.KiloByte
+const maxBufferSize = 8 * format.MegaByte
 
 func (c *Client) stream(ctx context.Context, method, path string, data any, fn func([]byte) error) error {
 	var buf io.Reader

api/types.go

@@ -127,6 +127,20 @@ type GenerateRequest struct {
 	// each with an associated log probability. Only applies when Logprobs is true.
 	// Valid values are 0-20. Default is 0 (only return the selected token's logprob).
 	TopLogprobs int `json:"top_logprobs,omitempty"`
+
+	// Experimental: Image generation fields (may change or be removed)
+
+	// Width is the width of the generated image in pixels.
+	// Only used for image generation models.
+	Width int32 `json:"width,omitempty"`
+
+	// Height is the height of the generated image in pixels.
+	// Only used for image generation models.
+	Height int32 `json:"height,omitempty"`
+
+	// Steps is the number of diffusion steps for image generation.
+	// Only used for image generation models.
+	Steps int32 `json:"steps,omitempty"`
 }
 
 // ChatRequest describes a request sent by [Client.Chat].
@@ -860,6 +874,20 @@ type GenerateResponse struct {
 	// Logprobs contains log probability information for the generated tokens,
 	// if requested via the Logprobs parameter.
 	Logprobs []Logprob `json:"logprobs,omitempty"`
+
+	// Experimental: Image generation fields (may change or be removed)
+
+	// Image contains a base64-encoded generated image.
+	// Only present for image generation models.
+	Image string `json:"image,omitempty"`
+
+	// Completed is the number of completed steps in image generation.
+	// Only present for image generation models during streaming.
+	Completed int64 `json:"completed,omitempty"`
+
+	// Total is the total number of steps for image generation.
+	// Only present for image generation models during streaming.
+	Total int64 `json:"total,omitempty"`
 }
 
 // ModelDetails provides details about a model.

app/cmd/app/app_darwin.m

@@ -14,6 +14,7 @@ extern NSString *SystemWidePath;
 @interface AppDelegate () <NSWindowDelegate, WKNavigationDelegate, WKUIDelegate>
 @property(strong, nonatomic) NSStatusItem *statusItem;
 @property(assign, nonatomic) BOOL updateAvailable;
+@property(assign, nonatomic) BOOL systemShutdownInProgress;
 @end
 
 @implementation AppDelegate
@@ -40,6 +41,13 @@ bool firstTimeRun,startHidden; // Set in run before initialization
 }
 
 - (void)applicationDidFinishLaunching:(NSNotification *)aNotification {
+    // Register for system shutdown/restart notification so we can allow termination
+    [[[NSWorkspace sharedWorkspace] notificationCenter]
+        addObserver:self
+           selector:@selector(systemWillPowerOff:)
+               name:NSWorkspaceWillPowerOffNotification
+             object:nil];
+
     // if we're in development mode, set the app icon
     NSString *bundlePath = [[NSBundle mainBundle] bundlePath];
     if (![bundlePath hasSuffix:@".app"]) {
@@ -278,7 +286,18 @@ bool firstTimeRun,startHidden; // Set in run before initialization
     [NSApp activateIgnoringOtherApps:YES];
 }
 
+- (void)systemWillPowerOff:(NSNotification *)notification {
+    // Set flag so applicationShouldTerminate: knows to allow termination.
+    // The system will call applicationShouldTerminate: after posting this notification.
+    self.systemShutdownInProgress = YES;
+}
+
 - (NSApplicationTerminateReply)applicationShouldTerminate:(NSApplication *)sender {
+    // Allow termination if the system is shutting down or restarting
+    if (self.systemShutdownInProgress) {
+        return NSTerminateNow;
+    }
+    // Otherwise just hide the app (for Cmd+Q, close button, etc.)
     [NSApp hide:nil];
     [NSApp setActivationPolicy:NSApplicationActivationPolicyAccessory];
     return NSTerminateCancel;

cmd/cmd.go

@@ -46,8 +46,9 @@ import (
 	"github.com/ollama/ollama/types/syncmap"
 	"github.com/ollama/ollama/version"
 	xcmd "github.com/ollama/ollama/x/cmd"
+	"github.com/ollama/ollama/x/create"
+	xcreateclient "github.com/ollama/ollama/x/create/client"
 	"github.com/ollama/ollama/x/imagegen"
-	imagegenclient "github.com/ollama/ollama/x/imagegen/client"
 )
 
 const ConnectInstructions = "To sign in, navigate to:\n    %s\n\n"
@@ -93,14 +94,87 @@ func CreateHandler(cmd *cobra.Command, args []string) error {
 	p := progress.NewProgress(os.Stderr)
 	defer p.Stop()
 
+	// Validate model name early to fail fast
+	modelName := args[0]
+	name := model.ParseName(modelName)
+	if !name.IsValid() {
+		return fmt.Errorf("invalid model name: %s", modelName)
+	}
+
+	// Check for --experimental flag for safetensors model creation
+	experimental, _ := cmd.Flags().GetBool("experimental")
+	if experimental {
+		// Get Modelfile content - either from -f flag or default to "FROM ."
+		var reader io.Reader
+		filename, err := getModelfileName(cmd)
+		if os.IsNotExist(err) || filename == "" {
+			// No Modelfile specified or found - use default
+			reader = strings.NewReader("FROM .\n")
+		} else if err != nil {
+			return err
+		} else {
+			f, err := os.Open(filename)
+			if err != nil {
+				return err
+			}
+			defer f.Close()
+			reader = f
+		}
+
+		// Parse the Modelfile
+		modelfile, err := parser.ParseFile(reader)
+		if err != nil {
+			return fmt.Errorf("failed to parse Modelfile: %w", err)
+		}
+
+		// Extract FROM path and configuration
+		var modelDir string
+		mfConfig := &xcreateclient.ModelfileConfig{}
+
+		for _, cmd := range modelfile.Commands {
+			switch cmd.Name {
+			case "model":
+				modelDir = cmd.Args
+			case "template":
+				mfConfig.Template = cmd.Args
+			case "system":
+				mfConfig.System = cmd.Args
+			case "license":
+				mfConfig.License = cmd.Args
+			}
+		}
+
+		if modelDir == "" {
+			modelDir = "."
+		}
+
+		// Resolve relative paths based on Modelfile location
+		if !filepath.IsAbs(modelDir) && filename != "" {
+			modelDir = filepath.Join(filepath.Dir(filename), modelDir)
+		}
+
+		quantize, _ := cmd.Flags().GetString("quantize")
+		return xcreateclient.CreateModel(xcreateclient.CreateOptions{
+			ModelName: modelName,
+			ModelDir:  modelDir,
+			Quantize:  quantize,
+			Modelfile: mfConfig,
+		}, p)
+	}
+
 	var reader io.Reader
 
 	filename, err := getModelfileName(cmd)
 	if os.IsNotExist(err) {
 		if filename == "" {
 			// No Modelfile found - check if current directory is an image gen model
-			if imagegen.IsTensorModelDir(".") {
-				return imagegenclient.CreateModel(args[0], ".", p)
+			if create.IsTensorModelDir(".") {
+				quantize, _ := cmd.Flags().GetString("quantize")
+				return xcreateclient.CreateModel(xcreateclient.CreateOptions{
+					ModelName: modelName,
+					ModelDir:  ".",
+					Quantize:  quantize,
+				}, p)
 			}
 			reader = strings.NewReader("FROM .\n")
 		} else {
@@ -133,7 +207,7 @@ func CreateHandler(cmd *cobra.Command, args []string) error {
 	}
 	spinner.Stop()
 
-	req.Model = args[0]
+	req.Model = modelName
 	quantize, _ := cmd.Flags().GetString("quantize")
 	if quantize != "" {
 		req.Quantize = quantize
@@ -464,14 +538,6 @@ func RunHandler(cmd *cobra.Command, args []string) error {
 
 	name := args[0]
 
-	// Check if this is a known image generation model (skip Show/Pull)
-	if imagegen.HasTensorLayers(name) {
-		if opts.Prompt == "" && !interactive {
-			return errors.New("image generation models require a prompt. Usage: ollama run " + name + " \"your prompt here\"")
-		}
-		return imagegen.RunCLI(cmd, name, opts.Prompt, interactive, opts.KeepAlive)
-	}
-
 	info, err := func() (*api.ShowResponse, error) {
 		showReq := &api.ShowRequest{Name: name}
 		info, err := client.Show(cmd.Context(), showReq)
@@ -533,9 +599,18 @@ func RunHandler(cmd *cobra.Command, args []string) error {
 		return generateEmbedding(cmd, name, opts.Prompt, opts.KeepAlive, truncate, dimensions)
 	}
 
+	// Check if this is an image generation model
+	if slices.Contains(info.Capabilities, model.CapabilityImage) {
+		if opts.Prompt == "" && !interactive {
+			return errors.New("image generation models require a prompt. Usage: ollama run " + name + " \"your prompt here\"")
+		}
+		return imagegen.RunCLI(cmd, name, opts.Prompt, interactive, opts.KeepAlive)
+	}
+
 	// Check for experimental flag
 	isExperimental, _ := cmd.Flags().GetBool("experimental")
 	yoloMode, _ := cmd.Flags().GetBool("experimental-yolo")
+	enableWebsearch, _ := cmd.Flags().GetBool("experimental-websearch")
 
 	if interactive {
 		if err := loadOrUnloadModel(cmd, &opts); err != nil {
@@ -565,7 +640,7 @@ func RunHandler(cmd *cobra.Command, args []string) error {
 
 		// Use experimental agent loop with tools
 		if isExperimental {
-			return xcmd.GenerateInteractive(cmd, opts.Model, opts.WordWrap, opts.Options, opts.Think, opts.HideThinking, opts.KeepAlive, yoloMode)
+			return xcmd.GenerateInteractive(cmd, opts.Model, opts.WordWrap, opts.Options, opts.Think, opts.HideThinking, opts.KeepAlive, yoloMode, enableWebsearch)
 		}
 
 		return generateInteractive(cmd, opts)
@@ -671,7 +746,11 @@ func PushHandler(cmd *cobra.Command, args []string) error {
 
 			bar, ok := bars[resp.Digest]
 			if !ok {
-				bar = progress.NewBar(fmt.Sprintf("pushing %s...", resp.Digest[7:19]), resp.Total, resp.Completed)
+				msg := resp.Status
+				if msg == "" {
+					msg = fmt.Sprintf("pushing %s...", resp.Digest[7:19])
+				}
+				bar = progress.NewBar(msg, resp.Total, resp.Completed)
 				bars[resp.Digest] = bar
 				p.Add(resp.Digest, bar)
 			}
@@ -837,11 +916,6 @@ func DeleteHandler(cmd *cobra.Command, args []string) error {
 }
 
 func ShowHandler(cmd *cobra.Command, args []string) error {
-	// Check if this is an image generation model
-	if imagegen.HasTensorLayers(args[0]) {
-		return imagegen.Show(args[0], os.Stdout)
-	}
-
 	client, err := api.ClientFromEnvironment()
 	if err != nil {
 		return err
@@ -1741,15 +1815,22 @@ func NewCLI() *cobra.Command {
 	rootCmd.Flags().BoolP("version", "v", false, "Show version information")
 
 	createCmd := &cobra.Command{
-		Use:     "create MODEL",
-		Short:   "Create a model",
-		Args:    cobra.ExactArgs(1),
-		PreRunE: checkServerHeartbeat,
-		RunE:    CreateHandler,
+		Use:   "create MODEL",
+		Short: "Create a model",
+		Args:  cobra.ExactArgs(1),
+		PreRunE: func(cmd *cobra.Command, args []string) error {
+			// Skip server check for experimental mode (writes directly to disk)
+			if experimental, _ := cmd.Flags().GetBool("experimental"); experimental {
+				return nil
+			}
+			return checkServerHeartbeat(cmd, args)
+		},
+		RunE: CreateHandler,
 	}
 
 	createCmd.Flags().StringP("file", "f", "", "Name of the Modelfile (default \"Modelfile\")")
 	createCmd.Flags().StringP("quantize", "q", "", "Quantize model to this level (e.g. q4_K_M)")
+	createCmd.Flags().Bool("experimental", false, "Enable experimental safetensors model creation")
 
 	showCmd := &cobra.Command{
 		Use:     "show MODEL",
@@ -1786,6 +1867,7 @@ func NewCLI() *cobra.Command {
 	runCmd.Flags().Int("dimensions", 0, "Truncate output embeddings to specified dimension (embedding models only)")
 	runCmd.Flags().Bool("experimental", false, "Enable experimental agent loop with tools")
 	runCmd.Flags().Bool("experimental-yolo", false, "Skip all tool approval prompts (use with caution)")
+	runCmd.Flags().Bool("experimental-websearch", false, "Enable web search tool in experimental mode")
 
 	// Image generation flags (width, height, steps, seed, etc.)
 	imagegen.RegisterFlags(runCmd)
@@ -1903,6 +1985,7 @@ func NewCLI() *cobra.Command {
 	} {
 		switch cmd {
 		case runCmd:
+			imagegen.AppendFlagsDocs(cmd)
 			appendEnvDocs(cmd, []envconfig.EnvVar{envVars["OLLAMA_HOST"], envVars["OLLAMA_NOHISTORY"]})
 		case serveCmd:
 			appendEnvDocs(cmd, []envconfig.EnvVar{

cmd/cmd_test.go

@@ -1547,6 +1547,79 @@ func TestRunOptions_Copy_ThinkValueVariants(t *testing.T) {
 	}
 }
 
+func TestShowInfoImageGen(t *testing.T) {
+	var b bytes.Buffer
+	err := showInfo(&api.ShowResponse{
+		Details: api.ModelDetails{
+			Family:            "ZImagePipeline",
+			ParameterSize:     "10.3B",
+			QuantizationLevel: "FP8",
+		},
+		Capabilities: []model.Capability{model.CapabilityImage},
+		Requires:     "0.14.0",
+	}, false, &b)
+	if err != nil {
+		t.Fatal(err)
+	}
+
+	expect := "  Model\n" +
+		"    architecture    ZImagePipeline    \n" +
+		"    parameters      10.3B             \n" +
+		"    quantization    FP8               \n" +
+		"    requires        0.14.0            \n" +
+		"\n" +
+		"  Capabilities\n" +
+		"    image    \n" +
+		"\n"
+	if diff := cmp.Diff(expect, b.String()); diff != "" {
+		t.Errorf("unexpected output (-want +got):\n%s", diff)
+	}
+}
+
+func TestPushProgressMessage(t *testing.T) {
+	tests := []struct {
+		name    string
+		status  string
+		digest  string
+		wantMsg string
+	}{
+		{
+			name:    "uses status when provided",
+			status:  "uploading model",
+			digest:  "sha256:abc123456789def",
+			wantMsg: "uploading model",
+		},
+		{
+			name:    "falls back to digest when status empty",
+			status:  "",
+			digest:  "sha256:abc123456789def",
+			wantMsg: "pushing abc123456789...",
+		},
+		{
+			name:    "handles short digest gracefully",
+			status:  "",
+			digest:  "sha256:abc",
+			wantMsg: "pushing sha256:abc...",
+		},
+	}
+
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			msg := tt.status
+			if msg == "" {
+				if len(tt.digest) >= 19 {
+					msg = fmt.Sprintf("pushing %s...", tt.digest[7:19])
+				} else {
+					msg = fmt.Sprintf("pushing %s...", tt.digest)
+				}
+			}
+			if msg != tt.wantMsg {
+				t.Errorf("got %q, want %q", msg, tt.wantMsg)
+			}
+		})
+	}
+}
+
 func TestRunOptions_Copy_Independence(t *testing.T) {
 	// Test that modifications to original don't affect copy
 	originalThink := &api.ThinkValue{Value: "original"}

cmd/interactive.go

@@ -116,7 +116,7 @@ func generateInteractive(cmd *cobra.Command, opts runOptions) error {
 		Prompt:         ">>> ",
 		AltPrompt:      "... ",
 		Placeholder:    "Send a message (/? for help)",
-		AltPlaceholder: `Use """ to end multi-line input`,
+		AltPlaceholder: "Press Enter to send",
 	})
 	if err != nil {
 		return err

docs/api.md

@@ -16,6 +16,7 @@
 - [Generate Embeddings](#generate-embeddings)
 - [List Running Models](#list-running-models)
 - [Version](#version)
+- [Experimental: Image Generation](#image-generation-experimental)
 
 ## Conventions
 
@@ -58,6 +59,15 @@ Advanced parameters (optional):
 - `keep_alive`: controls how long the model will stay loaded into memory following the request (default: `5m`)
 - `context` (deprecated): the context parameter returned from a previous request to `/generate`, this can be used to keep a short conversational memory
 
+Experimental image generation parameters (for image generation models only):
+
+> [!WARNING]
+> These parameters are experimental and may change in future versions.
+
+- `width`: width of the generated image in pixels
+- `height`: height of the generated image in pixels
+- `steps`: number of diffusion steps
+
 #### Structured outputs
 
 Structured outputs are supported by providing a JSON schema in the `format` parameter. The model will generate a response that matches the schema. See the [structured outputs](#request-structured-outputs) example below.
@@ -1867,3 +1877,55 @@ curl http://localhost:11434/api/version
   "version": "0.5.1"
 }
 ```
+
+## Experimental Features
+
+### Image Generation (Experimental)
+
+> [!WARNING]
+> Image generation is experimental and may change in future versions.
+
+Image generation is now supported through the standard `/api/generate` endpoint when using image generation models. The API automatically detects when an image generation model is being used.
+
+See the [Generate a completion](#generate-a-completion) section for the full API documentation. The experimental image generation parameters (`width`, `height`, `steps`) are documented there.
+
+#### Example
+
+##### Request
+
+```shell
+curl http://localhost:11434/api/generate -d '{
+  "model": "x/z-image-turbo",
+  "prompt": "a sunset over mountains",
+  "width": 1024,
+  "height": 768
+}'
+```
+
+##### Response (streaming)
+
+Progress updates during generation:
+
+```json
+{
+  "model": "x/z-image-turbo",
+  "created_at": "2024-01-15T10:30:00.000000Z",
+  "completed": 5,
+  "total": 20,
+  "done": false
+}
+```
+
+##### Final Response
+
+```json
+{
+  "model": "x/z-image-turbo",
+  "created_at": "2024-01-15T10:30:15.000000Z",
+  "image": "iVBORw0KGgoAAAANSUhEUg...",
+  "done": true,
+  "done_reason": "stop",
+  "total_duration": 15000000000,
+  "load_duration": 2000000000
+}
+```

docs/api/anthropic-compatibility.mdx

@@ -21,6 +21,7 @@ ollama pull glm-4.7:cloud
 To use Ollama with tools that expect the Anthropic API (like Claude Code), set these environment variables:
 
 ```shell
+export ANTHROPIC_AUTH_TOKEN=ollama  # required but ignored
 export ANTHROPIC_BASE_URL=http://localhost:11434
 export ANTHROPIC_API_KEY=ollama  # required but ignored
 ```
@@ -247,12 +248,13 @@ curl -X POST http://localhost:11434/v1/messages \
 [Claude Code](https://code.claude.com/docs/en/overview) can be configured to use Ollama as its backend:
 
 ```shell
-ANTHROPIC_BASE_URL=http://localhost:11434 ANTHROPIC_API_KEY=ollama claude --model qwen3-coder
+ANTHROPIC_AUTH_TOKEN=ollama ANTHROPIC_BASE_URL=http://localhost:11434 ANTHROPIC_API_KEY=ollama claude --model qwen3-coder
 ```
 
 Or set the environment variables in your shell profile:
 
 ```shell
+export ANTHROPIC_AUTH_TOKEN=ollama
 export ANTHROPIC_BASE_URL=http://localhost:11434
 export ANTHROPIC_API_KEY=ollama
 ```

docs/api/openai-compatibility.mdx

@@ -275,6 +275,73 @@ curl -X POST http://localhost:11434/v1/chat/completions \
 - [x] `dimensions`
 - [ ] `user`
 
+### `/v1/images/generations` (experimental)
+
+> Note: This endpoint is experimental and may change or be removed in future versions.
+
+Generate images using image generation models.
+
+<CodeGroup dropdown>
+
+```python images.py
+from openai import OpenAI
+
+client = OpenAI(
+    base_url='http://localhost:11434/v1/',
+    api_key='ollama',  # required but ignored
+)
+
+response = client.images.generate(
+    model='x/z-image-turbo',
+    prompt='A cute robot learning to paint',
+    size='1024x1024',
+    response_format='b64_json',
+)
+print(response.data[0].b64_json[:50] + '...')
+```
+
+```javascript images.js
+import OpenAI from "openai";
+
+const openai = new OpenAI({
+  baseURL: "http://localhost:11434/v1/",
+  apiKey: "ollama", // required but ignored
+});
+
+const response = await openai.images.generate({
+  model: "x/z-image-turbo",
+  prompt: "A cute robot learning to paint",
+  size: "1024x1024",
+  response_format: "b64_json",
+});
+
+console.log(response.data[0].b64_json.slice(0, 50) + "...");
+```
+
+```shell images.sh
+curl -X POST http://localhost:11434/v1/images/generations \
+-H "Content-Type: application/json" \
+-d '{
+  "model": "x/z-image-turbo",
+  "prompt": "A cute robot learning to paint",
+  "size": "1024x1024",
+  "response_format": "b64_json"
+}'
+```
+
+</CodeGroup>
+
+#### Supported request fields
+
+- [x] `model`
+- [x] `prompt`
+- [x] `size` (e.g. "1024x1024")
+- [x] `response_format` (only `b64_json` supported)
+- [ ] `n`
+- [ ] `quality`
+- [ ] `style`
+- [ ] `user`
+
 ### `/v1/responses`
 
 > Note: Added in Ollama v0.13.3

docs/capabilities/web-search.mdx

@@ -110,7 +110,7 @@ More Ollama [Python example](https://github.com/ollama/ollama-python/blob/main/e
 import { Ollama } from "ollama";
 
 const client = new Ollama();
-const results = await client.webSearch({ query: "what is ollama?" });
+const results = await client.webSearch("what is ollama?");
 console.log(JSON.stringify(results, null, 2));
 ```
 
@@ -213,7 +213,7 @@ models](https://ollama.com/models)\n\nAvailable for macOS, Windows, and Linux',
 import { Ollama } from "ollama";
 
 const client = new Ollama();
-const fetchResult = await client.webFetch({ url: "https://ollama.com" });
+const fetchResult = await client.webFetch("https://ollama.com");
 console.log(JSON.stringify(fetchResult, null, 2));
 ```
 

docs/docs.json

@@ -111,7 +111,9 @@
               "/integrations/zed",
               "/integrations/roo-code",
               "/integrations/n8n",
-              "/integrations/xcode"
+              "/integrations/xcode",
+              "/integrations/onyx",
+              "/integrations/marimo"
             ]
           },
           {

docs/faq.mdx

@@ -22,7 +22,7 @@ Please refer to the [GPU docs](./gpu).
 
 ## How can I specify the context window size?
 
-By default, Ollama uses a context window size of 2048 tokens.
+By default, Ollama uses a context window size of 4096 tokens.
 
 This can be overridden with the `OLLAMA_CONTEXT_LENGTH` environment variable. For example, to set the default context window to 8K, use:
 

docs/integrations/claude-code.mdx

@@ -2,6 +2,12 @@
 title: Claude Code
 ---
 
+Claude Code is Anthropic's agentic coding tool that can read, modify, and execute code in your working directory. 
+
+Open models can be used with Claude Code through Ollama's Anthropic-compatible API, enabling you to use models such as `qwen3-coder`, `gpt-oss:20b`, or other models.
+
+![Claude Code with Ollama](https://files.ollama.com/claude-code.png)
+
 ## Install
 
 Install [Claude Code](https://code.claude.com/docs/en/overview):
@@ -25,22 +31,24 @@ Claude Code connects to Ollama using the Anthropic-compatible API.
 1. Set the environment variables:
 
 ```shell
+export ANTHROPIC_AUTH_TOKEN=ollama
 export ANTHROPIC_BASE_URL=http://localhost:11434
-export ANTHROPIC_API_KEY=ollama
 ```
 
 2. Run Claude Code with an Ollama model:
 
 ```shell
-claude --model qwen3-coder
+claude --model gpt-oss:20b
 ```
 
 Or run with environment variables inline:
 
 ```shell
-ANTHROPIC_BASE_URL=http://localhost:11434 ANTHROPIC_API_KEY=ollama claude --model qwen3-coder
+ANTHROPIC_AUTH_TOKEN=ollama ANTHROPIC_BASE_URL=http://localhost:11434 claude --model gpt-oss:20b
 ```
 
+**Note:** Claude Code requires a large context window. We recommend at least 32K tokens. See the [context length documentation](/context-length) for how to adjust context length in Ollama.
+
 ## Connecting to ollama.com
 
 1. Create an [API key](https://ollama.com/settings/keys) on ollama.com
@@ -67,3 +75,4 @@ claude --model glm-4.7:cloud
 ### Local models
 - `qwen3-coder` - Excellent for coding tasks
 - `gpt-oss:20b` - Strong general-purpose model
+- `gpt-oss:120b` - Larger general-purpose model for more complex tasks

docs/integrations/marimo.mdx

@@ -0,0 +1,73 @@
+---
+title: marimo
+---
+
+## Install
+
+Install [marimo](https://marimo.io). You can use `pip` or `uv` for this. You 
+can also use `uv` to create a sandboxed environment for marimo by running:
+
+```
+uvx marimo edit --sandbox notebook.py
+```
+
+## Usage with Ollama
+
+1. In marimo, go to the user settings and go to the AI tab. From here
+you can find and configure Ollama as an AI provider. For local use you
+would typically point the base url to `http://localhost:11434/v1`.
+
+<div style={{ display: 'flex', justifyContent: 'center' }}>
+  <img 
+    src="/images/marimo-settings.png" 
+    alt="Ollama settings in marimo"
+    width="50%"
+  />
+</div>
+
+2. Once the AI provider is set up, you can turn on/off specific AI models you'd like to access. 
+
+<div style={{ display: 'flex', justifyContent: 'center' }}>
+  <img 
+    src="/images/marimo-models.png" 
+    alt="Selecting an Ollama model"
+    width="50%"
+  />
+</div>
+
+3. You can also add a model to the list of available models by scrolling to the bottom and using the UI there. 
+
+<div style={{ display: 'flex', justifyContent: 'center' }}>
+  <img 
+    src="/images/marimo-add-model.png" 
+    alt="Adding a new Ollama model"
+    width="50%"
+  />
+</div>
+
+4. Once configured, you can now use Ollama for AI chats in marimo.
+
+<div style={{ display: 'flex', justifyContent: 'center' }}>
+  <img 
+    src="/images/marimo-chat.png" 
+    alt="Configure code completion"
+    width="50%"
+  />
+</div>
+
+4. Alternatively, you can now use Ollama for **inline code completion** in marimo. This can be configured in the "AI Features" tab. 
+
+<div style={{ display: 'flex', justifyContent: 'center' }}>
+  <img 
+    src="/images/marimo-code-completion.png" 
+    alt="Configure code completion"
+    width="50%"
+  />
+</div>
+
+
+## Connecting to ollama.com
+
+1. Sign in to ollama cloud via `ollama signin` 
+2. In the ollama model settings add a model that ollama hosts, like `gpt-oss:120b`.
+3. You can now refer to this model in marimo!

docs/integrations/onyx.mdx

@@ -0,0 +1,63 @@
+---
+title: Onyx
+---
+
+## Overview
+[Onyx](http://onyx.app/) is a self-hostable Chat UI that integrates with all Ollama models. Features include:
+- Creating custom Agents
+- Web search
+- Deep Research
+- RAG over uploaded documents and connected apps
+- Connectors to applications like Google Drive, Email, Slack, etc.
+- MCP and OpenAPI Actions support
+- Image generation
+- User/Groups management, RBAC, SSO, etc.
+
+Onyx can be deployed for single users or large organizations.
+
+## Install Onyx
+
+Deploy Onyx with the [quickstart guide](https://docs.onyx.app/deployment/getting_started/quickstart).
+
+<Info>
+Resourcing/scaling docs [here](https://docs.onyx.app/deployment/getting_started/resourcing).
+</Info>
+
+## Usage with Ollama 
+
+1. Login to your Onyx deployment (create an account first).
+<div style={{ display: 'flex', justifyContent: 'center' }}>
+  <img 
+    src="/images/onyx-login.png" 
+    alt="Onyx Login Page"
+    width="75%"
+  />
+</div>
+2. In the set-up process select `Ollama` as the LLM provider.
+<div style={{ display: 'flex', justifyContent: 'center' }}>
+  <img 
+    src="/images/onyx-ollama-llm.png" 
+    alt="Onyx Set Up Form"
+    width="75%"
+  />
+</div>
+3. Provide your **Ollama API URL** and select your models.
+<Note>If you're running Onyx in Docker, to access your computer's local network use `http://host.docker.internal` instead of `http://127.0.0.1`.</Note>
+<div style={{ display: 'flex', justifyContent: 'center' }}>
+  <img 
+    src="/images/onyx-ollama-form.png" 
+    alt="Selecting Ollama Models"
+    width="75%"
+  />
+</div>
+
+You can also easily connect up Onyx Cloud with the `Ollama Cloud` tab of the setup.
+
+## Send your first query
+<div style={{ display: 'flex', justifyContent: 'center' }}>
+  <img 
+    src="/images/onyx-query.png" 
+    alt="Onyx Query Example"
+    width="75%"
+  />
+</div>

docs/linux.mdx

@@ -1,5 +1,5 @@
 ---
-title: "Linux"
+title: Linux
 ---
 
 ## Install
@@ -13,14 +13,15 @@ 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 should remove the old libraries
+  with `sudo rm -rf /usr/lib/ollama` first.
 </Note>
 
 Download and extract the package:
 
 ```shell
-curl -fsSL https://ollama.com/download/ollama-linux-amd64.tgz \
-    | sudo tar zx -C /usr
+curl -fsSL https://ollama.com/download/ollama-linux-amd64.tar.zst \
+    | sudo tar x -C /usr
 ```
 
 Start Ollama:
@@ -40,8 +41,8 @@ ollama -v
 If you have an AMD GPU, also download and extract the additional ROCm package:
 
 ```shell
-curl -fsSL https://ollama.com/download/ollama-linux-amd64-rocm.tgz \
-    | sudo tar zx -C /usr
+curl -fsSL https://ollama.com/download/ollama-linux-amd64-rocm.tar.zst \
+    | sudo tar x -C /usr
 ```
 
 ### ARM64 install
@@ -49,8 +50,8 @@ curl -fsSL https://ollama.com/download/ollama-linux-amd64-rocm.tgz \
 Download and extract the ARM64-specific package:
 
 ```shell
-curl -fsSL https://ollama.com/download/ollama-linux-arm64.tgz \
-    | sudo tar zx -C /usr
+curl -fsSL https://ollama.com/download/ollama-linux-arm64.tar.zst \
+    | sudo tar x -C /usr
 ```
 
 ### Adding Ollama as a startup service (recommended)
@@ -112,7 +113,11 @@ sudo systemctl status ollama
 ```
 
 <Note>
-  While AMD has contributed the `amdgpu` driver upstream to the official linux kernel source, the version is older and may not support all ROCm features. We recommend you install the latest driver from https://www.amd.com/en/support/linux-drivers for best support of your Radeon GPU.
+  While AMD has contributed the `amdgpu` driver upstream to the official linux
+  kernel source, the version is older and may not support all ROCm features. We
+  recommend you install the latest driver from
+  https://www.amd.com/en/support/linux-drivers for best support of your Radeon
+  GPU.
 </Note>
 
 ## Customizing
@@ -141,8 +146,8 @@ curl -fsSL https://ollama.com/install.sh | sh
 Or by re-downloading Ollama:
 
 ```shell
-curl -fsSL https://ollama.com/download/ollama-linux-amd64.tgz \
-    | sudo tar zx -C /usr
+curl -fsSL https://ollama.com/download/ollama-linux-amd64.tar.zst \
+    | sudo tar x -C /usr
 ```
 
 ## Installing specific versions
@@ -191,4 +196,4 @@ Remove the downloaded models and Ollama service user and group:
 sudo userdel ollama
 sudo groupdel ollama
 sudo rm -r /usr/share/ollama
-```
+```

docs/troubleshooting.md

@@ -1,3 +0,0 @@
-# Troubleshooting
-
-For troubleshooting, see [https://docs.ollama.com/troubleshooting](https://docs.ollama.com/troubleshooting)

integration/tools_test.go

@@ -131,7 +131,7 @@ func TestAPIToolCalling(t *testing.T) {
 					t.Errorf("unexpected tool called: got %q want %q", lastToolCall.Function.Name, "get_weather")
 				}
 
-				if _, ok := lastToolCall.Function.Arguments["location"]; !ok {
+				if _, ok := lastToolCall.Function.Arguments.Get("location"); !ok {
 					t.Errorf("expected tool arguments to include 'location', got: %s", lastToolCall.Function.Arguments.String())
 				}
 			case <-ctx.Done():

llm/server.go

@@ -1464,6 +1464,12 @@ type CompletionRequest struct {
 
 	// TopLogprobs specifies the number of most likely alternative tokens to return (0-20)
 	TopLogprobs int
+
+	// Image generation fields
+	Width  int32 `json:"width,omitempty"`
+	Height int32 `json:"height,omitempty"`
+	Steps  int32 `json:"steps,omitempty"`
+	Seed   int64 `json:"seed,omitempty"`
 }
 
 // DoneReason represents the reason why a completion response is done
@@ -1512,6 +1518,15 @@ type CompletionResponse struct {
 
 	// Logprobs contains log probability information if requested
 	Logprobs []Logprob `json:"logprobs,omitempty"`
+
+	// Image contains base64-encoded image data for image generation
+	Image string `json:"image,omitempty"`
+
+	// Step is the current step in image generation
+	Step int `json:"step,omitempty"`
+
+	// TotalSteps is the total number of steps for image generation
+	TotalSteps int `json:"total_steps,omitempty"`
 }
 
 func (s *llmServer) Completion(ctx context.Context, req CompletionRequest, fn func(CompletionResponse)) error {

middleware/anthropic.go

@@ -118,6 +118,9 @@ func AnthropicMessagesMiddleware() gin.HandlerFunc {
 			return
 		}
 
+		// Set think to nil when being used with Anthropic API to connect to tools like claude code
+		c.Set("relax_thinking", true)
+
 		var b bytes.Buffer
 		if err := json.NewEncoder(&b).Encode(chatReq); err != nil {
 			c.AbortWithStatusJSON(http.StatusInternalServerError, anthropic.NewError(http.StatusInternalServerError, err.Error()))

middleware/anthropic_test.go

@@ -582,3 +582,26 @@ func TestAnthropicWriter_ErrorFromRoutes(t *testing.T) {
 		})
 	}
 }
+
+func TestAnthropicMessagesMiddleware_SetsRelaxThinkingFlag(t *testing.T) {
+	gin.SetMode(gin.TestMode)
+
+	var flagSet bool
+	router := gin.New()
+	router.Use(AnthropicMessagesMiddleware())
+	router.POST("/v1/messages", func(c *gin.Context) {
+		_, flagSet = c.Get("relax_thinking")
+		c.Status(http.StatusOK)
+	})
+
+	body := `{"model": "test-model", "max_tokens": 100, "messages": [{"role": "user", "content": "Hi"}]}`
+	req, _ := http.NewRequest(http.MethodPost, "/v1/messages", strings.NewReader(body))
+	req.Header.Set("Content-Type", "application/json")
+
+	resp := httptest.NewRecorder()
+	router.ServeHTTP(resp, req)
+
+	if !flagSet {
+		t.Error("expected relax_thinking flag to be set in context")
+	}
+}

middleware/openai.go

@@ -8,6 +8,7 @@ import (
 	"math/rand"
 	"net/http"
 	"strings"
+	"time"
 
 	"github.com/gin-gonic/gin"
 
@@ -441,6 +442,7 @@ type ResponsesWriter struct {
 	stream     bool
 	responseID string
 	itemID     string
+	request    openai.ResponsesRequest
 }
 
 func (w *ResponsesWriter) writeEvent(eventType string, data any) error {
@@ -478,7 +480,9 @@ func (w *ResponsesWriter) writeResponse(data []byte) (int, error) {
 
 	// Non-streaming response
 	w.ResponseWriter.Header().Set("Content-Type", "application/json")
-	response := openai.ToResponse(w.model, w.responseID, w.itemID, chatResponse)
+	response := openai.ToResponse(w.model, w.responseID, w.itemID, chatResponse, w.request)
+	completedAt := time.Now().Unix()
+	response.CompletedAt = &completedAt
 	return len(data), json.NewEncoder(w.ResponseWriter).Encode(response)
 }
 
@@ -523,11 +527,12 @@ func ResponsesMiddleware() gin.HandlerFunc {
 
 		w := &ResponsesWriter{
 			BaseWriter: BaseWriter{ResponseWriter: c.Writer},
-			converter:  openai.NewResponsesStreamConverter(responseID, itemID, req.Model),
+			converter:  openai.NewResponsesStreamConverter(responseID, itemID, req.Model, req),
 			model:      req.Model,
 			stream:     streamRequested,
 			responseID: responseID,
 			itemID:     itemID,
+			request:    req,
 		}
 
 		// Set headers based on streaming mode
@@ -541,3 +546,66 @@ func ResponsesMiddleware() gin.HandlerFunc {
 		c.Next()
 	}
 }
+
+type ImageWriter struct {
+	BaseWriter
+}
+
+func (w *ImageWriter) writeResponse(data []byte) (int, error) {
+	var generateResponse api.GenerateResponse
+	if err := json.Unmarshal(data, &generateResponse); err != nil {
+		return 0, err
+	}
+
+	// Only write response when done with image
+	if generateResponse.Done && generateResponse.Image != "" {
+		w.ResponseWriter.Header().Set("Content-Type", "application/json")
+		return len(data), json.NewEncoder(w.ResponseWriter).Encode(openai.ToImageGenerationResponse(generateResponse))
+	}
+
+	return len(data), nil
+}
+
+func (w *ImageWriter) Write(data []byte) (int, error) {
+	code := w.ResponseWriter.Status()
+	if code != http.StatusOK {
+		return w.writeError(data)
+	}
+
+	return w.writeResponse(data)
+}
+
+func ImageGenerationsMiddleware() gin.HandlerFunc {
+	return func(c *gin.Context) {
+		var req openai.ImageGenerationRequest
+		if err := c.ShouldBindJSON(&req); err != nil {
+			c.AbortWithStatusJSON(http.StatusBadRequest, openai.NewError(http.StatusBadRequest, err.Error()))
+			return
+		}
+
+		if req.Prompt == "" {
+			c.AbortWithStatusJSON(http.StatusBadRequest, openai.NewError(http.StatusBadRequest, "prompt is required"))
+			return
+		}
+
+		if req.Model == "" {
+			c.AbortWithStatusJSON(http.StatusBadRequest, openai.NewError(http.StatusBadRequest, "model is required"))
+			return
+		}
+
+		var b bytes.Buffer
+		if err := json.NewEncoder(&b).Encode(openai.FromImageGenerationRequest(req)); err != nil {
+			c.AbortWithStatusJSON(http.StatusInternalServerError, openai.NewError(http.StatusInternalServerError, err.Error()))
+			return
+		}
+
+		c.Request.Body = io.NopCloser(&b)
+
+		w := &ImageWriter{
+			BaseWriter: BaseWriter{ResponseWriter: c.Writer},
+		}
+
+		c.Writer = w
+		c.Next()
+	}
+}

middleware/openai_test.go

@@ -961,3 +961,154 @@ func TestRetrieveMiddleware(t *testing.T) {
 		}
 	}
 }
+
+func TestImageGenerationsMiddleware(t *testing.T) {
+	type testCase struct {
+		name string
+		body string
+		req  api.GenerateRequest
+		err  openai.ErrorResponse
+	}
+
+	var capturedRequest *api.GenerateRequest
+
+	testCases := []testCase{
+		{
+			name: "image generation basic",
+			body: `{
+				"model": "test-model",
+				"prompt": "a beautiful sunset"
+			}`,
+			req: api.GenerateRequest{
+				Model:  "test-model",
+				Prompt: "a beautiful sunset",
+			},
+		},
+		{
+			name: "image generation with size",
+			body: `{
+				"model": "test-model",
+				"prompt": "a beautiful sunset",
+				"size": "512x768"
+			}`,
+			req: api.GenerateRequest{
+				Model:  "test-model",
+				Prompt: "a beautiful sunset",
+				Width:  512,
+				Height: 768,
+			},
+		},
+		{
+			name: "image generation missing prompt",
+			body: `{
+				"model": "test-model"
+			}`,
+			err: openai.ErrorResponse{
+				Error: openai.Error{
+					Message: "prompt is required",
+					Type:    "invalid_request_error",
+				},
+			},
+		},
+		{
+			name: "image generation missing model",
+			body: `{
+				"prompt": "a beautiful sunset"
+			}`,
+			err: openai.ErrorResponse{
+				Error: openai.Error{
+					Message: "model is required",
+					Type:    "invalid_request_error",
+				},
+			},
+		},
+	}
+
+	endpoint := func(c *gin.Context) {
+		c.Status(http.StatusOK)
+	}
+
+	gin.SetMode(gin.TestMode)
+	router := gin.New()
+	router.Use(ImageGenerationsMiddleware(), captureRequestMiddleware(&capturedRequest))
+	router.Handle(http.MethodPost, "/api/generate", endpoint)
+
+	for _, tc := range testCases {
+		t.Run(tc.name, func(t *testing.T) {
+			req, _ := http.NewRequest(http.MethodPost, "/api/generate", strings.NewReader(tc.body))
+			req.Header.Set("Content-Type", "application/json")
+
+			defer func() { capturedRequest = nil }()
+
+			resp := httptest.NewRecorder()
+			router.ServeHTTP(resp, req)
+
+			if tc.err.Error.Message != "" {
+				var errResp openai.ErrorResponse
+				if err := json.Unmarshal(resp.Body.Bytes(), &errResp); err != nil {
+					t.Fatal(err)
+				}
+				if diff := cmp.Diff(tc.err, errResp); diff != "" {
+					t.Fatalf("errors did not match:\n%s", diff)
+				}
+				return
+			}
+
+			if resp.Code != http.StatusOK {
+				t.Fatalf("expected status 200, got %d: %s", resp.Code, resp.Body.String())
+			}
+
+			if diff := cmp.Diff(&tc.req, capturedRequest); diff != "" {
+				t.Fatalf("requests did not match:\n%s", diff)
+			}
+		})
+	}
+}
+
+func TestImageWriterResponse(t *testing.T) {
+	gin.SetMode(gin.TestMode)
+
+	// Test that ImageWriter transforms GenerateResponse to OpenAI format
+	endpoint := func(c *gin.Context) {
+		resp := api.GenerateResponse{
+			Model:     "test-model",
+			CreatedAt: time.Unix(1234567890, 0).UTC(),
+			Done:      true,
+			Image:     "dGVzdC1pbWFnZS1kYXRh", // base64 of "test-image-data"
+		}
+		data, _ := json.Marshal(resp)
+		c.Writer.Write(append(data, '\n'))
+	}
+
+	router := gin.New()
+	router.Use(ImageGenerationsMiddleware())
+	router.Handle(http.MethodPost, "/api/generate", endpoint)
+
+	body := `{"model": "test-model", "prompt": "test"}`
+	req, _ := http.NewRequest(http.MethodPost, "/api/generate", strings.NewReader(body))
+	req.Header.Set("Content-Type", "application/json")
+
+	resp := httptest.NewRecorder()
+	router.ServeHTTP(resp, req)
+
+	if resp.Code != http.StatusOK {
+		t.Fatalf("expected status 200, got %d: %s", resp.Code, resp.Body.String())
+	}
+
+	var imageResp openai.ImageGenerationResponse
+	if err := json.Unmarshal(resp.Body.Bytes(), &imageResp); err != nil {
+		t.Fatalf("failed to unmarshal response: %v", err)
+	}
+
+	if imageResp.Created != 1234567890 {
+		t.Errorf("expected created 1234567890, got %d", imageResp.Created)
+	}
+
+	if len(imageResp.Data) != 1 {
+		t.Fatalf("expected 1 image, got %d", len(imageResp.Data))
+	}
+
+	if imageResp.Data[0].B64JSON != "dGVzdC1pbWFnZS1kYXRh" {
+		t.Errorf("expected image data 'dGVzdC1pbWFnZS1kYXRh', got %s", imageResp.Data[0].B64JSON)
+	}
+}

model/parsers/nemotron3nano.go

@@ -1,7 +1,6 @@
 package parsers
 
 import (
-	"regexp"
 	"strings"
 	"unicode"
 
@@ -14,243 +13,114 @@ const (
 	Nemotron3NanoCollectingThinking Nemotron3NanoParserState = iota
 	Nemotron3NanoSkipWhitespaceAfterThinking
 	Nemotron3NanoCollectingContent
-	Nemotron3NanoCollectingToolCalls
 )
 
 const (
-	nemotronThinkClose    = "</think>"
-	nemotronToolCallOpen  = "<tool_call>"
-	nemotronToolCallClose = "</tool_call>"
+	nemotronThinkClose   = "</think>"
+	nemotronToolCallOpen = "<tool_call>"
 )
 
 type Nemotron3NanoParser struct {
-	state  Nemotron3NanoParserState
-	buffer strings.Builder
-	tools  []api.Tool
+	state      Nemotron3NanoParserState
+	buffer     strings.Builder
+	toolParser *Qwen3CoderParser
 }
 
 func (p *Nemotron3NanoParser) HasToolSupport() bool     { return true }
 func (p *Nemotron3NanoParser) HasThinkingSupport() bool { return true }
 
 func (p *Nemotron3NanoParser) Init(tools []api.Tool, lastMessage *api.Message, thinkValue *api.ThinkValue) []api.Tool {
-	p.tools = tools
+	p.toolParser = &Qwen3CoderParser{}
+	p.toolParser.Init(tools, nil, nil)
 
-	// thinking is enabled if user requests it
 	thinkingEnabled := thinkValue != nil && thinkValue.Bool()
-
 	prefill := lastMessage != nil && lastMessage.Role == "assistant"
 
-	if !thinkingEnabled {
+	if !thinkingEnabled || (prefill && lastMessage.Content != "") {
 		p.state = Nemotron3NanoCollectingContent
-		return tools
+	} else {
+		p.state = Nemotron3NanoCollectingThinking
 	}
 
-	if prefill && lastMessage.Content != "" {
-		p.state = Nemotron3NanoCollectingContent
-		return tools
-	}
-
-	p.state = Nemotron3NanoCollectingThinking
 	return tools
 }
 
-type nemotronEvent interface {
-	isNemotronEvent()
-}
-
-type nemotronEventThinkingContent struct {
-	content string
-}
-
-type nemotronEventContent struct {
-	content string
-}
-
-type nemotronEventToolCall struct {
-	toolCall api.ToolCall
-}
-
-func (nemotronEventThinkingContent) isNemotronEvent() {}
-func (nemotronEventContent) isNemotronEvent()         {}
-func (nemotronEventToolCall) isNemotronEvent()        {}
-
 func (p *Nemotron3NanoParser) Add(s string, done bool) (content string, thinking string, calls []api.ToolCall, err error) {
-	p.buffer.WriteString(s)
-	events := p.parseEvents()
-
-	var toolCalls []api.ToolCall
-	var contentSb strings.Builder
-	var thinkingSb strings.Builder
-	for _, event := range events {
-		switch event := event.(type) {
-		case nemotronEventToolCall:
-			toolCalls = append(toolCalls, event.toolCall)
-		case nemotronEventThinkingContent:
-			thinkingSb.WriteString(event.content)
-		case nemotronEventContent:
-			contentSb.WriteString(event.content)
-		}
+	if p.state == Nemotron3NanoCollectingContent {
+		return p.toolParser.Add(s, done)
 	}
 
-	return contentSb.String(), thinkingSb.String(), toolCalls, nil
-}
-
-func (p *Nemotron3NanoParser) parseEvents() []nemotronEvent {
-	var all []nemotronEvent
-
-	keepLooping := true
-	for keepLooping {
-		var events []nemotronEvent
-		events, keepLooping = p.eat()
-		if len(events) > 0 {
-			all = append(all, events...)
-		}
-	}
-
-	return all
-}
-
-// emitWithPartialCheck extracts unambiguous content before a potential partial tag
-func (p *Nemotron3NanoParser) emitWithPartialCheck(bufStr, tag string) (unambiguous, ambiguous string) {
-	if overlapLen := overlap(bufStr, tag); overlapLen > 0 {
-		beforePartialTag := bufStr[:len(bufStr)-overlapLen]
-		trailingLen := trailingWhitespaceLen(beforePartialTag)
-		return bufStr[:len(beforePartialTag)-trailingLen], bufStr[len(beforePartialTag)-trailingLen:]
-	}
-	wsLen := trailingWhitespaceLen(bufStr)
-	return bufStr[:len(bufStr)-wsLen], bufStr[len(bufStr)-wsLen:]
-}
-
-func (p *Nemotron3NanoParser) eat() ([]nemotronEvent, bool) {
-	bufStr := p.buffer.String()
-	if bufStr == "" {
-		return nil, false
-	}
-
-	switch p.state {
-	case Nemotron3NanoCollectingThinking:
-		if strings.Contains(bufStr, nemotronThinkClose) {
-			split := strings.SplitN(bufStr, nemotronThinkClose, 2)
-			thinking := strings.TrimRightFunc(split[0], unicode.IsSpace)
-			p.buffer.Reset()
-			remainder := strings.TrimLeftFunc(split[1], unicode.IsSpace)
-			p.buffer.WriteString(remainder)
-			// Transition to whitespace-skipping state if buffer is empty,
-			// otherwise go directly to content collection
-			if remainder == "" {
-				p.state = Nemotron3NanoSkipWhitespaceAfterThinking
-			} else {
-				p.state = Nemotron3NanoCollectingContent
-			}
-			if thinking != "" {
-				return []nemotronEvent{nemotronEventThinkingContent{content: thinking}}, true
-			}
-			return nil, true
-		}
-		unambig, ambig := p.emitWithPartialCheck(bufStr, nemotronThinkClose)
-		p.buffer.Reset()
-		p.buffer.WriteString(ambig)
-		if unambig != "" {
-			return []nemotronEvent{nemotronEventThinkingContent{content: unambig}}, false
-		}
-		return nil, false
-
-	// We only want to skip whitespace between thinking and content
-	case Nemotron3NanoSkipWhitespaceAfterThinking:
-		bufStr = strings.TrimLeftFunc(bufStr, unicode.IsSpace)
-		p.buffer.Reset()
-		p.buffer.WriteString(bufStr)
-		if bufStr == "" {
-			return nil, false
+	if p.state == Nemotron3NanoSkipWhitespaceAfterThinking {
+		s = strings.TrimLeftFunc(s, unicode.IsSpace)
+		if s == "" {
+			return "", "", nil, nil
 		}
 		p.state = Nemotron3NanoCollectingContent
-		return nil, true
+		return p.toolParser.Add(s, done)
+	}
 
-	case Nemotron3NanoCollectingContent:
-		if strings.Contains(bufStr, nemotronToolCallOpen) {
-			split := strings.SplitN(bufStr, nemotronToolCallOpen, 2)
-			content := strings.TrimRightFunc(split[0], unicode.IsSpace)
-			p.buffer.Reset()
-			p.buffer.WriteString(split[1])
-			p.state = Nemotron3NanoCollectingToolCalls
-			if content != "" {
-				return []nemotronEvent{nemotronEventContent{content: content}}, true
-			}
-			return nil, true
-		}
-		unambig, ambig := p.emitWithPartialCheck(bufStr, nemotronToolCallOpen)
+	// Nemotron3NanoCollectingThinking - buffer and look for end markers
+	p.buffer.WriteString(s)
+	bufStr := p.buffer.String()
+
+	// Look for end of thinking: </think> or <tool_call> (model may skip </think>)
+	thinkIdx := strings.Index(bufStr, nemotronThinkClose)
+	toolIdx := strings.Index(bufStr, nemotronToolCallOpen)
+
+	var endIdx int = -1
+	var remainder string
+
+	if thinkIdx != -1 && (toolIdx == -1 || thinkIdx < toolIdx) {
+		endIdx = thinkIdx
+		remainder = strings.TrimLeftFunc(bufStr[thinkIdx+len(nemotronThinkClose):], unicode.IsSpace)
+	} else if toolIdx != -1 {
+		endIdx = toolIdx
+		remainder = bufStr[toolIdx:] // Include <tool_call> tag
+	}
+
+	if endIdx != -1 {
+		thinking = strings.TrimRightFunc(bufStr[:endIdx], unicode.IsSpace)
 		p.buffer.Reset()
-		p.buffer.WriteString(ambig)
-		if unambig != "" {
-			return []nemotronEvent{nemotronEventContent{content: unambig}}, false
+
+		if remainder == "" {
+			p.state = Nemotron3NanoSkipWhitespaceAfterThinking
+		} else {
+			p.state = Nemotron3NanoCollectingContent
+			content, _, calls, err = p.toolParser.Add(remainder, done)
 		}
-		return nil, false
-
-	case Nemotron3NanoCollectingToolCalls:
-		if strings.Contains(bufStr, nemotronToolCallClose) {
-			split := strings.SplitN(bufStr, nemotronToolCallClose, 2)
-			remaining := strings.TrimLeftFunc(split[1], unicode.IsSpace)
-			p.buffer.Reset()
-			p.buffer.WriteString(remaining)
-
-			var events []nemotronEvent
-			if tc, err := p.parseToolCall(split[0]); err == nil {
-				events = append(events, nemotronEventToolCall{toolCall: tc})
-			}
-
-			if !strings.Contains(remaining, nemotronToolCallOpen) {
-				p.state = Nemotron3NanoCollectingContent
-			}
-			return events, true
-		}
-		return nil, false
+		return content, thinking, calls, err
 	}
 
-	return nil, false
+	// No end marker - emit unambiguous thinking
+	thinking = p.emitThinking(bufStr)
+	return "", thinking, nil, nil
 }
 
-var (
-	nemotronFunctionRegex  = regexp.MustCompile(`<function=([^>]+)>`)
-	nemotronParameterRegex = regexp.MustCompile(`<parameter=([^>]+)>\n?([\s\S]*?)\n?</parameter>`)
-)
+// emitThinking returns unambiguous thinking content, keeping potential partial tags in buffer
+func (p *Nemotron3NanoParser) emitThinking(bufStr string) string {
+	// Check for partial </think> or <tool_call> at end
+	thinkOverlap := overlap(bufStr, nemotronThinkClose)
+	toolOverlap := overlap(bufStr, nemotronToolCallOpen)
+	maxOverlap := max(thinkOverlap, toolOverlap)
 
-func (p *Nemotron3NanoParser) parseToolCall(content string) (api.ToolCall, error) {
-	toolCall := api.ToolCall{}
-
-	// Extract function name
-	fnMatch := nemotronFunctionRegex.FindStringSubmatch(content)
-	if len(fnMatch) < 2 {
-		return toolCall, nil
-	}
-	toolCall.Function.Name = fnMatch[1]
-
-	// Extract parameters
-	toolCall.Function.Arguments = api.NewToolCallFunctionArguments()
-	paramMatches := nemotronParameterRegex.FindAllStringSubmatch(content, -1)
-	for _, match := range paramMatches {
-		if len(match) >= 3 {
-			paramName := match[1]
-			paramValue := strings.TrimSpace(match[2])
-
-			// Try to parse as typed value based on tool definition
-			toolCall.Function.Arguments.Set(paramName, p.parseParamValue(paramName, paramValue))
-		}
+	if maxOverlap > 0 {
+		unambiguous := bufStr[:len(bufStr)-maxOverlap]
+		unambiguous = strings.TrimRightFunc(unambiguous, unicode.IsSpace)
+		p.buffer.Reset()
+		p.buffer.WriteString(bufStr[len(bufStr)-maxOverlap:])
+		return unambiguous
 	}
 
-	return toolCall, nil
-}
-
-func (p *Nemotron3NanoParser) parseParamValue(paramName string, raw string) any {
-	// Find the matching tool to get parameter type
-	var paramType api.PropertyType
-	for _, tool := range p.tools {
-		if tool.Function.Parameters.Properties != nil {
-			if prop, ok := tool.Function.Parameters.Properties.Get(paramName); ok {
-				paramType = prop.Type
-				break
-			}
-		}
-	}
-
-	return parseValue(raw, paramType)
+	// No partial tags - emit all but trailing whitespace
+	wsLen := trailingWhitespaceLen(bufStr)
+	if wsLen > 0 {
+		unambiguous := bufStr[:len(bufStr)-wsLen]
+		p.buffer.Reset()
+		p.buffer.WriteString(bufStr[len(bufStr)-wsLen:])
+		return unambiguous
+	}
+
+	// Nothing to hold back
+	p.buffer.Reset()
+	return bufStr
 }

model/parsers/nemotron3nano_test.go

@@ -8,6 +8,8 @@ import (
 	"github.com/ollama/ollama/api"
 )
 
+// TestNemotron3NanoParser tests Nemotron-specific behavior (thinking support).
+// Tool call parsing is tested in qwen3coder_test.go since Nemotron delegates to Qwen3CoderParser.
 func TestNemotron3NanoParser(t *testing.T) {
 	tests := []struct {
 		name             string
@@ -17,18 +19,6 @@ func TestNemotron3NanoParser(t *testing.T) {
 		expectedThinking string
 		expectedCalls    []api.ToolCall
 	}{
-		{
-			name:            "simple content - no thinking",
-			input:           "Hello, how can I help you?",
-			thinkValue:      nil,
-			expectedContent: "Hello, how can I help you?",
-		},
-		{
-			name:            "simple content - thinking disabled",
-			input:           "Hello, how can I help you?",
-			thinkValue:      &api.ThinkValue{Value: false},
-			expectedContent: "Hello, how can I help you?",
-		},
 		{
 			name:             "thinking then content",
 			input:            "Let me think about this...</think>\nHere is my answer.",
@@ -43,69 +33,6 @@ func TestNemotron3NanoParser(t *testing.T) {
 			expectedThinking: "Step 1: Analyze\nStep 2: Process\nStep 3: Conclude",
 			expectedContent:  "The answer is 42.",
 		},
-		{
-			name:       "simple tool call",
-			input:      "<tool_call>\n<function=get_weather>\n<parameter=city>\nParis\n</parameter>\n</function>\n</tool_call>",
-			thinkValue: nil,
-			expectedCalls: []api.ToolCall{
-				{
-					Function: api.ToolCallFunction{
-						Name:      "get_weather",
-						Arguments: testArgs(map[string]any{"city": "Paris"}),
-					},
-				},
-			},
-		},
-		{
-			name:            "content then tool call",
-			input:           "Let me check the weather.\n<tool_call>\n<function=get_weather>\n<parameter=city>\nNYC\n</parameter>\n</function>\n</tool_call>",
-			thinkValue:      nil,
-			expectedContent: "Let me check the weather.",
-			expectedCalls: []api.ToolCall{
-				{
-					Function: api.ToolCallFunction{
-						Name:      "get_weather",
-						Arguments: testArgs(map[string]any{"city": "NYC"}),
-					},
-				},
-			},
-		},
-		{
-			name:       "tool call with multiple parameters",
-			input:      "<tool_call>\n<function=book_flight>\n<parameter=from>\nSFO\n</parameter>\n<parameter=to>\nNYC\n</parameter>\n</function>\n</tool_call>",
-			thinkValue: nil,
-			expectedCalls: []api.ToolCall{
-				{
-					Function: api.ToolCallFunction{
-						Name: "book_flight",
-						Arguments: testArgs(map[string]any{
-							"from": "SFO",
-							"to":   "NYC",
-						}),
-					},
-				},
-			},
-		},
-		{
-			name: "multiple tool calls",
-			input: "<tool_call>\n<function=get_weather>\n<parameter=city>\nSan Francisco\n</parameter>\n</function>\n</tool_call>\n" +
-				"<tool_call>\n<function=get_weather>\n<parameter=city>\nNew York\n</parameter>\n</function>\n</tool_call>",
-			thinkValue: nil,
-			expectedCalls: []api.ToolCall{
-				{
-					Function: api.ToolCallFunction{
-						Name:      "get_weather",
-						Arguments: testArgs(map[string]any{"city": "San Francisco"}),
-					},
-				},
-				{
-					Function: api.ToolCallFunction{
-						Name:      "get_weather",
-						Arguments: testArgs(map[string]any{"city": "New York"}),
-					},
-				},
-			},
-		},
 		{
 			name:             "thinking then tool call",
 			input:            "I should check the weather...</think>\n<tool_call>\n<function=get_weather>\n<parameter=city>\nParis\n</parameter>\n</function>\n</tool_call>",
@@ -135,19 +62,6 @@ func TestNemotron3NanoParser(t *testing.T) {
 				},
 			},
 		},
-		{
-			name:       "tool call with multiline parameter value",
-			input:      "<tool_call>\n<function=create_note>\n<parameter=content>\nLine 1\nLine 2\nLine 3\n</parameter>\n</function>\n</tool_call>",
-			thinkValue: nil,
-			expectedCalls: []api.ToolCall{
-				{
-					Function: api.ToolCallFunction{
-						Name:      "create_note",
-						Arguments: testArgs(map[string]any{"content": "Line 1\nLine 2\nLine 3"}),
-					},
-				},
-			},
-		},
 		{
 			name:             "empty thinking block - immediate close",
 			input:            "</think>\nHere is my answer.",
@@ -161,18 +75,6 @@ func TestNemotron3NanoParser(t *testing.T) {
 			thinkValue:      &api.ThinkValue{Value: false},
 			expectedContent: "</think>\nSome content after spurious tag.",
 		},
-		{
-			name:          "tool call with no function name - returns empty tool call",
-			input:         "<tool_call>\n<function=>\n</function>\n</tool_call>",
-			thinkValue:    nil,
-			expectedCalls: []api.ToolCall{{Function: api.ToolCallFunction{Name: "", Arguments: api.NewToolCallFunctionArguments()}}},
-		},
-		{
-			name:            "content with newlines preserved",
-			input:           "Line 1\n\nLine 2\n\n\nLine 3",
-			thinkValue:      nil,
-			expectedContent: "Line 1\n\nLine 2\n\n\nLine 3",
-		},
 		{
 			name:             "thinking with only whitespace after close tag",
 			input:            "My thoughts...</think>   \n\t\n   Content here.",
@@ -180,25 +82,6 @@ func TestNemotron3NanoParser(t *testing.T) {
 			expectedThinking: "My thoughts...",
 			expectedContent:  "Content here.",
 		},
-		{
-			name:            "unicode content",
-			input:           "Hello 世界! 🌍 Ñoño",
-			thinkValue:      nil,
-			expectedContent: "Hello 世界! 🌍 Ñoño",
-		},
-		{
-			name:       "tool call with numeric parameter",
-			input:      "<tool_call>\n<function=set_temp>\n<parameter=value>\n42\n</parameter>\n</function>\n</tool_call>",
-			thinkValue: nil,
-			expectedCalls: []api.ToolCall{
-				{
-					Function: api.ToolCallFunction{
-						Name:      "set_temp",
-						Arguments: testArgs(map[string]any{"value": "42"}),
-					},
-				},
-			},
-		},
 	}
 
 	for _, tt := range tests {
@@ -233,6 +116,8 @@ func TestNemotron3NanoParser(t *testing.T) {
 	}
 }
 
+// TestNemotron3NanoParser_Streaming tests streaming behavior for thinking support.
+// Tool call streaming is tested in qwen3coder_test.go.
 func TestNemotron3NanoParser_Streaming(t *testing.T) {
 	tests := []struct {
 		name             string
@@ -242,18 +127,6 @@ func TestNemotron3NanoParser_Streaming(t *testing.T) {
 		expectedThinking string
 		expectedCalls    []api.ToolCall
 	}{
-		{
-			name:            "streaming content character by character",
-			chunks:          []string{"H", "e", "l", "l", "o", ",", " ", "w", "o", "r", "l", "d", "!"},
-			thinkValue:      nil,
-			expectedContent: "Hello, world!",
-		},
-		{
-			name:            "streaming content small tokens",
-			chunks:          []string{"Hel", "lo", ", ", "how ", "can", " I", " help", " you", " today", "?"},
-			thinkValue:      nil,
-			expectedContent: "Hello, how can I help you today?",
-		},
 		{
 			name:             "streaming thinking then content - granular",
 			chunks:           []string{"Let", " me", " th", "ink", " about", " this", "...", "<", "/", "think", ">", "\n", "Here", " is", " my", " answer", "."},
@@ -268,45 +141,6 @@ func TestNemotron3NanoParser_Streaming(t *testing.T) {
 			expectedThinking: "Step 1: Analyze\nStep 2: Process",
 			expectedContent:  "The answer.",
 		},
-		{
-			name:       "streaming tool call - highly granular",
-			chunks:     []string{"<", "tool", "_", "call", ">", "\n", "<", "func", "tion", "=", "get", "_", "weather", ">", "\n", "<", "param", "eter", "=", "city", ">", "\n", "Par", "is", "\n", "</", "param", "eter", ">", "\n", "</", "func", "tion", ">", "\n", "</", "tool", "_", "call", ">"},
-			thinkValue: nil,
-			expectedCalls: []api.ToolCall{
-				{
-					Function: api.ToolCallFunction{
-						Name:      "get_weather",
-						Arguments: testArgs(map[string]any{"city": "Paris"}),
-					},
-				},
-			},
-		},
-		{
-			name:            "streaming content then tool call - granular",
-			chunks:          []string{"Let", " me", " check", " the", " weather", ".", "\n<", "tool_call", ">", "\n", "<function=", "get_weather", ">", "\n", "<parameter=", "city", ">", "\n", "NYC", "\n", "</parameter>", "\n", "</function>", "\n", "</tool_call>"},
-			thinkValue:      nil,
-			expectedContent: "Let me check the weather.",
-			expectedCalls: []api.ToolCall{
-				{
-					Function: api.ToolCallFunction{
-						Name:      "get_weather",
-						Arguments: testArgs(map[string]any{"city": "NYC"}),
-					},
-				},
-			},
-		},
-		{
-			name:   "tool call tag split character by character",
-			chunks: []string{"<", "t", "o", "o", "l", "_", "c", "a", "l", "l", ">", "\n", "<", "f", "u", "n", "c", "t", "i", "o", "n", "=", "t", "e", "s", "t", ">", "\n", "<", "/", "f", "u", "n", "c", "t", "i", "o", "n", ">", "\n", "<", "/", "t", "o", "o", "l", "_", "c", "a", "l", "l", ">"},
-			expectedCalls: []api.ToolCall{
-				{
-					Function: api.ToolCallFunction{
-						Name:      "test",
-						Arguments: api.NewToolCallFunctionArguments(),
-					},
-				},
-			},
-		},
 		{
 			name:             "thinking close tag split character by character",
 			chunks:           []string{"I", "'", "m", " ", "t", "h", "i", "n", "k", "i", "n", "g", ".", ".", ".", "<", "/", "t", "h", "i", "n", "k", ">", "\n", "D", "o", "n", "e", "!"},
@@ -321,22 +155,6 @@ func TestNemotron3NanoParser_Streaming(t *testing.T) {
 			expectedThinking: "Thinking...",
 			expectedContent:  "Content here.",
 		},
-		{
-			name:       "tool call with multiple parameters - streaming",
-			chunks:     []string{"<tool_", "call>\n", "<function", "=book_", "flight>", "\n<para", "meter=", "from>\n", "SFO\n", "</param", "eter>", "\n<param", "eter=to", ">\nNYC", "\n</para", "meter>", "\n</func", "tion>\n", "</tool_", "call>"},
-			thinkValue: nil,
-			expectedCalls: []api.ToolCall{
-				{
-					Function: api.ToolCallFunction{
-						Name: "book_flight",
-						Arguments: testArgs(map[string]any{
-							"from": "SFO",
-							"to":   "NYC",
-						}),
-					},
-				},
-			},
-		},
 		{
 			name:             "thinking then content then tool call - streaming",
 			chunks:           []string{"Ana", "lyzing", " your", " request", "...", "</", "think", ">\n", "I'll", " check", " that", " for", " you", ".", "\n", "<tool", "_call", ">\n", "<function", "=search", ">\n", "<parameter", "=query", ">\n", "test", " query", "\n</", "parameter", ">\n", "</function", ">\n", "</tool", "_call", ">"},
@@ -352,45 +170,6 @@ func TestNemotron3NanoParser_Streaming(t *testing.T) {
 				},
 			},
 		},
-		{
-			name: "multiple tool calls - streaming",
-			chunks: []string{
-				"<tool_call>", "\n", "<function=", "get_weather>", "\n",
-				"<parameter=", "city>\n", "San Fran", "cisco\n", "</parameter>", "\n",
-				"</function>", "\n", "</tool_call>", "\n",
-				"<tool_", "call>\n", "<function", "=get_weather", ">\n",
-				"<param", "eter=city", ">\nNew", " York\n", "</parameter>\n",
-				"</function>\n", "</tool_call>",
-			},
-			thinkValue: nil,
-			expectedCalls: []api.ToolCall{
-				{
-					Function: api.ToolCallFunction{
-						Name:      "get_weather",
-						Arguments: testArgs(map[string]any{"city": "San Francisco"}),
-					},
-				},
-				{
-					Function: api.ToolCallFunction{
-						Name:      "get_weather",
-						Arguments: testArgs(map[string]any{"city": "New York"}),
-					},
-				},
-			},
-		},
-		{
-			name:       "tool call with multiline parameter - streaming",
-			chunks:     []string{"<tool_call>\n", "<function=", "create_note>\n", "<parameter=", "content>\n", "Line 1", "\nLine", " 2\n", "Line 3", "\n</parameter>\n", "</function>\n", "</tool_call>"},
-			thinkValue: nil,
-			expectedCalls: []api.ToolCall{
-				{
-					Function: api.ToolCallFunction{
-						Name:      "create_note",
-						Arguments: testArgs(map[string]any{"content": "Line 1\nLine 2\nLine 3"}),
-					},
-				},
-			},
-		},
 		{
 			name:             "empty thinking block",
 			chunks:           []string{"</think>", "\n", "Just content."},
@@ -398,12 +177,6 @@ func TestNemotron3NanoParser_Streaming(t *testing.T) {
 			expectedThinking: "",
 			expectedContent:  "Just content.",
 		},
-		{
-			name:            "empty input chunks interspersed",
-			chunks:          []string{"Hello", "", " ", "", "world", "", "!"},
-			thinkValue:      nil,
-			expectedContent: "Hello world!",
-		},
 		{
 			name:             "tool call immediately after think close - no content",
 			chunks:           []string{"Analyzing...", "</think>", "\n", "<tool_call>", "\n<function=test>\n</function>\n", "</tool_call>"},
@@ -418,25 +191,6 @@ func TestNemotron3NanoParser_Streaming(t *testing.T) {
 				},
 			},
 		},
-		{
-			name:       "tool call with empty parameter value",
-			chunks:     []string{"<tool_call>\n<function=test>\n<parameter=name>\n", "\n</parameter>\n</function>\n</tool_call>"},
-			thinkValue: nil,
-			expectedCalls: []api.ToolCall{
-				{
-					Function: api.ToolCallFunction{
-						Name:      "test",
-						Arguments: testArgs(map[string]any{"name": ""}),
-					},
-				},
-			},
-		},
-		{
-			name:            "partial tool call tag at end - buffered",
-			chunks:          []string{"Here's some content", "<tool"},
-			thinkValue:      nil,
-			expectedContent: "Here's some content",
-		},
 	}
 
 	for _, tt := range tests {
@@ -572,3 +326,65 @@ func TestNemotron3NanoParser_WithTools(t *testing.T) {
 		t.Errorf("calls mismatch (-got +want):\n%s", diff)
 	}
 }
+
+// TestNemotron3NanoParser_ToolCallWithoutThinkClose tests the case where thinking is enabled
+// but the model outputs content + tool call WITHOUT the </think> tag.
+// The parser should still parse the tool call (content before is treated as thinking).
+func TestNemotron3NanoParser_ToolCallWithoutThinkClose(t *testing.T) {
+	chunks := []string{
+		"Let", " me", " analyze", " this", ".", "\n",
+		"<tool_call>", "\n",
+		"<function=get_weather>", "\n",
+		"<parameter=city>", "Paris", "</parameter>", "\n",
+		"</function>", "\n",
+		"</tool_call>",
+	}
+
+	p := &Nemotron3NanoParser{}
+	p.Init(nil, nil, &api.ThinkValue{Value: true}) // thinking ENABLED but model doesn't output </think>
+
+	var allContent string
+	var allThinking string
+	var allCalls []api.ToolCall
+
+	for _, chunk := range chunks {
+		content, thinking, calls, err := p.Add(chunk, false)
+		if err != nil {
+			t.Fatalf("unexpected error: %v", err)
+		}
+		allContent += content
+		allThinking += thinking
+		allCalls = append(allCalls, calls...)
+	}
+
+	// Drain
+	content, thinking, calls, err := p.Add("", true)
+	if err != nil {
+		t.Fatalf("unexpected error on done: %v", err)
+	}
+	allContent += content
+	allThinking += thinking
+	allCalls = append(allCalls, calls...)
+
+	// The parser was in thinking mode, so text before <tool_call> is emitted as thinking.
+	expectedThinking := "Let me analyze this."
+
+	expectedCalls := []api.ToolCall{
+		{
+			Function: api.ToolCallFunction{
+				Name:      "get_weather",
+				Arguments: testArgs(map[string]any{"city": "Paris"}),
+			},
+		},
+	}
+
+	if allContent != "" {
+		t.Errorf("expected no content (text was streamed as thinking), got: %q", allContent)
+	}
+	if diff := cmp.Diff(allThinking, expectedThinking); diff != "" {
+		t.Errorf("thinking mismatch (-got +want):\n%s", diff)
+	}
+	if diff := cmp.Diff(allCalls, expectedCalls, argsComparer); diff != "" {
+		t.Errorf("calls mismatch (-got +want):\n%s", diff)
+	}
+}

model/parsers/qwen3coder_test.go

@@ -91,6 +91,37 @@ func TestQwenParserStreaming(t *testing.T) {
 				},
 			},
 		},
+		{
+			desc: "tool call tags split character by character",
+			steps: []step{
+				{input: "<", wantEvents: []qwenEvent{}},
+				{input: "t", wantEvents: []qwenEvent{}},
+				{input: "o", wantEvents: []qwenEvent{}},
+				{input: "o", wantEvents: []qwenEvent{}},
+				{input: "l", wantEvents: []qwenEvent{}},
+				{input: "_", wantEvents: []qwenEvent{}},
+				{input: "c", wantEvents: []qwenEvent{}},
+				{input: "a", wantEvents: []qwenEvent{}},
+				{input: "l", wantEvents: []qwenEvent{}},
+				{input: "l", wantEvents: []qwenEvent{}},
+				{input: ">", wantEvents: []qwenEvent{}},
+				{input: "a", wantEvents: []qwenEvent{}},
+				{input: "b", wantEvents: []qwenEvent{}},
+				{input: "c", wantEvents: []qwenEvent{}},
+				{input: "<", wantEvents: []qwenEvent{}},
+				{input: "/", wantEvents: []qwenEvent{}},
+				{input: "t", wantEvents: []qwenEvent{}},
+				{input: "o", wantEvents: []qwenEvent{}},
+				{input: "o", wantEvents: []qwenEvent{}},
+				{input: "l", wantEvents: []qwenEvent{}},
+				{input: "_", wantEvents: []qwenEvent{}},
+				{input: "c", wantEvents: []qwenEvent{}},
+				{input: "a", wantEvents: []qwenEvent{}},
+				{input: "l", wantEvents: []qwenEvent{}},
+				{input: "l", wantEvents: []qwenEvent{}},
+				{input: ">", wantEvents: []qwenEvent{qwenEventRawToolCall{raw: "abc"}}},
+			},
+		},
 		{
 			desc: "trailing whitespace between content and tool call",
 			steps: []step{

openai/openai.go

@@ -630,6 +630,10 @@ func nameFromToolCallID(messages []Message, toolCallID string) string {
 
 // decodeImageURL decodes a base64 data URI into raw image bytes.
 func decodeImageURL(url string) (api.ImageData, error) {
+	if strings.HasPrefix(url, "http://") || strings.HasPrefix(url, "https://") {
+		return nil, errors.New("image URLs are not currently supported, please use base64 encoded data instead")
+	}
+
 	types := []string{"jpeg", "jpg", "png", "webp"}
 
 	// Support blank mime type to match /api/chat's behavior of taking just unadorned base64
@@ -733,3 +737,60 @@ func FromCompleteRequest(r CompletionRequest) (api.GenerateRequest, error) {
 		DebugRenderOnly: r.DebugRenderOnly,
 	}, nil
 }
+
+// ImageGenerationRequest is an OpenAI-compatible image generation request.
+type ImageGenerationRequest struct {
+	Model          string `json:"model"`
+	Prompt         string `json:"prompt"`
+	N              int    `json:"n,omitempty"`
+	Size           string `json:"size,omitempty"`
+	ResponseFormat string `json:"response_format,omitempty"`
+	Seed           *int64 `json:"seed,omitempty"`
+}
+
+// ImageGenerationResponse is an OpenAI-compatible image generation response.
+type ImageGenerationResponse struct {
+	Created int64            `json:"created"`
+	Data    []ImageURLOrData `json:"data"`
+}
+
+// ImageURLOrData contains either a URL or base64-encoded image data.
+type ImageURLOrData struct {
+	URL     string `json:"url,omitempty"`
+	B64JSON string `json:"b64_json,omitempty"`
+}
+
+// FromImageGenerationRequest converts an OpenAI image generation request to an Ollama GenerateRequest.
+func FromImageGenerationRequest(r ImageGenerationRequest) api.GenerateRequest {
+	req := api.GenerateRequest{
+		Model:  r.Model,
+		Prompt: r.Prompt,
+	}
+	// Parse size if provided (e.g., "1024x768")
+	if r.Size != "" {
+		var w, h int32
+		if _, err := fmt.Sscanf(r.Size, "%dx%d", &w, &h); err == nil {
+			req.Width = w
+			req.Height = h
+		}
+	}
+	if r.Seed != nil {
+		if req.Options == nil {
+			req.Options = map[string]any{}
+		}
+		req.Options["seed"] = *r.Seed
+	}
+	return req
+}
+
+// ToImageGenerationResponse converts an Ollama GenerateResponse to an OpenAI ImageGenerationResponse.
+func ToImageGenerationResponse(resp api.GenerateResponse) ImageGenerationResponse {
+	var data []ImageURLOrData
+	if resp.Image != "" {
+		data = []ImageURLOrData{{B64JSON: resp.Image}}
+	}
+	return ImageGenerationResponse{
+		Created: resp.CreatedAt.Unix(),
+		Data:    data,
+	}
+}

openai/responses.go

@@ -4,6 +4,7 @@ import (
 	"encoding/json"
 	"fmt"
 	"math/rand"
+	"time"
 
 	"github.com/ollama/ollama/api"
 )
@@ -265,9 +266,9 @@ type ResponsesText struct {
 type ResponsesTool struct {
 	Type        string         `json:"type"` // "function"
 	Name        string         `json:"name"`
-	Description string         `json:"description,omitempty"`
-	Strict      bool           `json:"strict,omitempty"`
-	Parameters  map[string]any `json:"parameters,omitempty"`
+	Description *string        `json:"description"` // nullable but required
+	Strict      *bool          `json:"strict"`      // nullable but required
+	Parameters  map[string]any `json:"parameters"`  // nullable but required
 }
 
 type ResponsesRequest struct {
@@ -475,11 +476,16 @@ func convertTool(t ResponsesTool) (api.Tool, error) {
 		}
 	}
 
+	var description string
+	if t.Description != nil {
+		description = *t.Description
+	}
+
 	return api.Tool{
 		Type: t.Type,
 		Function: api.ToolFunction{
 			Name:        t.Name,
-			Description: t.Description,
+			Description: description,
 			Parameters:  params,
 		},
 	}, nil
@@ -516,17 +522,60 @@ func convertInputMessage(m ResponsesInputMessage) (api.Message, error) {
 
 // Response types for the Responses API
 
+// ResponsesTextField represents the text output configuration in the response.
+type ResponsesTextField struct {
+	Format ResponsesTextFormat `json:"format"`
+}
+
+// ResponsesReasoningOutput represents reasoning configuration in the response.
+type ResponsesReasoningOutput struct {
+	Effort  *string `json:"effort,omitempty"`
+	Summary *string `json:"summary,omitempty"`
+}
+
+// ResponsesError represents an error in the response.
+type ResponsesError struct {
+	Code    string `json:"code"`
+	Message string `json:"message"`
+}
+
+// ResponsesIncompleteDetails represents details about why a response was incomplete.
+type ResponsesIncompleteDetails struct {
+	Reason string `json:"reason"`
+}
+
 type ResponsesResponse struct {
-	ID        string                `json:"id"`
-	Object    string                `json:"object"`
-	CreatedAt int64                 `json:"created_at"`
-	Status    string                `json:"status"`
-	Model     string                `json:"model"`
-	Output    []ResponsesOutputItem `json:"output"`
-	Usage     *ResponsesUsage       `json:"usage,omitempty"`
-	// TODO(drifkin): add `temperature` and `top_p` to the response, but this
-	// requires additional plumbing to find the effective values since the
-	// defaults can come from the model or the request
+	ID                 string                      `json:"id"`
+	Object             string                      `json:"object"`
+	CreatedAt          int64                       `json:"created_at"`
+	CompletedAt        *int64                      `json:"completed_at"`
+	Status             string                      `json:"status"`
+	IncompleteDetails  *ResponsesIncompleteDetails `json:"incomplete_details"`
+	Model              string                      `json:"model"`
+	PreviousResponseID *string                     `json:"previous_response_id"`
+	Instructions       *string                     `json:"instructions"`
+	Output             []ResponsesOutputItem       `json:"output"`
+	Error              *ResponsesError             `json:"error"`
+	Tools              []ResponsesTool             `json:"tools"`
+	ToolChoice         any                         `json:"tool_choice"`
+	Truncation         string                      `json:"truncation"`
+	ParallelToolCalls  bool                        `json:"parallel_tool_calls"`
+	Text               ResponsesTextField          `json:"text"`
+	TopP               float64                     `json:"top_p"`
+	PresencePenalty    float64                     `json:"presence_penalty"`
+	FrequencyPenalty   float64                     `json:"frequency_penalty"`
+	TopLogprobs        int                         `json:"top_logprobs"`
+	Temperature        float64                     `json:"temperature"`
+	Reasoning          *ResponsesReasoningOutput   `json:"reasoning"`
+	Usage              *ResponsesUsage             `json:"usage"`
+	MaxOutputTokens    *int                        `json:"max_output_tokens"`
+	MaxToolCalls       *int                        `json:"max_tool_calls"`
+	Store              bool                        `json:"store"`
+	Background         bool                        `json:"background"`
+	ServiceTier        string                      `json:"service_tier"`
+	Metadata           map[string]any              `json:"metadata"`
+	SafetyIdentifier   *string                     `json:"safety_identifier"`
+	PromptCacheKey     *string                     `json:"prompt_cache_key"`
 }
 
 type ResponsesOutputItem struct {
@@ -550,18 +599,39 @@ type ResponsesReasoningSummary struct {
 }
 
 type ResponsesOutputContent struct {
-	Type string `json:"type"` // "output_text"
-	Text string `json:"text"`
+	Type        string `json:"type"` // "output_text"
+	Text        string `json:"text"`
+	Annotations []any  `json:"annotations"`
+	Logprobs    []any  `json:"logprobs"`
+}
+
+type ResponsesInputTokensDetails struct {
+	CachedTokens int `json:"cached_tokens"`
+}
+
+type ResponsesOutputTokensDetails struct {
+	ReasoningTokens int `json:"reasoning_tokens"`
 }
 
 type ResponsesUsage struct {
-	InputTokens  int `json:"input_tokens"`
-	OutputTokens int `json:"output_tokens"`
-	TotalTokens  int `json:"total_tokens"`
+	InputTokens         int                          `json:"input_tokens"`
+	OutputTokens        int                          `json:"output_tokens"`
+	TotalTokens         int                          `json:"total_tokens"`
+	InputTokensDetails  ResponsesInputTokensDetails  `json:"input_tokens_details"`
+	OutputTokensDetails ResponsesOutputTokensDetails `json:"output_tokens_details"`
 }
 
-// ToResponse converts an api.ChatResponse to a Responses API response
-func ToResponse(model, responseID, itemID string, chatResponse api.ChatResponse) ResponsesResponse {
+// derefFloat64 returns the value of a float64 pointer, or a default if nil.
+func derefFloat64(p *float64, def float64) float64 {
+	if p != nil {
+		return *p
+	}
+	return def
+}
+
+// ToResponse converts an api.ChatResponse to a Responses API response.
+// The request is used to echo back request parameters in the response.
+func ToResponse(model, responseID, itemID string, chatResponse api.ChatResponse, request ResponsesRequest) ResponsesResponse {
 	var output []ResponsesOutputItem
 
 	// Add reasoning item if thinking is present
@@ -585,6 +655,7 @@ func ToResponse(model, responseID, itemID string, chatResponse api.ChatResponse)
 			output = append(output, ResponsesOutputItem{
 				ID:        fmt.Sprintf("fc_%s_%d", responseID, i),
 				Type:      "function_call",
+				Status:    "completed",
 				CallID:    tc.ID,
 				Name:      tc.Function.Name,
 				Arguments: tc.Function.Arguments,
@@ -598,25 +669,90 @@ func ToResponse(model, responseID, itemID string, chatResponse api.ChatResponse)
 			Role:   "assistant",
 			Content: []ResponsesOutputContent{
 				{
-					Type: "output_text",
-					Text: chatResponse.Message.Content,
+					Type:        "output_text",
+					Text:        chatResponse.Message.Content,
+					Annotations: []any{},
+					Logprobs:    []any{},
 				},
 			},
 		})
 	}
 
+	var instructions *string
+	if request.Instructions != "" {
+		instructions = &request.Instructions
+	}
+
+	// Build truncation with default
+	truncation := "disabled"
+	if request.Truncation != nil {
+		truncation = *request.Truncation
+	}
+
+	tools := request.Tools
+	if tools == nil {
+		tools = []ResponsesTool{}
+	}
+
+	text := ResponsesTextField{
+		Format: ResponsesTextFormat{Type: "text"},
+	}
+	if request.Text != nil && request.Text.Format != nil {
+		text.Format = *request.Text.Format
+	}
+
+	// Build reasoning output from request
+	var reasoning *ResponsesReasoningOutput
+	if request.Reasoning.Effort != "" || request.Reasoning.Summary != "" {
+		reasoning = &ResponsesReasoningOutput{}
+		if request.Reasoning.Effort != "" {
+			reasoning.Effort = &request.Reasoning.Effort
+		}
+		if request.Reasoning.Summary != "" {
+			reasoning.Summary = &request.Reasoning.Summary
+		}
+	}
+
 	return ResponsesResponse{
-		ID:        responseID,
-		Object:    "response",
-		CreatedAt: chatResponse.CreatedAt.Unix(),
-		Status:    "completed",
-		Model:     model,
-		Output:    output,
+		ID:                 responseID,
+		Object:             "response",
+		CreatedAt:          chatResponse.CreatedAt.Unix(),
+		CompletedAt:        nil, // Set by middleware when writing final response
+		Status:             "completed",
+		IncompleteDetails:  nil, // Only populated if response incomplete
+		Model:              model,
+		PreviousResponseID: nil, // Not supported
+		Instructions:       instructions,
+		Output:             output,
+		Error:              nil, // Only populated on failure
+		Tools:              tools,
+		ToolChoice:         "auto", // Default value
+		Truncation:         truncation,
+		ParallelToolCalls:  true, // Default value
+		Text:               text,
+		TopP:               derefFloat64(request.TopP, 1.0),
+		PresencePenalty:    0, // Default value
+		FrequencyPenalty:   0, // Default value
+		TopLogprobs:        0, // Default value
+		Temperature:        derefFloat64(request.Temperature, 1.0),
+		Reasoning:          reasoning,
 		Usage: &ResponsesUsage{
 			InputTokens:  chatResponse.PromptEvalCount,
 			OutputTokens: chatResponse.EvalCount,
 			TotalTokens:  chatResponse.PromptEvalCount + chatResponse.EvalCount,
+			// TODO(drifkin): wire through the actual values
+			InputTokensDetails: ResponsesInputTokensDetails{CachedTokens: 0},
+			// TODO(drifkin): wire through the actual values
+			OutputTokensDetails: ResponsesOutputTokensDetails{ReasoningTokens: 0},
 		},
+		MaxOutputTokens:  request.MaxOutputTokens,
+		MaxToolCalls:     nil,   // Not supported
+		Store:            false, // We don't store responses
+		Background:       request.Background,
+		ServiceTier:      "default", // Default value
+		Metadata:         map[string]any{},
+		SafetyIdentifier: nil, // Not supported
+		PromptCacheKey:   nil, // Not supported
 	}
 }
 
@@ -636,6 +772,7 @@ type ResponsesStreamConverter struct {
 	responseID string
 	itemID     string
 	model      string
+	request    ResponsesRequest
 
 	// State tracking (mutated across Process calls)
 	firstWrite      bool
@@ -668,11 +805,12 @@ func (c *ResponsesStreamConverter) newEvent(eventType string, data map[string]an
 }
 
 // NewResponsesStreamConverter creates a new converter with the given configuration.
-func NewResponsesStreamConverter(responseID, itemID, model string) *ResponsesStreamConverter {
+func NewResponsesStreamConverter(responseID, itemID, model string, request ResponsesRequest) *ResponsesStreamConverter {
 	return &ResponsesStreamConverter{
 		responseID: responseID,
 		itemID:     itemID,
 		model:      model,
+		request:    request,
 		firstWrite: true,
 	}
 }
@@ -717,25 +855,120 @@ func (c *ResponsesStreamConverter) Process(r api.ChatResponse) []ResponsesStream
 	return events
 }
 
+// buildResponseObject creates a full response object with all required fields for streaming events.
+func (c *ResponsesStreamConverter) buildResponseObject(status string, output []any, usage map[string]any) map[string]any {
+	var instructions any = nil
+	if c.request.Instructions != "" {
+		instructions = c.request.Instructions
+	}
+
+	truncation := "disabled"
+	if c.request.Truncation != nil {
+		truncation = *c.request.Truncation
+	}
+
+	var tools []any
+	if c.request.Tools != nil {
+		for _, t := range c.request.Tools {
+			tools = append(tools, map[string]any{
+				"type":        t.Type,
+				"name":        t.Name,
+				"description": t.Description,
+				"strict":      t.Strict,
+				"parameters":  t.Parameters,
+			})
+		}
+	}
+	if tools == nil {
+		tools = []any{}
+	}
+
+	textFormat := map[string]any{"type": "text"}
+	if c.request.Text != nil && c.request.Text.Format != nil {
+		textFormat = map[string]any{
+			"type": c.request.Text.Format.Type,
+		}
+		if c.request.Text.Format.Name != "" {
+			textFormat["name"] = c.request.Text.Format.Name
+		}
+		if c.request.Text.Format.Schema != nil {
+			textFormat["schema"] = c.request.Text.Format.Schema
+		}
+		if c.request.Text.Format.Strict != nil {
+			textFormat["strict"] = *c.request.Text.Format.Strict
+		}
+	}
+
+	var reasoning any = nil
+	if c.request.Reasoning.Effort != "" || c.request.Reasoning.Summary != "" {
+		r := map[string]any{}
+		if c.request.Reasoning.Effort != "" {
+			r["effort"] = c.request.Reasoning.Effort
+		} else {
+			r["effort"] = nil
+		}
+		if c.request.Reasoning.Summary != "" {
+			r["summary"] = c.request.Reasoning.Summary
+		} else {
+			r["summary"] = nil
+		}
+		reasoning = r
+	}
+
+	// Build top_p and temperature with defaults
+	topP := 1.0
+	if c.request.TopP != nil {
+		topP = *c.request.TopP
+	}
+	temperature := 1.0
+	if c.request.Temperature != nil {
+		temperature = *c.request.Temperature
+	}
+
+	return map[string]any{
+		"id":                   c.responseID,
+		"object":               "response",
+		"created_at":           time.Now().Unix(),
+		"completed_at":         nil,
+		"status":               status,
+		"incomplete_details":   nil,
+		"model":                c.model,
+		"previous_response_id": nil,
+		"instructions":         instructions,
+		"output":               output,
+		"error":                nil,
+		"tools":                tools,
+		"tool_choice":          "auto",
+		"truncation":           truncation,
+		"parallel_tool_calls":  true,
+		"text":                 map[string]any{"format": textFormat},
+		"top_p":                topP,
+		"presence_penalty":     0,
+		"frequency_penalty":    0,
+		"top_logprobs":         0,
+		"temperature":          temperature,
+		"reasoning":            reasoning,
+		"usage":                usage,
+		"max_output_tokens":    c.request.MaxOutputTokens,
+		"max_tool_calls":       nil,
+		"store":                false,
+		"background":           c.request.Background,
+		"service_tier":         "default",
+		"metadata":             map[string]any{},
+		"safety_identifier":    nil,
+		"prompt_cache_key":     nil,
+	}
+}
+
 func (c *ResponsesStreamConverter) createResponseCreatedEvent() ResponsesStreamEvent {
 	return c.newEvent("response.created", map[string]any{
-		"response": map[string]any{
-			"id":     c.responseID,
-			"object": "response",
-			"status": "in_progress",
-			"output": []any{},
-		},
+		"response": c.buildResponseObject("in_progress", []any{}, nil),
 	})
 }
 
 func (c *ResponsesStreamConverter) createResponseInProgressEvent() ResponsesStreamEvent {
 	return c.newEvent("response.in_progress", map[string]any{
-		"response": map[string]any{
-			"id":     c.responseID,
-			"object": "response",
-			"status": "in_progress",
-			"output": []any{},
-		},
+		"response": c.buildResponseObject("in_progress", []any{}, nil),
 	})
 }
 
@@ -762,9 +995,10 @@ func (c *ResponsesStreamConverter) processThinking(thinking string) []ResponsesS
 
 	// Emit delta
 	events = append(events, c.newEvent("response.reasoning_summary_text.delta", map[string]any{
-		"item_id":      c.reasoningItemID,
-		"output_index": c.outputIndex,
-		"delta":        thinking,
+		"item_id":       c.reasoningItemID,
+		"output_index":  c.outputIndex,
+		"summary_index": 0,
+		"delta":         thinking,
 	}))
 
 	// TODO(drifkin): consider adding
@@ -783,9 +1017,10 @@ func (c *ResponsesStreamConverter) finishReasoning() []ResponsesStreamEvent {
 
 	events := []ResponsesStreamEvent{
 		c.newEvent("response.reasoning_summary_text.done", map[string]any{
-			"item_id":      c.reasoningItemID,
-			"output_index": c.outputIndex,
-			"text":         c.accumulatedThinking,
+			"item_id":       c.reasoningItemID,
+			"output_index":  c.outputIndex,
+			"summary_index": 0,
+			"text":          c.accumulatedThinking,
 		}),
 		c.newEvent("response.output_item.done", map[string]any{
 			"output_index": c.outputIndex,
@@ -898,8 +1133,10 @@ func (c *ResponsesStreamConverter) processTextContent(content string) []Response
 			"output_index":  c.outputIndex,
 			"content_index": c.contentIndex,
 			"part": map[string]any{
-				"type": "output_text",
-				"text": "",
+				"type":        "output_text",
+				"text":        "",
+				"annotations": []any{},
+				"logprobs":    []any{},
 			},
 		}))
 	}
@@ -913,6 +1150,7 @@ func (c *ResponsesStreamConverter) processTextContent(content string) []Response
 		"output_index":  c.outputIndex,
 		"content_index": 0,
 		"delta":         content,
+		"logprobs":      []any{},
 	}))
 
 	return events
@@ -944,8 +1182,10 @@ func (c *ResponsesStreamConverter) buildFinalOutput() []any {
 			"status": "completed",
 			"role":   "assistant",
 			"content": []map[string]any{{
-				"type": "output_text",
-				"text": c.accumulatedText,
+				"type":        "output_text",
+				"text":        c.accumulatedText,
+				"annotations": []any{},
+				"logprobs":    []any{},
 			}},
 		})
 	}
@@ -967,6 +1207,7 @@ func (c *ResponsesStreamConverter) processCompletion(r api.ChatResponse) []Respo
 			"output_index":  c.outputIndex,
 			"content_index": 0,
 			"text":          c.accumulatedText,
+			"logprobs":      []any{},
 		}))
 
 		// response.content_part.done
@@ -975,8 +1216,10 @@ func (c *ResponsesStreamConverter) processCompletion(r api.ChatResponse) []Respo
 			"output_index":  c.outputIndex,
 			"content_index": 0,
 			"part": map[string]any{
-				"type": "output_text",
-				"text": c.accumulatedText,
+				"type":        "output_text",
+				"text":        c.accumulatedText,
+				"annotations": []any{},
+				"logprobs":    []any{},
 			},
 		}))
 
@@ -989,26 +1232,31 @@ func (c *ResponsesStreamConverter) processCompletion(r api.ChatResponse) []Respo
 				"status": "completed",
 				"role":   "assistant",
 				"content": []map[string]any{{
-					"type": "output_text",
-					"text": c.accumulatedText,
+					"type":        "output_text",
+					"text":        c.accumulatedText,
+					"annotations": []any{},
+					"logprobs":    []any{},
 				}},
 			},
 		}))
 	}
 
 	// response.completed
-	events = append(events, c.newEvent("response.completed", map[string]any{
-		"response": map[string]any{
-			"id":     c.responseID,
-			"object": "response",
-			"status": "completed",
-			"output": c.buildFinalOutput(),
-			"usage": map[string]any{
-				"input_tokens":  r.PromptEvalCount,
-				"output_tokens": r.EvalCount,
-				"total_tokens":  r.PromptEvalCount + r.EvalCount,
-			},
+	usage := map[string]any{
+		"input_tokens":  r.PromptEvalCount,
+		"output_tokens": r.EvalCount,
+		"total_tokens":  r.PromptEvalCount + r.EvalCount,
+		"input_tokens_details": map[string]any{
+			"cached_tokens": 0,
 		},
+		"output_tokens_details": map[string]any{
+			"reasoning_tokens": 0,
+		},
+	}
+	response := c.buildResponseObject("completed", c.buildFinalOutput(), usage)
+	response["completed_at"] = time.Now().Unix()
+	events = append(events, c.newEvent("response.completed", map[string]any{
+		"response": response,
 	}))
 
 	return events

openai/responses_test.go

@@ -850,7 +850,7 @@ func TestFromResponsesRequest_Images(t *testing.T) {
 }
 
 func TestResponsesStreamConverter_TextOnly(t *testing.T) {
-	converter := NewResponsesStreamConverter("resp_123", "msg_456", "gpt-oss:20b")
+	converter := NewResponsesStreamConverter("resp_123", "msg_456", "gpt-oss:20b", ResponsesRequest{})
 
 	// First chunk with content
 	events := converter.Process(api.ChatResponse{
@@ -916,7 +916,7 @@ func TestResponsesStreamConverter_TextOnly(t *testing.T) {
 }
 
 func TestResponsesStreamConverter_ToolCalls(t *testing.T) {
-	converter := NewResponsesStreamConverter("resp_123", "msg_456", "gpt-oss:20b")
+	converter := NewResponsesStreamConverter("resp_123", "msg_456", "gpt-oss:20b", ResponsesRequest{})
 
 	events := converter.Process(api.ChatResponse{
 		Message: api.Message{
@@ -952,7 +952,7 @@ func TestResponsesStreamConverter_ToolCalls(t *testing.T) {
 }
 
 func TestResponsesStreamConverter_Reasoning(t *testing.T) {
-	converter := NewResponsesStreamConverter("resp_123", "msg_456", "gpt-oss:20b")
+	converter := NewResponsesStreamConverter("resp_123", "msg_456", "gpt-oss:20b", ResponsesRequest{})
 
 	// First chunk with thinking
 	events := converter.Process(api.ChatResponse{
@@ -1267,7 +1267,7 @@ func TestToResponse_WithReasoning(t *testing.T) {
 			Content:  "The answer is 42",
 		},
 		Done: true,
-	})
+	}, ResponsesRequest{})
 
 	// Should have 2 output items: reasoning + message
 	if len(response.Output) != 2 {
@@ -1638,7 +1638,7 @@ func TestFromResponsesRequest_ShorthandFormats(t *testing.T) {
 
 func TestResponsesStreamConverter_OutputIncludesContent(t *testing.T) {
 	// Verify that response.output_item.done includes content field for messages
-	converter := NewResponsesStreamConverter("resp_123", "msg_456", "gpt-oss:20b")
+	converter := NewResponsesStreamConverter("resp_123", "msg_456", "gpt-oss:20b", ResponsesRequest{})
 
 	// First chunk
 	converter.Process(api.ChatResponse{
@@ -1686,7 +1686,7 @@ func TestResponsesStreamConverter_OutputIncludesContent(t *testing.T) {
 
 func TestResponsesStreamConverter_ResponseCompletedIncludesOutput(t *testing.T) {
 	// Verify that response.completed includes the output array
-	converter := NewResponsesStreamConverter("resp_123", "msg_456", "gpt-oss:20b")
+	converter := NewResponsesStreamConverter("resp_123", "msg_456", "gpt-oss:20b", ResponsesRequest{})
 
 	// Process some content
 	converter.Process(api.ChatResponse{
@@ -1730,7 +1730,7 @@ func TestResponsesStreamConverter_ResponseCompletedIncludesOutput(t *testing.T)
 
 func TestResponsesStreamConverter_ResponseCreatedIncludesOutput(t *testing.T) {
 	// Verify that response.created includes an empty output array
-	converter := NewResponsesStreamConverter("resp_123", "msg_456", "gpt-oss:20b")
+	converter := NewResponsesStreamConverter("resp_123", "msg_456", "gpt-oss:20b", ResponsesRequest{})
 
 	events := converter.Process(api.ChatResponse{
 		Message: api.Message{Content: "Hi"},
@@ -1757,7 +1757,7 @@ func TestResponsesStreamConverter_ResponseCreatedIncludesOutput(t *testing.T) {
 
 func TestResponsesStreamConverter_SequenceNumbers(t *testing.T) {
 	// Verify that events include incrementing sequence numbers
-	converter := NewResponsesStreamConverter("resp_123", "msg_456", "gpt-oss:20b")
+	converter := NewResponsesStreamConverter("resp_123", "msg_456", "gpt-oss:20b", ResponsesRequest{})
 
 	events := converter.Process(api.ChatResponse{
 		Message: api.Message{Content: "Hello"},
@@ -1791,7 +1791,7 @@ func TestResponsesStreamConverter_SequenceNumbers(t *testing.T) {
 
 func TestResponsesStreamConverter_FunctionCallStatus(t *testing.T) {
 	// Verify that function call items include status field
-	converter := NewResponsesStreamConverter("resp_123", "msg_456", "gpt-oss:20b")
+	converter := NewResponsesStreamConverter("resp_123", "msg_456", "gpt-oss:20b", ResponsesRequest{})
 
 	events := converter.Process(api.ChatResponse{
 		Message: api.Message{

readline/readline.go

@@ -5,6 +5,7 @@ import (
 	"fmt"
 	"io"
 	"os"
+	"strings"
 )
 
 type Prompt struct {
@@ -36,10 +37,11 @@ type Terminal struct {
 }
 
 type Instance struct {
-	Prompt   *Prompt
-	Terminal *Terminal
-	History  *History
-	Pasting  bool
+	Prompt      *Prompt
+	Terminal    *Terminal
+	History     *History
+	Pasting     bool
+	pastedLines []string
 }
 
 func New(prompt Prompt) (*Instance, error) {
@@ -174,6 +176,8 @@ func (i *Instance) Readline() (string, error) {
 		case CharEsc:
 			esc = true
 		case CharInterrupt:
+			i.pastedLines = nil
+			i.Prompt.UseAlt = false
 			return "", ErrInterrupt
 		case CharPrev:
 			i.historyPrev(buf, &currentLineBuf)
@@ -188,7 +192,23 @@ func (i *Instance) Readline() (string, error) {
 		case CharForward:
 			buf.MoveRight()
 		case CharBackspace, CharCtrlH:
-			buf.Remove()
+			if buf.IsEmpty() && len(i.pastedLines) > 0 {
+				lastIdx := len(i.pastedLines) - 1
+				prevLine := i.pastedLines[lastIdx]
+				i.pastedLines = i.pastedLines[:lastIdx]
+				fmt.Print(CursorBOL + ClearToEOL + CursorUp + CursorBOL + ClearToEOL)
+				if len(i.pastedLines) == 0 {
+					fmt.Print(i.Prompt.Prompt)
+					i.Prompt.UseAlt = false
+				} else {
+					fmt.Print(i.Prompt.AltPrompt)
+				}
+				for _, r := range prevLine {
+					buf.Add(r)
+				}
+			} else {
+				buf.Remove()
+			}
 		case CharTab:
 			// todo: convert back to real tabs
 			for range 8 {
@@ -211,13 +231,28 @@ func (i *Instance) Readline() (string, error) {
 		case CharCtrlZ:
 			fd := os.Stdin.Fd()
 			return handleCharCtrlZ(fd, i.Terminal.termios)
-		case CharEnter, CharCtrlJ:
+		case CharCtrlJ:
+			i.pastedLines = append(i.pastedLines, buf.String())
+			buf.Buf.Clear()
+			buf.Pos = 0
+			buf.DisplayPos = 0
+			buf.LineHasSpace.Clear()
+			fmt.Println()
+			fmt.Print(i.Prompt.AltPrompt)
+			i.Prompt.UseAlt = true
+			continue
+		case CharEnter:
 			output := buf.String()
+			if len(i.pastedLines) > 0 {
+				output = strings.Join(i.pastedLines, "\n") + "\n" + output
+				i.pastedLines = nil
+			}
 			if output != "" {
 				i.History.Add(output)
 			}
 			buf.MoveToEnd()
 			fmt.Println()
+			i.Prompt.UseAlt = false
 
 			return output, nil
 		default:

scripts/build_darwin.sh

@@ -60,7 +60,7 @@ _build_darwin() {
             cmake --install $BUILD_DIR --component MLX
             # Override CGO flags to point to the amd64 build directory
             MLX_CGO_CFLAGS="-O3 -I$(pwd)/$BUILD_DIR/_deps/mlx-c-src -mmacosx-version-min=14.0"
-            MLX_CGO_LDFLAGS="-L$(pwd)/$BUILD_DIR/lib/ollama -lmlxc -lmlx -Wl,-rpath,@executable_path -lc++ -framework Accelerate -mmacosx-version-min=14.0"
+            MLX_CGO_LDFLAGS="-ldl -lc++ -framework Accelerate -mmacosx-version-min=14.0"
         else
             BUILD_DIR=build
             cmake --preset MLX \
@@ -71,10 +71,12 @@ _build_darwin() {
             cmake --install $BUILD_DIR --component MLX
             # Use default CGO flags from mlx.go for arm64
             MLX_CGO_CFLAGS="-O3 -I$(pwd)/$BUILD_DIR/_deps/mlx-c-src -mmacosx-version-min=14.0"
-            MLX_CGO_LDFLAGS="-L$(pwd)/$BUILD_DIR/lib/ollama -lmlxc -lmlx -Wl,-rpath,@executable_path -lc++ -framework Metal -framework Foundation -framework Accelerate -mmacosx-version-min=14.0"
+            MLX_CGO_LDFLAGS="-lc++ -framework Metal -framework Foundation -framework Accelerate -mmacosx-version-min=14.0"
         fi
-        GOOS=darwin GOARCH=$ARCH CGO_ENABLED=1 CGO_CFLAGS="$MLX_CGO_CFLAGS" CGO_LDFLAGS="$MLX_CGO_LDFLAGS" go build -tags mlx -o $INSTALL_PREFIX/imagegen ./x/imagegen/cmd/engine
-        GOOS=darwin GOARCH=$ARCH CGO_ENABLED=1 go build -o $INSTALL_PREFIX .
+        GOOS=darwin GOARCH=$ARCH CGO_ENABLED=1 CGO_CFLAGS="$MLX_CGO_CFLAGS" CGO_LDFLAGS="$MLX_CGO_LDFLAGS" go build -tags mlx -o $INSTALL_PREFIX .
+        # Copy MLX libraries to same directory as executable for dlopen
+        cp $INSTALL_PREFIX/lib/ollama/libmlxc.dylib $INSTALL_PREFIX/
+        cp $INSTALL_PREFIX/lib/ollama/libmlx.dylib $INSTALL_PREFIX/
     done
 }
 
@@ -82,19 +84,17 @@ _sign_darwin() {
     status "Creating universal binary..."
     mkdir -p dist/darwin
     lipo -create -output dist/darwin/ollama dist/darwin-*/ollama
-    lipo -create -output dist/darwin/imagegen dist/darwin-*/imagegen
     chmod +x dist/darwin/ollama
-    chmod +x dist/darwin/imagegen
 
     if [ -n "$APPLE_IDENTITY" ]; then
-        for F in dist/darwin/ollama dist/darwin-*/lib/ollama/* dist/darwin/imagegen; do
+        for F in dist/darwin/ollama dist/darwin-*/lib/ollama/*; do
             codesign -f --timestamp -s "$APPLE_IDENTITY" --identifier ai.ollama.ollama --options=runtime $F
         done
 
         # create a temporary zip for notarization
         TEMP=$(mktemp -u).zip
         ditto -c -k --keepParent dist/darwin/ollama "$TEMP"
-        xcrun notarytool submit "$TEMP" --wait --timeout 10m --apple-id $APPLE_ID --password $APPLE_PASSWORD --team-id $APPLE_TEAM_ID
+        xcrun notarytool submit "$TEMP" --wait --timeout 20m --apple-id $APPLE_ID --password $APPLE_PASSWORD --team-id $APPLE_TEAM_ID
         rm -f "$TEMP"
     fi
 
@@ -154,38 +154,38 @@ _build_macapp() {
     mkdir -p dist/Ollama.app/Contents/Resources
     if [ -d dist/darwin-amd64 ]; then
         lipo -create -output dist/Ollama.app/Contents/Resources/ollama dist/darwin-amd64/ollama dist/darwin-arm64/ollama
-        lipo -create -output dist/Ollama.app/Contents/Resources/imagegen dist/darwin-amd64/imagegen dist/darwin-arm64/imagegen
         for F in dist/darwin-amd64/lib/ollama/*mlx*.dylib ; do
             lipo -create -output dist/darwin/$(basename $F) $F dist/darwin-arm64/lib/ollama/$(basename $F)
         done
         cp dist/darwin-*/lib/ollama/*.so dist/darwin-*/lib/ollama/*.dylib dist/Ollama.app/Contents/Resources/
         cp dist/darwin/*.dylib dist/Ollama.app/Contents/Resources/
+        # Copy MLX metallib (architecture-independent, just use arm64 version)
+        cp dist/darwin-arm64/lib/ollama/*.metallib dist/Ollama.app/Contents/Resources/ 2>/dev/null || true
     else
         cp -a dist/darwin/ollama dist/Ollama.app/Contents/Resources/ollama
         cp dist/darwin/*.so dist/darwin/*.dylib dist/Ollama.app/Contents/Resources/
     fi
-    cp -a dist/darwin/imagegen dist/Ollama.app/Contents/Resources/imagegen
     chmod a+x dist/Ollama.app/Contents/Resources/ollama
 
     # Sign
     if [ -n "$APPLE_IDENTITY" ]; then
         codesign -f --timestamp -s "$APPLE_IDENTITY" --identifier ai.ollama.ollama --options=runtime dist/Ollama.app/Contents/Resources/ollama
-        for lib in dist/Ollama.app/Contents/Resources/*.so dist/Ollama.app/Contents/Resources/*.dylib dist/Ollama.app/Contents/Resources/imagegen ; do
+        for lib in dist/Ollama.app/Contents/Resources/*.so dist/Ollama.app/Contents/Resources/*.dylib dist/Ollama.app/Contents/Resources/*.metallib ; do
             codesign -f --timestamp -s "$APPLE_IDENTITY" --identifier ai.ollama.ollama --options=runtime ${lib}
         done
         codesign -f --timestamp -s "$APPLE_IDENTITY" --identifier com.electron.ollama --deep --options=runtime dist/Ollama.app
     fi
 
     rm -f dist/Ollama-darwin.zip
-    ditto -c -k --keepParent dist/Ollama.app dist/Ollama-darwin.zip
-    (cd dist/Ollama.app/Contents/Resources/; tar -cf - ollama imagegen *.so *.dylib) | gzip -9vc > dist/ollama-darwin.tgz
+    ditto -c -k --norsrc --keepParent dist/Ollama.app dist/Ollama-darwin.zip
+    (cd dist/Ollama.app/Contents/Resources/; tar -cf - ollama *.so *.dylib *.metallib 2>/dev/null) | gzip -9vc > dist/ollama-darwin.tgz
 
     # Notarize and Staple
     if [ -n "$APPLE_IDENTITY" ]; then
-        $(xcrun -f notarytool) submit dist/Ollama-darwin.zip --wait --timeout 10m --apple-id "$APPLE_ID" --password "$APPLE_PASSWORD" --team-id "$APPLE_TEAM_ID"
+        $(xcrun -f notarytool) submit dist/Ollama-darwin.zip --wait --timeout 20m --apple-id "$APPLE_ID" --password "$APPLE_PASSWORD" --team-id "$APPLE_TEAM_ID"
         rm -f dist/Ollama-darwin.zip
         $(xcrun -f stapler) staple dist/Ollama.app
-        ditto -c -k --keepParent dist/Ollama.app dist/Ollama-darwin.zip
+        ditto -c -k --norsrc --keepParent dist/Ollama.app dist/Ollama-darwin.zip
 
         rm -f dist/Ollama.dmg
 
@@ -206,7 +206,7 @@ _build_macapp() {
         rm -f dist/rw*.dmg
 
         codesign -f --timestamp -s "$APPLE_IDENTITY" --identifier ai.ollama.ollama --options=runtime dist/Ollama.dmg
-        $(xcrun -f notarytool) submit dist/Ollama.dmg --wait --timeout 10m --apple-id "$APPLE_ID" --password "$APPLE_PASSWORD" --team-id "$APPLE_TEAM_ID"
+        $(xcrun -f notarytool) submit dist/Ollama.dmg --wait --timeout 20m --apple-id "$APPLE_ID" --password "$APPLE_PASSWORD" --team-id "$APPLE_TEAM_ID"
         $(xcrun -f stapler) staple dist/Ollama.dmg
     else
         echo "WARNING: Code signing disabled, this bundle will not work for upgrade testing"

scripts/build_linux.sh

@@ -48,53 +48,12 @@ if echo $PLATFORM | grep "amd64" > /dev/null; then
         .
 fi
 
-# Deduplicate CUDA libraries across mlx_* and cuda_* directories
-deduplicate_cuda_libs() {
-    local base_dir="$1"
-    echo "Deduplicating CUDA libraries in ${base_dir}..."
-
-    # Find all mlx_cuda_* directories
-    for mlx_dir in "${base_dir}"/lib/ollama/mlx_cuda_*; do
-        [ -d "${mlx_dir}" ] || continue
-
-        # Extract CUDA version (e.g., v12, v13)
-        cuda_version=$(basename "${mlx_dir}" | sed 's/mlx_cuda_//')
-        cuda_dir="${base_dir}/lib/ollama/cuda_${cuda_version}"
-
-        # Skip if corresponding cuda_* directory doesn't exist
-        [ -d "${cuda_dir}" ] || continue
-
-        echo "  Checking ${mlx_dir} against ${cuda_dir}..."
-
-        # Find all .so* files in mlx directory
-        find "${mlx_dir}" -type f -name "*.so*" | while read mlx_file; do
-            filename=$(basename "${mlx_file}")
-            cuda_file="${cuda_dir}/${filename}"
-
-            # Skip if file doesn't exist in cuda directory
-            [ -f "${cuda_file}" ] || continue
-
-            # Compare checksums
-            mlx_sum=$(sha256sum "${mlx_file}" | awk '{print $1}')
-            cuda_sum=$(sha256sum "${cuda_file}" | awk '{print $1}')
-
-            if [ "${mlx_sum}" = "${cuda_sum}" ]; then
-                echo "    Deduplicating ${filename}"
-                # Calculate relative path from mlx_dir to cuda_dir
-                rel_path="../cuda_${cuda_version}/${filename}"
-                rm -f "${mlx_file}"
-                ln -s "${rel_path}" "${mlx_file}"
-            fi
-        done
-    done
-}
-
 # Run deduplication for each platform output directory
 if echo $PLATFORM | grep "," > /dev/null ; then
-    deduplicate_cuda_libs "./dist/linux_amd64"
-    deduplicate_cuda_libs "./dist/linux_arm64"
+    $(dirname $0)/deduplicate_cuda_libs.sh "./dist/linux_amd64"
+    $(dirname $0)/deduplicate_cuda_libs.sh "./dist/linux_arm64"
 elif echo $PLATFORM | grep "amd64\|arm64" > /dev/null ; then
-    deduplicate_cuda_libs "./dist"
+    $(dirname $0)/deduplicate_cuda_libs.sh "./dist"
 fi
 
 # buildx behavior changes for single vs. multiplatform

scripts/deduplicate_cuda_libs.sh

@@ -0,0 +1,60 @@
+#!/bin/sh
+#
+# Deduplicate CUDA libraries across mlx_* and cuda_* directories
+# This script finds identical .so* files in mlx_cuda_* directories that exist
+# in corresponding cuda_* directories and replaces them with symlinks.
+#
+
+set -eu
+
+if [ $# -eq 0 ]; then
+    echo "ERROR: No directory specified" >&2
+    echo "Usage: $0 <base_directory>" >&2
+    exit 1
+fi
+
+base_dir="$1"
+
+if [ ! -d "${base_dir}" ]; then
+    echo "ERROR: Directory ${base_dir} does not exist" >&2
+    exit 1
+fi
+
+echo "Deduplicating CUDA libraries in ${base_dir}..."
+
+# Find all mlx_cuda_* directories
+for mlx_dir in "${base_dir}"/lib/ollama/mlx_cuda_*; do
+    [ -d "${mlx_dir}" ] || continue
+
+    # Extract CUDA version (e.g., v12, v13)
+    cuda_version=$(basename "${mlx_dir}" | sed 's/mlx_cuda_//')
+    cuda_dir="${base_dir}/lib/ollama/cuda_${cuda_version}"
+
+    # Skip if corresponding cuda_* directory doesn't exist
+    [ -d "${cuda_dir}" ] || continue
+
+    echo "  Checking ${mlx_dir} against ${cuda_dir}..."
+
+    # Find all .so* files in mlx directory
+    find "${mlx_dir}" -type f -name "*.so*" | while read mlx_file; do
+        filename=$(basename "${mlx_file}")
+        cuda_file="${cuda_dir}/${filename}"
+
+        # Skip if file doesn't exist in cuda directory
+        [ -f "${cuda_file}" ] || continue
+
+        # Compare checksums
+        mlx_sum=$(sha256sum "${mlx_file}" | awk '{print $1}')
+        cuda_sum=$(sha256sum "${cuda_file}" | awk '{print $1}')
+
+        if [ "${mlx_sum}" = "${cuda_sum}" ]; then
+            echo "    Deduplicating ${filename}"
+            # Calculate relative path from mlx_dir to cuda_dir
+            rel_path="../cuda_${cuda_version}/${filename}"
+            rm -f "${mlx_file}"
+            ln -s "${rel_path}" "${mlx_file}"
+        fi
+    done
+done
+
+echo "Deduplication complete"

server/auth.go

@@ -50,12 +50,17 @@ func (r registryChallenge) URL() (*url.URL, error) {
 	return redirectURL, nil
 }
 
-func getAuthorizationToken(ctx context.Context, challenge registryChallenge) (string, error) {
+func getAuthorizationToken(ctx context.Context, challenge registryChallenge, originalHost string) (string, error) {
 	redirectURL, err := challenge.URL()
 	if err != nil {
 		return "", err
 	}
 
+	// Validate that the realm host matches the original request host to prevent sending tokens cross-origin.
+	if redirectURL.Host != originalHost {
+		return "", fmt.Errorf("realm host %q does not match original host %q", redirectURL.Host, originalHost)
+	}
+
 	sha256sum := sha256.Sum256(nil)
 	data := []byte(fmt.Sprintf("%s,%s,%s", http.MethodGet, redirectURL.String(), base64.StdEncoding.EncodeToString([]byte(hex.EncodeToString(sha256sum[:])))))
 

server/auth_test.go

@@ -0,0 +1,113 @@
+package server
+
+import (
+	"context"
+	"strings"
+	"testing"
+	"time"
+)
+
+func TestGetAuthorizationTokenRejectsCrossDomain(t *testing.T) {
+	tests := []struct {
+		realm        string
+		originalHost string
+		wantMismatch bool
+	}{
+		{"https://example.com/token", "example.com", false},
+		{"https://example.com/token", "other.com", true},
+		{"https://example.com/token", "localhost:8000", true},
+		{"https://localhost:5000/token", "localhost:5000", false},
+		{"https://localhost:5000/token", "localhost:6000", true},
+	}
+
+	for _, tt := range tests {
+		t.Run(tt.originalHost, func(t *testing.T) {
+			ctx, cancel := context.WithTimeout(context.Background(), 100*time.Millisecond)
+			defer cancel()
+
+			challenge := registryChallenge{Realm: tt.realm, Service: "test", Scope: "repo:x:pull"}
+			_, err := getAuthorizationToken(ctx, challenge, tt.originalHost)
+
+			isMismatch := err != nil && strings.Contains(err.Error(), "does not match")
+			if tt.wantMismatch && !isMismatch {
+				t.Errorf("expected domain mismatch error, got: %v", err)
+			}
+			if !tt.wantMismatch && isMismatch {
+				t.Errorf("unexpected domain mismatch error: %v", err)
+			}
+		})
+	}
+}
+
+func TestParseRegistryChallenge(t *testing.T) {
+	tests := []struct {
+		input                             string
+		wantRealm, wantService, wantScope string
+	}{
+		{
+			`Bearer realm="https://auth.example.com/token",service="registry",scope="repo:foo:pull"`,
+			"https://auth.example.com/token", "registry", "repo:foo:pull",
+		},
+		{
+			`Bearer realm="https://r.ollama.ai/v2/token",service="ollama",scope="-"`,
+			"https://r.ollama.ai/v2/token", "ollama", "-",
+		},
+		{"", "", "", ""},
+	}
+
+	for _, tt := range tests {
+		result := parseRegistryChallenge(tt.input)
+		if result.Realm != tt.wantRealm || result.Service != tt.wantService || result.Scope != tt.wantScope {
+			t.Errorf("parseRegistryChallenge(%q) = {%q, %q, %q}, want {%q, %q, %q}",
+				tt.input, result.Realm, result.Service, result.Scope,
+				tt.wantRealm, tt.wantService, tt.wantScope)
+		}
+	}
+}
+
+func TestRegistryChallengeURL(t *testing.T) {
+	challenge := registryChallenge{
+		Realm:   "https://auth.example.com/token",
+		Service: "registry",
+		Scope:   "repo:foo:pull repo:bar:push",
+	}
+
+	u, err := challenge.URL()
+	if err != nil {
+		t.Fatalf("URL() error: %v", err)
+	}
+
+	if u.Host != "auth.example.com" {
+		t.Errorf("host = %q, want %q", u.Host, "auth.example.com")
+	}
+	if u.Path != "/token" {
+		t.Errorf("path = %q, want %q", u.Path, "/token")
+	}
+
+	q := u.Query()
+	if q.Get("service") != "registry" {
+		t.Errorf("service = %q, want %q", q.Get("service"), "registry")
+	}
+	if scopes := q["scope"]; len(scopes) != 2 {
+		t.Errorf("scope count = %d, want 2", len(scopes))
+	}
+	if q.Get("ts") == "" {
+		t.Error("missing ts")
+	}
+	if q.Get("nonce") == "" {
+		t.Error("missing nonce")
+	}
+
+	// Nonces should differ between calls
+	u2, _ := challenge.URL()
+	if q.Get("nonce") == u2.Query().Get("nonce") {
+		t.Error("nonce should be unique per call")
+	}
+}
+
+func TestRegistryChallengeURLInvalid(t *testing.T) {
+	challenge := registryChallenge{Realm: "://invalid"}
+	if _, err := challenge.URL(); err == nil {
+		t.Error("expected error for invalid URL")
+	}
+}

server/images.go

@@ -41,6 +41,7 @@ var (
 	errCapabilityVision     = errors.New("vision")
 	errCapabilityEmbedding  = errors.New("embedding")
 	errCapabilityThinking   = errors.New("thinking")
+	errCapabilityImage      = errors.New("image generation")
 	errInsecureProtocol     = errors.New("insecure protocol http")
 )
 
@@ -76,7 +77,7 @@ func (m *Model) Capabilities() []model.Capability {
 
 	// Check for image generation model via config capabilities
 	if slices.Contains(m.Config.Capabilities, "image") {
-		return []model.Capability{model.CapabilityImageGeneration}
+		return []model.Capability{model.CapabilityImage}
 	}
 
 	// Check for completion capability
@@ -159,6 +160,7 @@ func (m *Model) CheckCapabilities(want ...model.Capability) error {
 		model.CapabilityVision:     errCapabilityVision,
 		model.CapabilityEmbedding:  errCapabilityEmbedding,
 		model.CapabilityThinking:   errCapabilityThinking,
+		model.CapabilityImage:      errCapabilityImage,
 	}
 
 	for _, cap := range want {
@@ -775,7 +777,7 @@ func pullWithTransfer(ctx context.Context, mp ModelPath, layers []Layer, manifes
 			Realm:   challenge.Realm,
 			Service: challenge.Service,
 			Scope:   challenge.Scope,
-		})
+		}, base.Host)
 	}
 
 	if err := transfer.Download(ctx, transfer.DownloadOptions{
@@ -850,7 +852,7 @@ func pushWithTransfer(ctx context.Context, mp ModelPath, layers []Layer, manifes
 			Realm:   challenge.Realm,
 			Service: challenge.Service,
 			Scope:   challenge.Scope,
-		})
+		}, base.Host)
 	}
 
 	return transfer.Upload(ctx, transfer.UploadOptions{
@@ -916,7 +918,7 @@ func makeRequestWithRetry(ctx context.Context, method string, requestURL *url.UR
 
 			// Handle authentication error with one retry
 			challenge := parseRegistryChallenge(resp.Header.Get("www-authenticate"))
-			token, err := getAuthorizationToken(ctx, challenge)
+			token, err := getAuthorizationToken(ctx, challenge, requestURL.Host)
 			if err != nil {
 				return nil, err
 			}

server/images_test.go

@@ -54,7 +54,7 @@ func TestModelCapabilities(t *testing.T) {
 					Capabilities: []string{"image"},
 				},
 			},
-			expectedCaps: []model.Capability{model.CapabilityImageGeneration},
+			expectedCaps: []model.Capability{model.CapabilityImage},
 		},
 		{
 			name: "model with completion capability",
@@ -242,6 +242,24 @@ func TestModelCheckCapabilities(t *testing.T) {
 			checkCaps:      []model.Capability{"unknown"},
 			expectedErrMsg: "unknown capability",
 		},
+		{
+			name: "model missing image generation capability",
+			model: Model{
+				ModelPath: completionModelPath,
+				Template:  chatTemplate,
+			},
+			checkCaps:      []model.Capability{model.CapabilityImage},
+			expectedErrMsg: "does not support image generation",
+		},
+		{
+			name: "model with image generation capability",
+			model: Model{
+				Config: model.ConfigV2{
+					Capabilities: []string{"image"},
+				},
+			},
+			checkCaps: []model.Capability{model.CapabilityImage},
+		},
 	}
 
 	for _, tt := range tests {

server/manifest.go

@@ -47,16 +47,40 @@ func (m *Manifest) Remove() error {
 }
 
 func (m *Manifest) RemoveLayers() error {
-	for _, layer := range append(m.Layers, m.Config) {
-		if layer.Digest != "" {
-			if err := layer.Remove(); errors.Is(err, os.ErrNotExist) {
-				slog.Debug("layer does not exist", "digest", layer.Digest)
-			} else if err != nil {
-				return err
+	ms, err := Manifests(true)
+	if err != nil {
+		return err
+	}
+
+	// Build set of digests still in use by other manifests
+	inUse := make(map[string]struct{})
+	for _, other := range ms {
+		for _, layer := range append(other.Layers, other.Config) {
+			if layer.Digest != "" {
+				inUse[layer.Digest] = struct{}{}
 			}
 		}
 	}
 
+	// Remove layers not used by any other manifest
+	for _, layer := range append(m.Layers, m.Config) {
+		if layer.Digest == "" {
+			continue
+		}
+		if _, used := inUse[layer.Digest]; used {
+			continue
+		}
+		blob, err := GetBlobsPath(layer.Digest)
+		if err != nil {
+			return err
+		}
+		if err := os.Remove(blob); errors.Is(err, os.ErrNotExist) {
+			slog.Debug("layer does not exist", "digest", layer.Digest)
+		} else if err != nil {
+			return err
+		}
+	}
+
 	return nil
 }
 

server/routes.go

@@ -51,7 +51,7 @@ import (
 	"github.com/ollama/ollama/types/model"
 	"github.com/ollama/ollama/version"
 	"github.com/ollama/ollama/x/imagegen"
-	imagegenapi "github.com/ollama/ollama/x/imagegen/api"
+	xserver "github.com/ollama/ollama/x/server"
 )
 
 const signinURLStr = "https://ollama.com/connect?name=%s&key=%s"
@@ -164,29 +164,6 @@ func (s *Server) scheduleRunner(ctx context.Context, name string, caps []model.C
 	return runner.llama, model, &opts, nil
 }
 
-// ScheduleImageGenRunner schedules an image generation model runner.
-// This implements the imagegenapi.RunnerScheduler interface.
-func (s *Server) ScheduleImageGenRunner(c *gin.Context, modelName string, opts api.Options, keepAlive *api.Duration) (llm.LlamaServer, error) {
-	m := &Model{
-		Name:      modelName,
-		ShortName: modelName,
-		ModelPath: modelName, // For image gen, ModelPath is just the model name
-		Config: model.ConfigV2{
-			Capabilities: []string{"image"},
-		},
-	}
-
-	runnerCh, errCh := s.sched.GetRunner(c.Request.Context(), m, opts, keepAlive)
-	var runner *runnerRef
-	select {
-	case runner = <-runnerCh:
-	case err := <-errCh:
-		return nil, err
-	}
-
-	return runner.llama, nil
-}
-
 func signinURL() (string, error) {
 	pubKey, err := auth.GetPublicKey()
 	if err != nil {
@@ -214,12 +191,6 @@ func (s *Server) GenerateHandler(c *gin.Context) {
 		return
 	}
 
-	// Check if this is a known image generation model
-	if imagegen.ResolveModelName(req.Model) != "" {
-		imagegenapi.HandleGenerateRequest(c, s, req.Model, req.Prompt, req.KeepAlive, streamResponse)
-		return
-	}
-
 	name := model.ParseName(req.Model)
 	if !name.IsValid() {
 		// Ideally this is "invalid model name" but we're keeping with
@@ -249,6 +220,12 @@ func (s *Server) GenerateHandler(c *gin.Context) {
 		return
 	}
 
+	// Handle image generation models
+	if slices.Contains(m.Capabilities(), model.CapabilityImage) {
+		s.handleImageGenerate(c, req, name.String(), checkpointStart)
+		return
+	}
+
 	if req.TopLogprobs < 0 || req.TopLogprobs > 20 {
 		c.AbortWithStatusJSON(http.StatusBadRequest, gin.H{"error": "top_logprobs must be between 0 and 20"})
 		return
@@ -1124,6 +1101,31 @@ func GetModelInfo(req api.ShowRequest) (*api.ShowResponse, error) {
 		QuantizationLevel: m.Config.FileType,
 	}
 
+	// For image generation models, populate details from imagegen package
+	if slices.Contains(m.Capabilities(), model.CapabilityImage) {
+		if info, err := imagegen.GetModelInfo(name.String()); err == nil {
+			modelDetails.Family = info.Architecture
+			modelDetails.ParameterSize = format.HumanNumber(uint64(info.ParameterCount))
+			modelDetails.QuantizationLevel = info.Quantization
+		}
+	}
+
+	// For safetensors LLM models (experimental), populate details from config.json
+	if m.Config.ModelFormat == "safetensors" && slices.Contains(m.Config.Capabilities, "completion") {
+		if info, err := xserver.GetSafetensorsLLMInfo(name.String()); err == nil {
+			if arch, ok := info["general.architecture"].(string); ok && arch != "" {
+				modelDetails.Family = arch
+			}
+			if paramCount, ok := info["general.parameter_count"].(int64); ok && paramCount > 0 {
+				modelDetails.ParameterSize = format.HumanNumber(uint64(paramCount))
+			}
+		}
+		// Get torch_dtype directly from config.json for quantization level
+		if dtype, err := xserver.GetSafetensorsDtype(name.String()); err == nil && dtype != "" {
+			modelDetails.QuantizationLevel = dtype
+		}
+	}
+
 	if req.System != "" {
 		m.System = req.System
 	}
@@ -1206,6 +1208,30 @@ func GetModelInfo(req api.ShowRequest) (*api.ShowResponse, error) {
 		return resp, nil
 	}
 
+	if slices.Contains(m.Capabilities(), model.CapabilityImage) {
+		// Populate tensor info if verbose
+		if req.Verbose {
+			if tensors, err := xserver.GetSafetensorsTensorInfo(name.String()); err == nil {
+				resp.Tensors = tensors
+			}
+		}
+		return resp, nil
+	}
+
+	// For safetensors LLM models (experimental), populate ModelInfo from config.json
+	if m.Config.ModelFormat == "safetensors" && slices.Contains(m.Config.Capabilities, "completion") {
+		if info, err := xserver.GetSafetensorsLLMInfo(name.String()); err == nil {
+			resp.ModelInfo = info
+		}
+		// Populate tensor info if verbose
+		if req.Verbose {
+			if tensors, err := xserver.GetSafetensorsTensorInfo(name.String()); err == nil {
+				resp.Tensors = tensors
+			}
+		}
+		return resp, nil
+	}
+
 	kvData, tensors, err := getModelData(m.ModelPath, req.Verbose)
 	if err != nil {
 		return nil, err
@@ -1574,13 +1600,12 @@ func (s *Server) GenerateRoutes(rc *ollama.Registry) (http.Handler, error) {
 	r.GET("/v1/models", middleware.ListMiddleware(), s.ListHandler)
 	r.GET("/v1/models/:model", middleware.RetrieveMiddleware(), s.ShowHandler)
 	r.POST("/v1/responses", middleware.ResponsesMiddleware(), s.ChatHandler)
+	// OpenAI-compatible image generation endpoint
+	r.POST("/v1/images/generations", middleware.ImageGenerationsMiddleware(), s.GenerateHandler)
 
 	// Inference (Anthropic compatibility)
 	r.POST("/v1/messages", middleware.AnthropicMessagesMiddleware(), s.ChatHandler)
 
-	// Experimental image generation support
-	imagegenapi.RegisterRoutes(r, s)
-
 	if rc != nil {
 		// wrap old with new
 		rs := &registry.Local{
@@ -2059,8 +2084,14 @@ func (s *Server) ChatHandler(c *gin.Context) {
 		}
 	} else {
 		if req.Think != nil && req.Think.Bool() {
-			c.JSON(http.StatusBadRequest, gin.H{"error": fmt.Sprintf("%q does not support thinking", req.Model)})
-			return
+			// Set think to nil when being used with Anthropic API to connect to tools like claude code
+			if _, ok := c.Get("relax_thinking"); ok {
+				slog.Warn("model does not support thinking, relaxing thinking to nil", "model", req.Model)
+				req.Think = nil
+			} else {
+				c.JSON(http.StatusBadRequest, gin.H{"error": fmt.Sprintf("%q does not support thinking", req.Model)})
+				return
+			}
 		}
 	}
 
@@ -2441,3 +2472,91 @@ func filterThinkTags(msgs []api.Message, m *Model) []api.Message {
 	}
 	return msgs
 }
+
+// handleImageGenerate handles image generation requests within GenerateHandler.
+// This is called when the model has the Image capability.
+func (s *Server) handleImageGenerate(c *gin.Context, req api.GenerateRequest, modelName string, checkpointStart time.Time) {
+	// Validate image dimensions
+	const maxDimension int32 = 4096
+	if req.Width > maxDimension || req.Height > maxDimension {
+		c.JSON(http.StatusBadRequest, gin.H{"error": fmt.Sprintf("width and height must be <= %d", maxDimension)})
+		return
+	}
+
+	// Schedule the runner for image generation
+	runner, _, _, err := s.scheduleRunner(c.Request.Context(), modelName, []model.Capability{model.CapabilityImage}, nil, req.KeepAlive)
+	if err != nil {
+		handleScheduleError(c, req.Model, err)
+		return
+	}
+
+	checkpointLoaded := time.Now()
+
+	// Handle load-only request (empty prompt)
+	if req.Prompt == "" {
+		c.JSON(http.StatusOK, api.GenerateResponse{
+			Model:      req.Model,
+			CreatedAt:  time.Now().UTC(),
+			Done:       true,
+			DoneReason: "load",
+		})
+		return
+	}
+
+	// Set headers for streaming response
+	c.Header("Content-Type", "application/x-ndjson")
+
+	// Get seed from options if provided
+	var seed int64
+	if s, ok := req.Options["seed"]; ok {
+		switch v := s.(type) {
+		case int:
+			seed = int64(v)
+		case int64:
+			seed = v
+		case float64:
+			seed = int64(v)
+		}
+	}
+
+	var streamStarted bool
+	if err := runner.Completion(c.Request.Context(), llm.CompletionRequest{
+		Prompt: req.Prompt,
+		Width:  req.Width,
+		Height: req.Height,
+		Steps:  req.Steps,
+		Seed:   seed,
+	}, func(cr llm.CompletionResponse) {
+		streamStarted = true
+		res := api.GenerateResponse{
+			Model:     req.Model,
+			CreatedAt: time.Now().UTC(),
+			Done:      cr.Done,
+		}
+
+		if cr.TotalSteps > 0 {
+			res.Completed = int64(cr.Step)
+			res.Total = int64(cr.TotalSteps)
+		}
+
+		if cr.Image != "" {
+			res.Image = cr.Image
+		}
+
+		if cr.Done {
+			res.DoneReason = cr.DoneReason.String()
+			res.Metrics.TotalDuration = time.Since(checkpointStart)
+			res.Metrics.LoadDuration = checkpointLoaded.Sub(checkpointStart)
+		}
+
+		data, _ := json.Marshal(res)
+		c.Writer.Write(append(data, '\n'))
+		c.Writer.Flush()
+	}); err != nil {
+		// Only send JSON error if streaming hasn't started yet
+		// (once streaming starts, headers are committed and we can't change status code)
+		if !streamStarted {
+			c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
+		}
+	}
+}

server/sched.go

@@ -571,10 +571,10 @@ func (s *Scheduler) loadImageGen(req *LlmRequest) bool {
 		model:           req.model,
 		modelPath:       req.model.ModelPath,
 		llama:           server,
-		Options:         &req.opts,
 		loading:         false,
 		sessionDuration: sessionDuration,
-		refCount:        1,
+		totalSize:       server.TotalSize(),
+		vramSize:        server.VRAMSize(),
 	}
 
 	s.loadedMu.Lock()

server/sched_test.go

@@ -6,7 +6,6 @@ import (
 	"errors"
 	"log/slog"
 	"os"
-	"slices"
 	"testing"
 	"time"
 
@@ -17,7 +16,6 @@ import (
 	"github.com/ollama/ollama/fs/ggml"
 	"github.com/ollama/ollama/llm"
 	"github.com/ollama/ollama/ml"
-	"github.com/ollama/ollama/types/model"
 )
 
 func TestMain(m *testing.M) {
@@ -807,32 +805,8 @@ func (s *mockLlm) GetDeviceInfos(ctx context.Context) []ml.DeviceInfo { return n
 func (s *mockLlm) HasExited() bool                                    { return false }
 func (s *mockLlm) GetActiveDeviceIDs() []ml.DeviceID                  { return nil }
 
-// TestImageGenCapabilityDetection verifies that models with "image" capability
-// are correctly identified and routed differently from language models.
-func TestImageGenCapabilityDetection(t *testing.T) {
-	// Model with image capability should be detected
-	imageModel := &Model{
-		Config: model.ConfigV2{
-			Capabilities: []string{"image"},
-		},
-	}
-	require.True(t, slices.Contains(imageModel.Config.Capabilities, "image"))
-
-	// Model without image capability should not be detected
-	langModel := &Model{
-		Config: model.ConfigV2{
-			Capabilities: []string{"completion"},
-		},
-	}
-	require.False(t, slices.Contains(langModel.Config.Capabilities, "image"))
-
-	// Empty capabilities should not match
-	emptyModel := &Model{}
-	require.False(t, slices.Contains(emptyModel.Config.Capabilities, "image"))
-}
-
 // TestImageGenRunnerCanBeEvicted verifies that an image generation model
-// loaded in the scheduler can be evicted by a language model request.
+// loaded in the scheduler can be evicted when idle.
 func TestImageGenRunnerCanBeEvicted(t *testing.T) {
 	ctx, done := context.WithTimeout(t.Context(), 500*time.Millisecond)
 	defer done()
@@ -864,3 +838,59 @@ func TestImageGenRunnerCanBeEvicted(t *testing.T) {
 	require.NotNil(t, runner)
 	require.Equal(t, "/fake/image/model", runner.modelPath)
 }
+
+// TestImageGenSchedulerCoexistence verifies that image generation models
+// can coexist with language models in the scheduler and VRAM is tracked correctly.
+func TestImageGenSchedulerCoexistence(t *testing.T) {
+	ctx, done := context.WithTimeout(t.Context(), 500*time.Millisecond)
+	defer done()
+
+	s := InitScheduler(ctx)
+	s.getGpuFn = getGpuFn
+	s.getSystemInfoFn = getSystemInfoFn
+
+	// Load both an imagegen runner and a language model runner
+	imageGenRunner := &runnerRef{
+		model:           &Model{Name: "flux", ModelPath: "/fake/flux/model"},
+		modelPath:       "/fake/flux/model",
+		llama:           &mockLlm{vramSize: 8 * format.GigaByte, vramByGPU: map[ml.DeviceID]uint64{{Library: "Metal"}: 8 * format.GigaByte}},
+		sessionDuration: 10 * time.Millisecond,
+		numParallel:     1,
+		refCount:        0,
+	}
+
+	langModelRunner := &runnerRef{
+		model:           &Model{Name: "llama3", ModelPath: "/fake/llama3/model"},
+		modelPath:       "/fake/llama3/model",
+		llama:           &mockLlm{vramSize: 4 * format.GigaByte, vramByGPU: map[ml.DeviceID]uint64{{Library: "Metal"}: 4 * format.GigaByte}},
+		sessionDuration: 10 * time.Millisecond,
+		numParallel:     1,
+		refCount:        0,
+	}
+
+	s.loadedMu.Lock()
+	s.loaded["/fake/flux/model"] = imageGenRunner
+	s.loaded["/fake/llama3/model"] = langModelRunner
+	s.loadedMu.Unlock()
+
+	// Verify both are loaded
+	s.loadedMu.Lock()
+	require.Len(t, s.loaded, 2)
+	require.NotNil(t, s.loaded["/fake/flux/model"])
+	require.NotNil(t, s.loaded["/fake/llama3/model"])
+	s.loadedMu.Unlock()
+
+	// Verify updateFreeSpace accounts for both
+	gpus := []ml.DeviceInfo{
+		{
+			DeviceID:    ml.DeviceID{Library: "Metal"},
+			TotalMemory: 24 * format.GigaByte,
+			FreeMemory:  24 * format.GigaByte,
+		},
+	}
+	s.updateFreeSpace(gpus)
+
+	// Free memory should be reduced by both models
+	expectedFree := uint64(24*format.GigaByte) - uint64(8*format.GigaByte) - uint64(4*format.GigaByte)
+	require.Equal(t, expectedFree, gpus[0].FreeMemory)
+}

server/upload.go

@@ -279,7 +279,7 @@ func (b *blobUpload) uploadPart(ctx context.Context, method string, requestURL *
 	case resp.StatusCode == http.StatusUnauthorized:
 		w.Rollback()
 		challenge := parseRegistryChallenge(resp.Header.Get("www-authenticate"))
-		token, err := getAuthorizationToken(ctx, challenge)
+		token, err := getAuthorizationToken(ctx, challenge, requestURL.Host)
 		if err != nil {
 			return err
 		}

types/model/capability.go

@@ -9,7 +9,7 @@ const (
 	CapabilityVision          = Capability("vision")
 	CapabilityEmbedding       = Capability("embedding")
 	CapabilityThinking        = Capability("thinking")
-	CapabilityImageGeneration = Capability("image")
+	CapabilityImage = Capability("image")
 )
 
 func (c Capability) String() string {

x/README.md

@@ -1,24 +0,0 @@
-# Experimental Features 
-
-## MLX Backend
-
-We're working on a new experimental backend based on the [MLX project](https://github.com/ml-explore/mlx)
-
-Support is currently limited to MacOS and Linux with CUDA GPUs.  We're looking to add support for Windows CUDA soon, and other GPU vendors.  To build:
-
-```
-cmake --preset MLX
-cmake --build --preset MLX --parallel
-cmake --install --component MLX
-go build -tags mlx .
-```
-
-On linux, use the preset "MLX CUDA 13" or "MLX CUDA 12" to enable CUDA with the default Ollama NVIDIA GPU architectures enabled. 
-
-## Image Generation
-
-Based on the experimental MLX backend, we're working on adding imagegen support.  After running the cmake commands above:
-
-```
-go build -o imagegen ./x/imagegen/cmd/engine
-```

x/agent/approval.go

@@ -41,6 +41,7 @@ var optionLabels = []string{
 var toolDisplayNames = map[string]string{
 	"bash":       "Bash",
 	"web_search": "Web Search",
+	"web_fetch":  "Web Fetch",
 }
 
 // ToolDisplayName returns the human-readable display name for a tool.
@@ -565,6 +566,16 @@ func formatToolDisplay(toolName string, args map[string]any) string {
 		}
 	}
 
+	// For web fetch, show URL and internet notice
+	if toolName == "web_fetch" {
+		if url, ok := args["url"].(string); ok {
+			sb.WriteString(fmt.Sprintf("Tool: %s\n", displayName))
+			sb.WriteString(fmt.Sprintf("URL: %s\n", url))
+			sb.WriteString("Uses internet via ollama.com")
+			return sb.String()
+		}
+	}
+
 	// Generic display
 	sb.WriteString(fmt.Sprintf("Tool: %s", displayName))
 	if len(args) > 0 {
@@ -1017,6 +1028,16 @@ func FormatApprovalResult(toolName string, args map[string]any, result ApprovalR
 		}
 	}
 
+	if toolName == "web_fetch" {
+		if url, ok := args["url"].(string); ok {
+			// Truncate long URLs
+			if len(url) > 50 {
+				url = url[:47] + "..."
+			}
+			return fmt.Sprintf("\033[1m%s:\033[0m %s: %s", label, displayName, url)
+		}
+	}
+
 	return fmt.Sprintf("\033[1m%s:\033[0m %s", label, displayName)
 }
 

x/cmd/run.go

@@ -9,6 +9,7 @@ import (
 	"net/url"
 	"os"
 	"os/signal"
+	"slices"
 	"strings"
 	"syscall"
 	"time"
@@ -130,6 +131,7 @@ type RunOptions struct {
 	KeepAlive    *api.Duration
 	Think        *api.ThinkValue
 	HideThinking bool
+	Verbose      bool
 
 	// Agent fields (managed externally for session persistence)
 	Tools    *tools.Registry
@@ -178,6 +180,7 @@ func Chat(ctx context.Context, opts RunOptions) (*api.Message, error) {
 	var thinkTagClosed bool = false
 	var pendingToolCalls []api.ToolCall
 	var consecutiveErrors int // Track consecutive 500 errors for retry limit
+	var latest api.ChatResponse
 
 	role := "assistant"
 	messages := opts.Messages
@@ -187,6 +190,7 @@ func Chat(ctx context.Context, opts RunOptions) (*api.Message, error) {
 			p.StopAndClear()
 		}
 
+		latest = response
 		role = response.Message.Role
 		if response.Message.Thinking != "" && !opts.HideThinking {
 			if !thinkTagOpened {
@@ -483,6 +487,10 @@ func Chat(ctx context.Context, opts RunOptions) (*api.Message, error) {
 		fmt.Println()
 	}
 
+	if opts.Verbose {
+		latest.Summary()
+	}
+
 	return &api.Message{Role: role, Thinking: thinkingContent.String(), Content: fullResponse.String()}, nil
 }
 
@@ -634,12 +642,13 @@ func checkModelCapabilities(ctx context.Context, modelName string) (supportsTool
 // GenerateInteractive runs an interactive agent session.
 // This is called from cmd.go when --experimental flag is set.
 // If yoloMode is true, all tool approvals are skipped.
-func GenerateInteractive(cmd *cobra.Command, modelName string, wordWrap bool, options map[string]any, think *api.ThinkValue, hideThinking bool, keepAlive *api.Duration, yoloMode bool) error {
+// If enableWebsearch is true, the web search tool is registered.
+func GenerateInteractive(cmd *cobra.Command, modelName string, wordWrap bool, options map[string]any, think *api.ThinkValue, hideThinking bool, keepAlive *api.Duration, yoloMode bool, enableWebsearch bool) error {
 	scanner, err := readline.New(readline.Prompt{
 		Prompt:         ">>> ",
 		AltPrompt:      "... ",
 		Placeholder:    "Send a message (/? for help)",
-		AltPlaceholder: `Use """ to end multi-line input`,
+		AltPlaceholder: "Press Enter to send",
 	})
 	if err != nil {
 		return err
@@ -660,6 +669,12 @@ func GenerateInteractive(cmd *cobra.Command, modelName string, wordWrap bool, op
 	if supportsTools {
 		toolRegistry = tools.DefaultRegistry()
 
+		// Register web search and web fetch tools if enabled via flag
+		if enableWebsearch {
+			toolRegistry.RegisterWebSearch()
+			toolRegistry.RegisterWebFetch()
+		}
+
 		if toolRegistry.Has("bash") {
 			fmt.Fprintln(os.Stderr)
 			fmt.Fprintln(os.Stderr, "This experimental version of Ollama has the \033[1mbash\033[0m tool enabled.")
@@ -667,6 +682,11 @@ func GenerateInteractive(cmd *cobra.Command, modelName string, wordWrap bool, op
 			fmt.Fprintln(os.Stderr)
 		}
 
+		if toolRegistry.Has("web_search") || toolRegistry.Has("web_fetch") {
+			fmt.Fprintln(os.Stderr, "The \033[1mWeb Search\033[0m and \033[1mWeb Fetch\033[0m tools are enabled. Models can search and fetch web content via ollama.com.")
+			fmt.Fprintln(os.Stderr)
+		}
+
 		if yoloMode {
 			fmt.Fprintf(os.Stderr, "\033[1mwarning:\033[0m yolo mode - all tool approvals will be skipped\n")
 		}
@@ -677,6 +697,8 @@ func GenerateInteractive(cmd *cobra.Command, modelName string, wordWrap bool, op
 
 	var messages []api.Message
 	var sb strings.Builder
+	var format string
+	var system string
 
 	for {
 		line, err := scanner.Readline()
@@ -688,6 +710,7 @@ func GenerateInteractive(cmd *cobra.Command, modelName string, wordWrap bool, op
 			if line == "" {
 				fmt.Println("\nUse Ctrl + d or /bye to exit.")
 			}
+			scanner.Prompt.UseAlt = false
 			sb.Reset()
 			continue
 		case err != nil:
@@ -707,6 +730,10 @@ func GenerateInteractive(cmd *cobra.Command, modelName string, wordWrap bool, op
 			continue
 		case strings.HasPrefix(line, "/help"), strings.HasPrefix(line, "/?"):
 			fmt.Fprintln(os.Stderr, "Available Commands:")
+			fmt.Fprintln(os.Stderr, "  /set            Set session variables")
+			fmt.Fprintln(os.Stderr, "  /show           Show model information")
+			fmt.Fprintln(os.Stderr, "  /load           Load a different model")
+			fmt.Fprintln(os.Stderr, "  /save           Save session as a model")
 			fmt.Fprintln(os.Stderr, "  /tools          Show available tools and approvals")
 			fmt.Fprintln(os.Stderr, "  /clear          Clear session context and approvals")
 			fmt.Fprintln(os.Stderr, "  /bye            Exit")
@@ -716,6 +743,280 @@ func GenerateInteractive(cmd *cobra.Command, modelName string, wordWrap bool, op
 			fmt.Fprintln(os.Stderr, "  Ctrl+O          Expand last tool output")
 			fmt.Fprintln(os.Stderr, "")
 			continue
+		case strings.HasPrefix(line, "/set"):
+			args := strings.Fields(line)
+			if len(args) > 1 {
+				switch args[1] {
+				case "history":
+					scanner.HistoryEnable()
+				case "nohistory":
+					scanner.HistoryDisable()
+				case "wordwrap":
+					wordWrap = true
+					fmt.Println("Set 'wordwrap' mode.")
+				case "nowordwrap":
+					wordWrap = false
+					fmt.Println("Set 'nowordwrap' mode.")
+				case "verbose":
+					if err := cmd.Flags().Set("verbose", "true"); err != nil {
+						return err
+					}
+					fmt.Println("Set 'verbose' mode.")
+				case "quiet":
+					if err := cmd.Flags().Set("verbose", "false"); err != nil {
+						return err
+					}
+					fmt.Println("Set 'quiet' mode.")
+				case "think":
+					thinkValue := api.ThinkValue{Value: true}
+					var maybeLevel string
+					if len(args) > 2 {
+						maybeLevel = args[2]
+					}
+					if maybeLevel != "" {
+						thinkValue.Value = maybeLevel
+					}
+					think = &thinkValue
+					// Check if model supports thinking
+					if client, err := api.ClientFromEnvironment(); err == nil {
+						if resp, err := client.Show(cmd.Context(), &api.ShowRequest{Model: modelName}); err == nil {
+							if !slices.Contains(resp.Capabilities, model.CapabilityThinking) {
+								fmt.Fprintf(os.Stderr, "warning: model %q does not support thinking output\n", modelName)
+							}
+						}
+					}
+					if maybeLevel != "" {
+						fmt.Printf("Set 'think' mode to '%s'.\n", maybeLevel)
+					} else {
+						fmt.Println("Set 'think' mode.")
+					}
+				case "nothink":
+					think = &api.ThinkValue{Value: false}
+					// Check if model supports thinking
+					if client, err := api.ClientFromEnvironment(); err == nil {
+						if resp, err := client.Show(cmd.Context(), &api.ShowRequest{Model: modelName}); err == nil {
+							if !slices.Contains(resp.Capabilities, model.CapabilityThinking) {
+								fmt.Fprintf(os.Stderr, "warning: model %q does not support thinking output\n", modelName)
+							}
+						}
+					}
+					fmt.Println("Set 'nothink' mode.")
+				case "format":
+					if len(args) < 3 || args[2] != "json" {
+						fmt.Println("Invalid or missing format. For 'json' mode use '/set format json'")
+					} else {
+						format = args[2]
+						fmt.Printf("Set format to '%s' mode.\n", args[2])
+					}
+				case "noformat":
+					format = ""
+					fmt.Println("Disabled format.")
+				case "parameter":
+					if len(args) < 4 {
+						fmt.Println("Usage: /set parameter <name> <value>")
+						continue
+					}
+					params := args[3:]
+					fp, err := api.FormatParams(map[string][]string{args[2]: params})
+					if err != nil {
+						fmt.Printf("Couldn't set parameter: %q\n", err)
+						continue
+					}
+					fmt.Printf("Set parameter '%s' to '%s'\n", args[2], strings.Join(params, ", "))
+					options[args[2]] = fp[args[2]]
+				case "system":
+					if len(args) < 3 {
+						fmt.Println("Usage: /set system <message>")
+						continue
+					}
+
+					system = strings.Join(args[2:], " ")
+					newMessage := api.Message{Role: "system", Content: system}
+					if len(messages) > 0 && messages[len(messages)-1].Role == "system" {
+						messages[len(messages)-1] = newMessage
+					} else {
+						messages = append(messages, newMessage)
+					}
+					fmt.Println("Set system message.")
+					continue
+				default:
+					fmt.Printf("Unknown command '/set %s'. Type /? for help\n", args[1])
+				}
+			} else {
+				fmt.Println("Usage: /set <parameter|system|history|format|wordwrap|think|verbose> [value]")
+			}
+			continue
+		case strings.HasPrefix(line, "/show"):
+			args := strings.Fields(line)
+			if len(args) > 1 {
+				client, err := api.ClientFromEnvironment()
+				if err != nil {
+					fmt.Println("error: couldn't connect to ollama server")
+					continue
+				}
+				req := &api.ShowRequest{
+					Name:    modelName,
+					Options: options,
+				}
+				resp, err := client.Show(cmd.Context(), req)
+				if err != nil {
+					fmt.Println("error: couldn't get model")
+					continue
+				}
+
+				switch args[1] {
+				case "info":
+					fmt.Fprintf(os.Stderr, "  Model\n")
+					fmt.Fprintf(os.Stderr, "    %-16s %s\n", "Name", modelName)
+					if resp.Details.Family != "" {
+						fmt.Fprintf(os.Stderr, "    %-16s %s\n", "Family", resp.Details.Family)
+					}
+					if resp.Details.ParameterSize != "" {
+						fmt.Fprintf(os.Stderr, "    %-16s %s\n", "Parameter Size", resp.Details.ParameterSize)
+					}
+					if resp.Details.QuantizationLevel != "" {
+						fmt.Fprintf(os.Stderr, "    %-16s %s\n", "Quantization", resp.Details.QuantizationLevel)
+					}
+					if len(resp.Capabilities) > 0 {
+						caps := make([]string, len(resp.Capabilities))
+						for i, c := range resp.Capabilities {
+							caps[i] = string(c)
+						}
+						fmt.Fprintf(os.Stderr, "    %-16s %s\n", "Capabilities", strings.Join(caps, ", "))
+					}
+					fmt.Fprintln(os.Stderr)
+				case "license":
+					if resp.License == "" {
+						fmt.Println("No license was specified for this model.")
+					} else {
+						fmt.Println(resp.License)
+					}
+				case "modelfile":
+					fmt.Println(resp.Modelfile)
+				case "parameters":
+					fmt.Println("Model defined parameters:")
+					if resp.Parameters == "" {
+						fmt.Println("  No additional parameters were specified.")
+					} else {
+						for _, l := range strings.Split(resp.Parameters, "\n") {
+							fmt.Printf("  %s\n", l)
+						}
+					}
+					if len(options) > 0 {
+						fmt.Println("\nUser defined parameters:")
+						for k, v := range options {
+							fmt.Printf("  %-30s %v\n", k, v)
+						}
+					}
+				case "system":
+					switch {
+					case system != "":
+						fmt.Println(system + "\n")
+					case resp.System != "":
+						fmt.Println(resp.System + "\n")
+					default:
+						fmt.Println("No system message was specified for this model.")
+					}
+				case "template":
+					if resp.Template != "" {
+						fmt.Println(resp.Template)
+					} else {
+						fmt.Println("No prompt template was specified for this model.")
+					}
+				default:
+					fmt.Printf("Unknown command '/show %s'. Type /? for help\n", args[1])
+				}
+			} else {
+				fmt.Println("Usage: /show <info|license|modelfile|parameters|system|template>")
+			}
+			continue
+		case strings.HasPrefix(line, "/load"):
+			args := strings.Fields(line)
+			if len(args) != 2 {
+				fmt.Println("Usage: /load <modelname>")
+				continue
+			}
+			newModelName := args[1]
+			fmt.Printf("Loading model '%s'\n", newModelName)
+
+			// Create progress spinner
+			p := progress.NewProgress(os.Stderr)
+			spinner := progress.NewSpinner("")
+			p.Add("", spinner)
+
+			// Get client
+			client, err := api.ClientFromEnvironment()
+			if err != nil {
+				p.StopAndClear()
+				fmt.Println("error: couldn't connect to ollama server")
+				continue
+			}
+
+			// Check if model exists and get its info
+			info, err := client.Show(cmd.Context(), &api.ShowRequest{Model: newModelName})
+			if err != nil {
+				p.StopAndClear()
+				if strings.Contains(err.Error(), "not found") {
+					fmt.Printf("Couldn't find model '%s'\n", newModelName)
+				} else {
+					fmt.Printf("error: %v\n", err)
+				}
+				continue
+			}
+
+			// For cloud models, no need to preload
+			if info.RemoteHost == "" {
+				// Preload the model by sending an empty generate request
+				req := &api.GenerateRequest{
+					Model: newModelName,
+					Think: think,
+				}
+				err = client.Generate(cmd.Context(), req, func(r api.GenerateResponse) error {
+					return nil
+				})
+				if err != nil {
+					p.StopAndClear()
+					if strings.Contains(err.Error(), "not found") {
+						fmt.Printf("Couldn't find model '%s'\n", newModelName)
+					} else if strings.Contains(err.Error(), "does not support thinking") {
+						fmt.Printf("error: %v\n", err)
+					} else {
+						fmt.Printf("error loading model: %v\n", err)
+					}
+					continue
+				}
+			}
+
+			p.StopAndClear()
+			modelName = newModelName
+			messages = []api.Message{}
+			approval.Reset()
+			continue
+		case strings.HasPrefix(line, "/save"):
+			args := strings.Fields(line)
+			if len(args) != 2 {
+				fmt.Println("Usage: /save <modelname>")
+				continue
+			}
+			client, err := api.ClientFromEnvironment()
+			if err != nil {
+				fmt.Println("error: couldn't connect to ollama server")
+				continue
+			}
+			req := &api.CreateRequest{
+				Model:      args[1],
+				From:       modelName,
+				Parameters: options,
+				Messages:   messages,
+			}
+			fn := func(resp api.ProgressResponse) error { return nil }
+			err = client.Create(cmd.Context(), req, fn)
+			if err != nil {
+				fmt.Printf("error: %v\n", err)
+				continue
+			}
+			fmt.Printf("Created new model '%s'\n", args[1])
+			continue
 		case strings.HasPrefix(line, "/"):
 			fmt.Printf("Unknown command '%s'. Type /? for help\n", strings.Fields(line)[0])
 			continue
@@ -727,10 +1028,12 @@ func GenerateInteractive(cmd *cobra.Command, modelName string, wordWrap bool, op
 			newMessage := api.Message{Role: "user", Content: sb.String()}
 			messages = append(messages, newMessage)
 
+			verbose, _ := cmd.Flags().GetBool("verbose")
 			opts := RunOptions{
 				Model:        modelName,
 				Messages:     messages,
 				WordWrap:     wordWrap,
+				Format:       format,
 				Options:      options,
 				Think:        think,
 				HideThinking: hideThinking,
@@ -738,6 +1041,7 @@ func GenerateInteractive(cmd *cobra.Command, modelName string, wordWrap bool, op
 				Tools:        toolRegistry,
 				Approval:     approval,
 				YoloMode:     yoloMode,
+				Verbose:      verbose,
 			}
 
 			assistant, err := Chat(cmd.Context(), opts)

x/create/client/create.go

@@ -0,0 +1,282 @@
+// Package client provides client-side model creation for safetensors-based models.
+//
+// This package is in x/ because the safetensors model storage format is under development.
+// It also exists to break an import cycle: server imports x/create, so x/create
+// cannot import server. This sub-package can import server because server doesn't
+// import it.
+package client
+
+import (
+	"bytes"
+	"encoding/json"
+	"fmt"
+	"io"
+
+	"github.com/ollama/ollama/progress"
+	"github.com/ollama/ollama/server"
+	"github.com/ollama/ollama/types/model"
+	"github.com/ollama/ollama/x/create"
+)
+
+// MinOllamaVersion is the minimum Ollama version required for safetensors models.
+const MinOllamaVersion = "0.14.0"
+
+// ModelfileConfig holds configuration extracted from a Modelfile.
+type ModelfileConfig struct {
+	Template string
+	System   string
+	License  string
+}
+
+// CreateOptions holds all options for model creation.
+type CreateOptions struct {
+	ModelName string
+	ModelDir  string
+	Quantize  string           // "fp8" for quantization
+	Modelfile *ModelfileConfig // template/system/license from Modelfile
+}
+
+// CreateModel imports a model from a local directory.
+// This creates blobs and manifest directly on disk, bypassing the HTTP API.
+// Automatically detects model type (safetensors LLM vs image gen) and routes accordingly.
+func CreateModel(opts CreateOptions, p *progress.Progress) error {
+	// Detect model type
+	isSafetensors := create.IsSafetensorsModelDir(opts.ModelDir)
+	isImageGen := create.IsTensorModelDir(opts.ModelDir)
+
+	if !isSafetensors && !isImageGen {
+		return fmt.Errorf("%s is not a supported model directory (needs config.json + *.safetensors or model_index.json)", opts.ModelDir)
+	}
+
+	// Determine model type settings
+	var modelType, spinnerKey string
+	var capabilities []string
+	if isSafetensors {
+		modelType = "safetensors model"
+		spinnerKey = "create"
+		capabilities = []string{"completion"}
+	} else {
+		modelType = "image generation model"
+		spinnerKey = "imagegen"
+		capabilities = []string{"image"}
+	}
+
+	// Set up progress spinner
+	statusMsg := "importing " + modelType
+	spinner := progress.NewSpinner(statusMsg)
+	p.Add(spinnerKey, spinner)
+
+	progressFn := func(msg string) {
+		spinner.Stop()
+		statusMsg = msg
+		spinner = progress.NewSpinner(statusMsg)
+		p.Add(spinnerKey, spinner)
+	}
+
+	// Create the model using shared callbacks
+	var err error
+	if isSafetensors {
+		err = create.CreateSafetensorsModel(
+			opts.ModelName, opts.ModelDir, opts.Quantize,
+			newLayerCreator(), newTensorLayerCreator(),
+			newManifestWriter(opts, capabilities),
+			progressFn,
+		)
+	} else {
+		err = create.CreateImageGenModel(
+			opts.ModelName, opts.ModelDir, opts.Quantize,
+			newLayerCreator(), newTensorLayerCreator(),
+			newManifestWriter(opts, capabilities),
+			progressFn,
+		)
+	}
+
+	spinner.Stop()
+	if err != nil {
+		return err
+	}
+
+	fmt.Printf("Created %s '%s'\n", modelType, opts.ModelName)
+	return nil
+}
+
+// newLayerCreator returns a LayerCreator callback for creating config/JSON layers.
+func newLayerCreator() create.LayerCreator {
+	return func(r io.Reader, mediaType, name string) (create.LayerInfo, error) {
+		layer, err := server.NewLayer(r, mediaType)
+		if err != nil {
+			return create.LayerInfo{}, err
+		}
+
+		return create.LayerInfo{
+			Digest:    layer.Digest,
+			Size:      layer.Size,
+			MediaType: layer.MediaType,
+			Name:      name,
+		}, nil
+	}
+}
+
+// newTensorLayerCreator returns a QuantizingTensorLayerCreator callback for creating tensor layers.
+// When quantize is non-empty, returns multiple layers (weight + scales + optional qbias).
+func newTensorLayerCreator() create.QuantizingTensorLayerCreator {
+	return func(r io.Reader, name, dtype string, shape []int32, quantize string) ([]create.LayerInfo, error) {
+		if quantize != "" {
+			return createQuantizedLayers(r, name, dtype, shape, quantize)
+		}
+		return createUnquantizedLayer(r, name)
+	}
+}
+
+// createQuantizedLayers quantizes a tensor and returns the resulting layers.
+func createQuantizedLayers(r io.Reader, name, dtype string, shape []int32, quantize string) ([]create.LayerInfo, error) {
+	if !QuantizeSupported() {
+		return nil, fmt.Errorf("quantization requires MLX support")
+	}
+
+	// Quantize the tensor
+	qweightData, scalesData, qbiasData, _, _, _, err := quantizeTensor(r, name, dtype, shape, quantize)
+	if err != nil {
+		return nil, fmt.Errorf("failed to quantize %s: %w", name, err)
+	}
+
+	// Create layer for quantized weight
+	weightLayer, err := server.NewLayer(bytes.NewReader(qweightData), server.MediaTypeImageTensor)
+	if err != nil {
+		return nil, err
+	}
+
+	// Create layer for scales
+	scalesLayer, err := server.NewLayer(bytes.NewReader(scalesData), server.MediaTypeImageTensor)
+	if err != nil {
+		return nil, err
+	}
+
+	layers := []create.LayerInfo{
+		{
+			Digest:    weightLayer.Digest,
+			Size:      weightLayer.Size,
+			MediaType: weightLayer.MediaType,
+			Name:      name,
+		},
+		{
+			Digest:    scalesLayer.Digest,
+			Size:      scalesLayer.Size,
+			MediaType: scalesLayer.MediaType,
+			Name:      name + "_scale",
+		},
+	}
+
+	// Add qbiases layer if present (affine mode)
+	if qbiasData != nil {
+		qbiasLayer, err := server.NewLayer(bytes.NewReader(qbiasData), server.MediaTypeImageTensor)
+		if err != nil {
+			return nil, err
+		}
+		layers = append(layers, create.LayerInfo{
+			Digest:    qbiasLayer.Digest,
+			Size:      qbiasLayer.Size,
+			MediaType: qbiasLayer.MediaType,
+			Name:      name + "_qbias",
+		})
+	}
+
+	return layers, nil
+}
+
+// createUnquantizedLayer creates a single tensor layer without quantization.
+func createUnquantizedLayer(r io.Reader, name string) ([]create.LayerInfo, error) {
+	layer, err := server.NewLayer(r, server.MediaTypeImageTensor)
+	if err != nil {
+		return nil, err
+	}
+
+	return []create.LayerInfo{
+		{
+			Digest:    layer.Digest,
+			Size:      layer.Size,
+			MediaType: layer.MediaType,
+			Name:      name,
+		},
+	}, nil
+}
+
+// newManifestWriter returns a ManifestWriter callback for writing the model manifest.
+func newManifestWriter(opts CreateOptions, capabilities []string) create.ManifestWriter {
+	return func(modelName string, config create.LayerInfo, layers []create.LayerInfo) error {
+		name := model.ParseName(modelName)
+		if !name.IsValid() {
+			return fmt.Errorf("invalid model name: %s", modelName)
+		}
+
+		// Create config blob with version requirement
+		configData := model.ConfigV2{
+			ModelFormat:  "safetensors",
+			Capabilities: capabilities,
+			Requires:     MinOllamaVersion,
+		}
+		configJSON, err := json.Marshal(configData)
+		if err != nil {
+			return fmt.Errorf("failed to marshal config: %w", err)
+		}
+
+		// Create config layer blob
+		configLayer, err := server.NewLayer(bytes.NewReader(configJSON), "application/vnd.docker.container.image.v1+json")
+		if err != nil {
+			return fmt.Errorf("failed to create config layer: %w", err)
+		}
+
+		// Convert LayerInfo to server.Layer
+		serverLayers := make([]server.Layer, 0, len(layers))
+		for _, l := range layers {
+			serverLayers = append(serverLayers, server.Layer{
+				MediaType: l.MediaType,
+				Digest:    l.Digest,
+				Size:      l.Size,
+				Name:      l.Name,
+			})
+		}
+
+		// Add Modelfile layers if present
+		if opts.Modelfile != nil {
+			modelfileLayers, err := createModelfileLayers(opts.Modelfile)
+			if err != nil {
+				return err
+			}
+			serverLayers = append(serverLayers, modelfileLayers...)
+		}
+
+		return server.WriteManifest(name, configLayer, serverLayers)
+	}
+}
+
+// createModelfileLayers creates layers for template, system, and license from Modelfile config.
+func createModelfileLayers(mf *ModelfileConfig) ([]server.Layer, error) {
+	var layers []server.Layer
+
+	if mf.Template != "" {
+		layer, err := server.NewLayer(bytes.NewReader([]byte(mf.Template)), "application/vnd.ollama.image.template")
+		if err != nil {
+			return nil, fmt.Errorf("failed to create template layer: %w", err)
+		}
+		layers = append(layers, layer)
+	}
+
+	if mf.System != "" {
+		layer, err := server.NewLayer(bytes.NewReader([]byte(mf.System)), "application/vnd.ollama.image.system")
+		if err != nil {
+			return nil, fmt.Errorf("failed to create system layer: %w", err)
+		}
+		layers = append(layers, layer)
+	}
+
+	if mf.License != "" {
+		layer, err := server.NewLayer(bytes.NewReader([]byte(mf.License)), "application/vnd.ollama.image.license")
+		if err != nil {
+			return nil, fmt.Errorf("failed to create license layer: %w", err)
+		}
+		layers = append(layers, layer)
+	}
+
+	return layers, nil
+}

x/create/client/create_test.go

@@ -0,0 +1,146 @@
+package client
+
+import (
+	"testing"
+)
+
+func TestModelfileConfig(t *testing.T) {
+	// Test that ModelfileConfig struct works as expected
+	config := &ModelfileConfig{
+		Template: "{{ .Prompt }}",
+		System:   "You are a helpful assistant.",
+		License:  "MIT",
+	}
+
+	if config.Template != "{{ .Prompt }}" {
+		t.Errorf("Template = %q, want %q", config.Template, "{{ .Prompt }}")
+	}
+	if config.System != "You are a helpful assistant." {
+		t.Errorf("System = %q, want %q", config.System, "You are a helpful assistant.")
+	}
+	if config.License != "MIT" {
+		t.Errorf("License = %q, want %q", config.License, "MIT")
+	}
+}
+
+func TestModelfileConfig_Empty(t *testing.T) {
+	config := &ModelfileConfig{}
+
+	if config.Template != "" {
+		t.Errorf("Template should be empty, got %q", config.Template)
+	}
+	if config.System != "" {
+		t.Errorf("System should be empty, got %q", config.System)
+	}
+	if config.License != "" {
+		t.Errorf("License should be empty, got %q", config.License)
+	}
+}
+
+func TestModelfileConfig_PartialFields(t *testing.T) {
+	// Test config with only some fields set
+	config := &ModelfileConfig{
+		Template: "{{ .Prompt }}",
+		// System and License intentionally empty
+	}
+
+	if config.Template == "" {
+		t.Error("Template should not be empty")
+	}
+	if config.System != "" {
+		t.Error("System should be empty")
+	}
+	if config.License != "" {
+		t.Error("License should be empty")
+	}
+}
+
+func TestMinOllamaVersion(t *testing.T) {
+	// Verify the minimum version constant is set
+	if MinOllamaVersion == "" {
+		t.Error("MinOllamaVersion should not be empty")
+	}
+	if MinOllamaVersion != "0.14.0" {
+		t.Errorf("MinOllamaVersion = %q, want %q", MinOllamaVersion, "0.14.0")
+	}
+}
+
+func TestCreateModel_InvalidDir(t *testing.T) {
+	// Test that CreateModel returns error for invalid directory
+	err := CreateModel(CreateOptions{
+		ModelName: "test-model",
+		ModelDir:  "/nonexistent/path",
+	}, nil)
+	if err == nil {
+		t.Error("expected error for nonexistent directory, got nil")
+	}
+}
+
+func TestCreateModel_NotSafetensorsDir(t *testing.T) {
+	// Test that CreateModel returns error for directory without safetensors
+	dir := t.TempDir()
+
+	err := CreateModel(CreateOptions{
+		ModelName: "test-model",
+		ModelDir:  dir,
+	}, nil)
+	if err == nil {
+		t.Error("expected error for empty directory, got nil")
+	}
+}
+
+func TestCreateOptions(t *testing.T) {
+	opts := CreateOptions{
+		ModelName: "my-model",
+		ModelDir:  "/path/to/model",
+		Quantize:  "fp8",
+		Modelfile: &ModelfileConfig{
+			Template: "test",
+			System:   "system",
+			License:  "MIT",
+		},
+	}
+
+	if opts.ModelName != "my-model" {
+		t.Errorf("ModelName = %q, want %q", opts.ModelName, "my-model")
+	}
+	if opts.ModelDir != "/path/to/model" {
+		t.Errorf("ModelDir = %q, want %q", opts.ModelDir, "/path/to/model")
+	}
+	if opts.Quantize != "fp8" {
+		t.Errorf("Quantize = %q, want %q", opts.Quantize, "fp8")
+	}
+	if opts.Modelfile == nil {
+		t.Error("Modelfile should not be nil")
+	}
+	if opts.Modelfile.Template != "test" {
+		t.Errorf("Modelfile.Template = %q, want %q", opts.Modelfile.Template, "test")
+	}
+}
+
+func TestCreateOptions_Defaults(t *testing.T) {
+	opts := CreateOptions{
+		ModelName: "test",
+		ModelDir:  "/tmp",
+	}
+
+	// Quantize should default to empty
+	if opts.Quantize != "" {
+		t.Errorf("Quantize should be empty by default, got %q", opts.Quantize)
+	}
+
+	// Modelfile should default to nil
+	if opts.Modelfile != nil {
+		t.Error("Modelfile should be nil by default")
+	}
+}
+
+func TestQuantizeSupported(t *testing.T) {
+	// This just verifies the function exists and returns a boolean
+	// The actual value depends on build tags (mlx vs non-mlx)
+	supported := QuantizeSupported()
+
+	// In non-mlx builds, this should be false
+	// We can't easily test both cases, so just verify it returns something
+	_ = supported
+}

x/create/client/quantize.go

@@ -0,0 +1,132 @@
+//go:build mlx
+
+package client
+
+import (
+	"fmt"
+	"io"
+	"os"
+	"path/filepath"
+
+	"github.com/ollama/ollama/x/imagegen/mlx"
+)
+
+// quantizeTensor loads a tensor from safetensors format, quantizes it,
+// and returns safetensors data for the quantized weights, scales, and biases.
+// Supported quantization types: "fp8" (affine 8-bit)
+// Uses MLX's native SaveSafetensors to ensure correct dtype handling (especially uint32 for quantized weights).
+func quantizeTensor(r io.Reader, name, dtype string, shape []int32, quantize string) (qweightData, scalesData, qbiasData []byte, qweightShape, scalesShape, qbiasShape []int32, err error) {
+	// Lazy init MLX when needed for quantization
+	if err := mlx.InitMLX(); err != nil {
+		return nil, nil, nil, nil, nil, nil, fmt.Errorf("MLX initialization failed: %w", err)
+	}
+
+	tmpDir := ensureTempDir()
+
+	// Read safetensors data to a temp file (LoadSafetensorsNative needs a path)
+	tmpFile, err := os.CreateTemp(tmpDir, "quant-input-*.safetensors")
+	if err != nil {
+		return nil, nil, nil, nil, nil, nil, fmt.Errorf("failed to create temp file: %w", err)
+	}
+	tmpPath := tmpFile.Name()
+	defer os.Remove(tmpPath)
+
+	if _, err := io.Copy(tmpFile, r); err != nil {
+		tmpFile.Close()
+		return nil, nil, nil, nil, nil, nil, fmt.Errorf("failed to write temp file: %w", err)
+	}
+	tmpFile.Close()
+
+	// Load the tensor using MLX's native loader
+	st, err := mlx.LoadSafetensorsNative(tmpPath)
+	if err != nil {
+		return nil, nil, nil, nil, nil, nil, fmt.Errorf("failed to load safetensors: %w", err)
+	}
+	defer st.Free()
+
+	// Get the tensor (it's stored as "data" in our minimal safetensors format)
+	arr := st.Get("data")
+	if arr == nil {
+		return nil, nil, nil, nil, nil, nil, fmt.Errorf("tensor 'data' not found in safetensors")
+	}
+
+	// Convert to BFloat16 if needed (quantize expects float type)
+	if arr.Dtype() != mlx.DtypeBFloat16 && arr.Dtype() != mlx.DtypeFloat32 && arr.Dtype() != mlx.DtypeFloat16 {
+		arr = mlx.AsType(arr, mlx.DtypeBFloat16)
+		mlx.Eval(arr)
+	}
+
+	// Quantize based on quantization type
+	var qweight, scales, qbiases *mlx.Array
+	switch quantize {
+	case "fp8":
+		// affine mode: group_size=32, bits=8
+		qweight, scales, qbiases = mlx.Quantize(arr, 32, 8, "affine")
+	default:
+		return nil, nil, nil, nil, nil, nil, fmt.Errorf("unsupported quantization type: %s", quantize)
+	}
+
+	// Eval and make contiguous for data access
+	qweight = mlx.Contiguous(qweight)
+	scales = mlx.Contiguous(scales)
+	if qbiases != nil {
+		qbiases = mlx.Contiguous(qbiases)
+		mlx.Eval(qweight, scales, qbiases)
+	} else {
+		mlx.Eval(qweight, scales)
+	}
+
+	// Get shapes
+	qweightShape = qweight.Shape()
+	scalesShape = scales.Shape()
+
+	// Save quantized weight using MLX's native safetensors (correctly handles uint32 dtype)
+	qweightPath := filepath.Join(tmpDir, "qweight.safetensors")
+	defer os.Remove(qweightPath)
+	if err := mlx.SaveSafetensors(qweightPath, map[string]*mlx.Array{"data": qweight}); err != nil {
+		return nil, nil, nil, nil, nil, nil, fmt.Errorf("failed to save quantized weight: %w", err)
+	}
+	qweightData, err = os.ReadFile(qweightPath)
+	if err != nil {
+		return nil, nil, nil, nil, nil, nil, fmt.Errorf("failed to read quantized weight: %w", err)
+	}
+
+	// Save scales using MLX's native safetensors
+	scalesPath := filepath.Join(tmpDir, "scales.safetensors")
+	defer os.Remove(scalesPath)
+	if err := mlx.SaveSafetensors(scalesPath, map[string]*mlx.Array{"data": scales}); err != nil {
+		return nil, nil, nil, nil, nil, nil, fmt.Errorf("failed to save scales: %w", err)
+	}
+	scalesData, err = os.ReadFile(scalesPath)
+	if err != nil {
+		return nil, nil, nil, nil, nil, nil, fmt.Errorf("failed to read scales: %w", err)
+	}
+
+	// Affine mode returns qbiases for zero-point offset
+	if qbiases != nil {
+		qbiasShape = qbiases.Shape()
+		qbiasPath := filepath.Join(tmpDir, "qbias.safetensors")
+		defer os.Remove(qbiasPath)
+		if err := mlx.SaveSafetensors(qbiasPath, map[string]*mlx.Array{"data": qbiases}); err != nil {
+			return nil, nil, nil, nil, nil, nil, fmt.Errorf("failed to save qbiases: %w", err)
+		}
+		qbiasData, err = os.ReadFile(qbiasPath)
+		if err != nil {
+			return nil, nil, nil, nil, nil, nil, fmt.Errorf("failed to read qbiases: %w", err)
+		}
+	}
+
+	return qweightData, scalesData, qbiasData, qweightShape, scalesShape, qbiasShape, nil
+}
+
+// QuantizeSupported returns true if quantization is supported (MLX build)
+func QuantizeSupported() bool {
+	return true
+}
+
+// ensureTempDir creates the temp directory for quantization if it doesn't exist
+func ensureTempDir() string {
+	tmpDir := filepath.Join(os.TempDir(), "ollama-quantize")
+	os.MkdirAll(tmpDir, 0755)
+	return tmpDir
+}

x/create/client/quantize_stub.go

@@ -0,0 +1,18 @@
+//go:build !mlx
+
+package client
+
+import (
+	"fmt"
+	"io"
+)
+
+// quantizeTensor is not available without MLX
+func quantizeTensor(r io.Reader, name, dtype string, shape []int32, quantize string) (qweightData, scalesData, qbiasData []byte, qweightShape, scalesShape, qbiasShape []int32, err error) {
+	return nil, nil, nil, nil, nil, nil, fmt.Errorf("quantization requires MLX support (build with mlx tag)")
+}
+
+// QuantizeSupported returns false when MLX is not available
+func QuantizeSupported() bool {
+	return false
+}

x/create/create.go

@@ -0,0 +1,399 @@
+package create
+
+import (
+	"encoding/json"
+	"fmt"
+	"io"
+	"os"
+	"path/filepath"
+	"slices"
+	"strings"
+
+	"github.com/ollama/ollama/envconfig"
+	"github.com/ollama/ollama/x/imagegen/safetensors"
+)
+
+// ModelConfig represents the config blob stored with a model.
+type ModelConfig struct {
+	ModelFormat  string   `json:"model_format"`
+	Capabilities []string `json:"capabilities"`
+}
+
+// Manifest represents the manifest JSON structure.
+type Manifest struct {
+	SchemaVersion int             `json:"schemaVersion"`
+	MediaType     string          `json:"mediaType"`
+	Config        ManifestLayer   `json:"config"`
+	Layers        []ManifestLayer `json:"layers"`
+}
+
+// ManifestLayer represents a layer in the manifest.
+type ManifestLayer struct {
+	MediaType string `json:"mediaType"`
+	Digest    string `json:"digest"`
+	Size      int64  `json:"size"`
+	Name      string `json:"name,omitempty"`
+}
+
+// defaultManifestDir returns the manifest storage directory.
+func defaultManifestDir() string {
+	return filepath.Join(envconfig.Models(), "manifests")
+}
+
+// defaultBlobDir returns the blob storage directory.
+func defaultBlobDir() string {
+	return filepath.Join(envconfig.Models(), "blobs")
+}
+
+// resolveManifestPath converts a model name to a manifest file path.
+func resolveManifestPath(modelName string) string {
+	host := "registry.ollama.ai"
+	namespace := "library"
+	name := modelName
+	tag := "latest"
+
+	if idx := strings.LastIndex(name, ":"); idx != -1 {
+		tag = name[idx+1:]
+		name = name[:idx]
+	}
+
+	parts := strings.Split(name, "/")
+	switch len(parts) {
+	case 3:
+		host = parts[0]
+		namespace = parts[1]
+		name = parts[2]
+	case 2:
+		namespace = parts[0]
+		name = parts[1]
+	}
+
+	return filepath.Join(defaultManifestDir(), host, namespace, name, tag)
+}
+
+// loadManifest loads a manifest for the given model name.
+func loadManifest(modelName string) (*Manifest, error) {
+	manifestPath := resolveManifestPath(modelName)
+
+	data, err := os.ReadFile(manifestPath)
+	if err != nil {
+		return nil, err
+	}
+
+	var manifest Manifest
+	if err := json.Unmarshal(data, &manifest); err != nil {
+		return nil, err
+	}
+
+	return &manifest, nil
+}
+
+// loadModelConfig loads the config blob for a model.
+func loadModelConfig(modelName string) (*ModelConfig, error) {
+	manifest, err := loadManifest(modelName)
+	if err != nil {
+		return nil, err
+	}
+
+	// Read the config blob
+	blobName := strings.Replace(manifest.Config.Digest, ":", "-", 1)
+	blobPath := filepath.Join(defaultBlobDir(), blobName)
+
+	data, err := os.ReadFile(blobPath)
+	if err != nil {
+		return nil, err
+	}
+
+	var config ModelConfig
+	if err := json.Unmarshal(data, &config); err != nil {
+		return nil, err
+	}
+
+	return &config, nil
+}
+
+// IsSafetensorsModel checks if a model was created with the experimental
+// safetensors builder by checking the model format in the config.
+func IsSafetensorsModel(modelName string) bool {
+	config, err := loadModelConfig(modelName)
+	if err != nil {
+		return false
+	}
+	return config.ModelFormat == "safetensors"
+}
+
+// IsSafetensorsLLMModel checks if a model is a safetensors LLM model
+// (has completion capability, not image generation).
+func IsSafetensorsLLMModel(modelName string) bool {
+	config, err := loadModelConfig(modelName)
+	if err != nil {
+		return false
+	}
+	return config.ModelFormat == "safetensors" && slices.Contains(config.Capabilities, "completion")
+}
+
+// IsImageGenModel checks if a model is an image generation model
+// (has image capability).
+func IsImageGenModel(modelName string) bool {
+	config, err := loadModelConfig(modelName)
+	if err != nil {
+		return false
+	}
+	return config.ModelFormat == "safetensors" && slices.Contains(config.Capabilities, "image")
+}
+
+// GetModelArchitecture returns the architecture from the model's config.json layer.
+func GetModelArchitecture(modelName string) (string, error) {
+	manifest, err := loadManifest(modelName)
+	if err != nil {
+		return "", err
+	}
+
+	// Find the config.json layer
+	for _, layer := range manifest.Layers {
+		if layer.Name == "config.json" && layer.MediaType == "application/vnd.ollama.image.json" {
+			blobName := strings.Replace(layer.Digest, ":", "-", 1)
+			blobPath := filepath.Join(defaultBlobDir(), blobName)
+
+			data, err := os.ReadFile(blobPath)
+			if err != nil {
+				return "", err
+			}
+
+			var cfg struct {
+				Architectures []string `json:"architectures"`
+				ModelType     string   `json:"model_type"`
+			}
+			if err := json.Unmarshal(data, &cfg); err != nil {
+				return "", err
+			}
+
+			// Prefer model_type, fall back to first architecture
+			if cfg.ModelType != "" {
+				return cfg.ModelType, nil
+			}
+			if len(cfg.Architectures) > 0 {
+				return cfg.Architectures[0], nil
+			}
+		}
+	}
+
+	return "", fmt.Errorf("architecture not found in model config")
+}
+
+// IsTensorModelDir checks if the directory contains a diffusers-style tensor model
+// by looking for model_index.json, which is the standard diffusers pipeline config.
+func IsTensorModelDir(dir string) bool {
+	_, err := os.Stat(filepath.Join(dir, "model_index.json"))
+	return err == nil
+}
+
+// IsSafetensorsModelDir checks if the directory contains a standard safetensors model
+// by looking for config.json and at least one .safetensors file.
+func IsSafetensorsModelDir(dir string) bool {
+	// Must have config.json
+	if _, err := os.Stat(filepath.Join(dir, "config.json")); err != nil {
+		return false
+	}
+
+	// Must have at least one .safetensors file
+	entries, err := os.ReadDir(dir)
+	if err != nil {
+		return false
+	}
+
+	for _, entry := range entries {
+		if strings.HasSuffix(entry.Name(), ".safetensors") {
+			return true
+		}
+	}
+
+	return false
+}
+
+// LayerInfo holds metadata for a created layer.
+type LayerInfo struct {
+	Digest    string
+	Size      int64
+	MediaType string
+	Name      string // Path-style name: "component/tensor" or "path/to/config.json"
+}
+
+// LayerCreator is called to create a blob layer.
+// name is the path-style name (e.g., "tokenizer/tokenizer.json")
+type LayerCreator func(r io.Reader, mediaType, name string) (LayerInfo, error)
+
+// TensorLayerCreator creates a tensor blob layer with metadata.
+// name is the path-style name including component (e.g., "text_encoder/model.embed_tokens.weight")
+type TensorLayerCreator func(r io.Reader, name, dtype string, shape []int32) (LayerInfo, error)
+
+// QuantizingTensorLayerCreator creates tensor layers with optional quantization.
+// When quantize is non-empty (e.g., "fp8"), returns multiple layers (weight + scales + biases).
+type QuantizingTensorLayerCreator func(r io.Reader, name, dtype string, shape []int32, quantize string) ([]LayerInfo, error)
+
+// ManifestWriter writes the manifest file.
+type ManifestWriter func(modelName string, config LayerInfo, layers []LayerInfo) error
+
+// ShouldQuantize returns true if a tensor should be quantized.
+// For image gen models (component non-empty): quantizes linear weights, skipping VAE, embeddings, norms.
+// For LLM models (component empty): quantizes linear weights, skipping embeddings, norms, and small tensors.
+func ShouldQuantize(name, component string) bool {
+	// Image gen specific: skip VAE entirely
+	if component == "vae" {
+		return false
+	}
+
+	// Skip embeddings
+	if strings.Contains(name, "embed") {
+		return false
+	}
+
+	// Skip layer norms and RMS norms
+	if strings.Contains(name, "norm") || strings.Contains(name, "ln_") || strings.Contains(name, "layernorm") {
+		return false
+	}
+
+	// Skip biases
+	if strings.HasSuffix(name, ".bias") {
+		return false
+	}
+
+	// Only quantize weights
+	return strings.HasSuffix(name, ".weight")
+}
+
+// ShouldQuantizeTensor returns true if a tensor should be quantized based on name and shape.
+// This is a more detailed check that also considers tensor dimensions.
+func ShouldQuantizeTensor(name string, shape []int32) bool {
+	// Use basic name-based check first
+	if !ShouldQuantize(name, "") {
+		return false
+	}
+
+	// Only quantize 2D tensors (linear layers) - skip 1D (biases, norms) and higher-D (convolutions if any)
+	if len(shape) != 2 {
+		return false
+	}
+
+	// Skip small tensors (less than 1024 elements) - not worth quantizing
+	if len(shape) >= 2 && int64(shape[0])*int64(shape[1]) < 1024 {
+		return false
+	}
+
+	// MLX quantization requires last dimension to be divisible by group size (32)
+	if shape[len(shape)-1]%32 != 0 {
+		return false
+	}
+
+	return true
+}
+
+// CreateSafetensorsModel imports a standard safetensors model from a directory.
+// This handles Hugging Face style models with config.json and *.safetensors files.
+// Stores each tensor as a separate blob for fine-grained deduplication.
+// If quantize is non-empty (e.g., "fp8"), eligible tensors will be quantized.
+func CreateSafetensorsModel(modelName, modelDir, quantize string, createLayer LayerCreator, createTensorLayer QuantizingTensorLayerCreator, writeManifest ManifestWriter, fn func(status string)) error {
+	var layers []LayerInfo
+	var configLayer LayerInfo
+
+	entries, err := os.ReadDir(modelDir)
+	if err != nil {
+		return fmt.Errorf("failed to read directory: %w", err)
+	}
+
+	// Process all safetensors files
+	for _, entry := range entries {
+		if entry.IsDir() || !strings.HasSuffix(entry.Name(), ".safetensors") {
+			continue
+		}
+
+		stPath := filepath.Join(modelDir, entry.Name())
+
+		// Extract individual tensors from safetensors file
+		extractor, err := safetensors.OpenForExtraction(stPath)
+		if err != nil {
+			return fmt.Errorf("failed to open %s: %w", stPath, err)
+		}
+
+		tensorNames := extractor.ListTensors()
+		quantizeMsg := ""
+		if quantize != "" {
+			quantizeMsg = fmt.Sprintf(", quantizing to %s", quantize)
+		}
+		fn(fmt.Sprintf("importing %s (%d tensors%s)", entry.Name(), len(tensorNames), quantizeMsg))
+
+		for _, tensorName := range tensorNames {
+			td, err := extractor.GetTensor(tensorName)
+			if err != nil {
+				extractor.Close()
+				return fmt.Errorf("failed to get tensor %s: %w", tensorName, err)
+			}
+
+			// Determine quantization type for this tensor (empty string if not quantizing)
+			quantizeType := ""
+			if quantize != "" && ShouldQuantizeTensor(tensorName, td.Shape) {
+				quantizeType = quantize
+			}
+
+			// Store as minimal safetensors format (88 bytes header overhead)
+			// This enables native mmap loading via mlx_load_safetensors
+			// createTensorLayer returns multiple layers if quantizing (weight + scales)
+			newLayers, err := createTensorLayer(td.SafetensorsReader(), tensorName, td.Dtype, td.Shape, quantizeType)
+			if err != nil {
+				extractor.Close()
+				return fmt.Errorf("failed to create layer for %s: %w", tensorName, err)
+			}
+			layers = append(layers, newLayers...)
+		}
+
+		extractor.Close()
+	}
+
+	// Process all JSON config files
+	for _, entry := range entries {
+		if entry.IsDir() || !strings.HasSuffix(entry.Name(), ".json") {
+			continue
+		}
+
+		// Skip the index file as we don't need it after extraction
+		if entry.Name() == "model.safetensors.index.json" {
+			continue
+		}
+
+		cfgPath := entry.Name()
+		fullPath := filepath.Join(modelDir, cfgPath)
+
+		fn(fmt.Sprintf("importing config %s", cfgPath))
+
+		f, err := os.Open(fullPath)
+		if err != nil {
+			return fmt.Errorf("failed to open %s: %w", cfgPath, err)
+		}
+
+		layer, err := createLayer(f, "application/vnd.ollama.image.json", cfgPath)
+		f.Close()
+		if err != nil {
+			return fmt.Errorf("failed to create layer for %s: %w", cfgPath, err)
+		}
+
+		// Use config.json as the config layer
+		if cfgPath == "config.json" {
+			configLayer = layer
+		}
+
+		layers = append(layers, layer)
+	}
+
+	if configLayer.Digest == "" {
+		return fmt.Errorf("config.json not found in %s", modelDir)
+	}
+
+	fn(fmt.Sprintf("writing manifest for %s", modelName))
+
+	if err := writeManifest(modelName, configLayer, layers); err != nil {
+		return fmt.Errorf("failed to write manifest: %w", err)
+	}
+
+	fn(fmt.Sprintf("successfully imported %s with %d layers", modelName, len(layers)))
+	return nil
+}

x/create/create_test.go

@@ -0,0 +1,752 @@
+package create
+
+import (
+	"bytes"
+	"encoding/binary"
+	"encoding/json"
+	"io"
+	"os"
+	"path/filepath"
+	"strings"
+	"testing"
+)
+
+func TestIsTensorModelDir(t *testing.T) {
+	tests := []struct {
+		name     string
+		setup    func(dir string) error
+		expected bool
+	}{
+		{
+			name: "valid diffusers model with model_index.json",
+			setup: func(dir string) error {
+				return os.WriteFile(filepath.Join(dir, "model_index.json"), []byte(`{"_class_name": "FluxPipeline"}`), 0o644)
+			},
+			expected: true,
+		},
+		{
+			name: "empty directory",
+			setup: func(dir string) error {
+				return nil
+			},
+			expected: false,
+		},
+		{
+			name: "directory with other files but no model_index.json",
+			setup: func(dir string) error {
+				return os.WriteFile(filepath.Join(dir, "config.json"), []byte(`{}`), 0o644)
+			},
+			expected: false,
+		},
+	}
+
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			dir := t.TempDir()
+			if err := tt.setup(dir); err != nil {
+				t.Fatalf("setup failed: %v", err)
+			}
+
+			got := IsTensorModelDir(dir)
+			if got != tt.expected {
+				t.Errorf("IsTensorModelDir() = %v, want %v", got, tt.expected)
+			}
+		})
+	}
+}
+
+func TestIsSafetensorsModelDir(t *testing.T) {
+	tests := []struct {
+		name     string
+		setup    func(dir string) error
+		expected bool
+	}{
+		{
+			name: "valid safetensors model with config.json and .safetensors file",
+			setup: func(dir string) error {
+				if err := os.WriteFile(filepath.Join(dir, "config.json"), []byte(`{"model_type": "gemma3"}`), 0o644); err != nil {
+					return err
+				}
+				return os.WriteFile(filepath.Join(dir, "model.safetensors"), []byte("dummy"), 0o644)
+			},
+			expected: true,
+		},
+		{
+			name: "config.json only, no safetensors files",
+			setup: func(dir string) error {
+				return os.WriteFile(filepath.Join(dir, "config.json"), []byte(`{}`), 0o644)
+			},
+			expected: false,
+		},
+		{
+			name: "safetensors file only, no config.json",
+			setup: func(dir string) error {
+				return os.WriteFile(filepath.Join(dir, "model.safetensors"), []byte("dummy"), 0o644)
+			},
+			expected: false,
+		},
+		{
+			name: "empty directory",
+			setup: func(dir string) error {
+				return nil
+			},
+			expected: false,
+		},
+		{
+			name: "multiple safetensors files with config.json",
+			setup: func(dir string) error {
+				if err := os.WriteFile(filepath.Join(dir, "config.json"), []byte(`{}`), 0o644); err != nil {
+					return err
+				}
+				if err := os.WriteFile(filepath.Join(dir, "model-00001-of-00002.safetensors"), []byte("dummy"), 0o644); err != nil {
+					return err
+				}
+				return os.WriteFile(filepath.Join(dir, "model-00002-of-00002.safetensors"), []byte("dummy"), 0o644)
+			},
+			expected: true,
+		},
+	}
+
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			dir := t.TempDir()
+			if err := tt.setup(dir); err != nil {
+				t.Fatalf("setup failed: %v", err)
+			}
+
+			got := IsSafetensorsModelDir(dir)
+			if got != tt.expected {
+				t.Errorf("IsSafetensorsModelDir() = %v, want %v", got, tt.expected)
+			}
+		})
+	}
+}
+
+func TestIsSafetensorsModelDir_NonexistentDir(t *testing.T) {
+	got := IsSafetensorsModelDir("/nonexistent/path/that/does/not/exist")
+	if got != false {
+		t.Errorf("IsSafetensorsModelDir() = %v for nonexistent dir, want false", got)
+	}
+}
+
+// createMinimalSafetensors creates a minimal valid safetensors file with one tensor
+func createMinimalSafetensors(t *testing.T, path string) {
+	t.Helper()
+
+	// Create a minimal safetensors file with a single float32 tensor
+	header := map[string]interface{}{
+		"test_tensor": map[string]interface{}{
+			"dtype":        "F32",
+			"shape":        []int{2, 2},
+			"data_offsets": []int{0, 16}, // 4 float32 values = 16 bytes
+		},
+	}
+	headerJSON, err := json.Marshal(header)
+	if err != nil {
+		t.Fatalf("failed to marshal header: %v", err)
+	}
+
+	// Pad header to 8-byte alignment
+	padding := (8 - len(headerJSON)%8) % 8
+	headerJSON = append(headerJSON, bytes.Repeat([]byte(" "), padding)...)
+
+	// Write file
+	f, err := os.Create(path)
+	if err != nil {
+		t.Fatalf("failed to create file: %v", err)
+	}
+	defer f.Close()
+
+	// Write header size (8 bytes, little endian)
+	if err := binary.Write(f, binary.LittleEndian, uint64(len(headerJSON))); err != nil {
+		t.Fatalf("failed to write header size: %v", err)
+	}
+
+	// Write header
+	if _, err := f.Write(headerJSON); err != nil {
+		t.Fatalf("failed to write header: %v", err)
+	}
+
+	// Write tensor data (16 bytes of zeros for 4 float32 values)
+	if _, err := f.Write(make([]byte, 16)); err != nil {
+		t.Fatalf("failed to write tensor data: %v", err)
+	}
+}
+
+func TestCreateSafetensorsModel(t *testing.T) {
+	dir := t.TempDir()
+
+	// Create config.json
+	configJSON := `{"model_type": "test", "architectures": ["TestModel"]}`
+	if err := os.WriteFile(filepath.Join(dir, "config.json"), []byte(configJSON), 0o644); err != nil {
+		t.Fatalf("failed to write config.json: %v", err)
+	}
+
+	// Create a minimal safetensors file
+	createMinimalSafetensors(t, filepath.Join(dir, "model.safetensors"))
+
+	// Track what was created
+	var createdLayers []LayerInfo
+	var manifestWritten bool
+	var manifestModelName string
+	var manifestConfigLayer LayerInfo
+	var manifestLayers []LayerInfo
+	var statusMessages []string
+
+	// Mock callbacks
+	createLayer := func(r io.Reader, mediaType, name string) (LayerInfo, error) {
+		data, err := io.ReadAll(r)
+		if err != nil {
+			return LayerInfo{}, err
+		}
+		layer := LayerInfo{
+			Digest:    "sha256:test",
+			Size:      int64(len(data)),
+			MediaType: mediaType,
+			Name:      name,
+		}
+		createdLayers = append(createdLayers, layer)
+		return layer, nil
+	}
+
+	createTensorLayer := func(r io.Reader, name, dtype string, shape []int32, quantize string) ([]LayerInfo, error) {
+		data, err := io.ReadAll(r)
+		if err != nil {
+			return nil, err
+		}
+		layer := LayerInfo{
+			Digest:    "sha256:tensor_" + name,
+			Size:      int64(len(data)),
+			MediaType: "application/vnd.ollama.image.tensor",
+			Name:      name,
+		}
+		createdLayers = append(createdLayers, layer)
+		return []LayerInfo{layer}, nil
+	}
+
+	writeManifest := func(modelName string, config LayerInfo, layers []LayerInfo) error {
+		manifestWritten = true
+		manifestModelName = modelName
+		manifestConfigLayer = config
+		manifestLayers = layers
+		return nil
+	}
+
+	progressFn := func(status string) {
+		statusMessages = append(statusMessages, status)
+	}
+
+	// Run CreateSafetensorsModel
+	err := CreateSafetensorsModel("test-model", dir, "", createLayer, createTensorLayer, writeManifest, progressFn)
+	if err != nil {
+		t.Fatalf("CreateSafetensorsModel failed: %v", err)
+	}
+
+	// Verify manifest was written
+	if !manifestWritten {
+		t.Error("manifest was not written")
+	}
+
+	if manifestModelName != "test-model" {
+		t.Errorf("manifest model name = %q, want %q", manifestModelName, "test-model")
+	}
+
+	// Verify config layer was set
+	if manifestConfigLayer.Name != "config.json" {
+		t.Errorf("config layer name = %q, want %q", manifestConfigLayer.Name, "config.json")
+	}
+
+	// Verify we have at least one tensor and one config layer
+	hasTensor := false
+	hasConfig := false
+	for _, layer := range manifestLayers {
+		if layer.Name == "test_tensor" {
+			hasTensor = true
+		}
+		if layer.Name == "config.json" {
+			hasConfig = true
+		}
+	}
+
+	if !hasTensor {
+		t.Error("no tensor layer found in manifest")
+	}
+	if !hasConfig {
+		t.Error("no config layer found in manifest")
+	}
+
+	// Verify status messages were sent
+	if len(statusMessages) == 0 {
+		t.Error("no status messages received")
+	}
+}
+
+func TestCreateSafetensorsModel_NoConfigJson(t *testing.T) {
+	dir := t.TempDir()
+
+	// Create only a safetensors file, no config.json
+	createMinimalSafetensors(t, filepath.Join(dir, "model.safetensors"))
+
+	// Mock callbacks (minimal)
+	createLayer := func(r io.Reader, mediaType, name string) (LayerInfo, error) {
+		io.ReadAll(r)
+		return LayerInfo{Name: name}, nil
+	}
+	createTensorLayer := func(r io.Reader, name, dtype string, shape []int32, quantize string) ([]LayerInfo, error) {
+		io.ReadAll(r)
+		return []LayerInfo{{Name: name}}, nil
+	}
+	writeManifest := func(modelName string, config LayerInfo, layers []LayerInfo) error {
+		return nil
+	}
+	progressFn := func(status string) {}
+
+	err := CreateSafetensorsModel("test-model", dir, "", createLayer, createTensorLayer, writeManifest, progressFn)
+	if err == nil {
+		t.Error("expected error for missing config.json, got nil")
+	}
+}
+
+func TestCreateSafetensorsModel_EmptyDir(t *testing.T) {
+	dir := t.TempDir()
+
+	// Mock callbacks
+	createLayer := func(r io.Reader, mediaType, name string) (LayerInfo, error) {
+		return LayerInfo{}, nil
+	}
+	createTensorLayer := func(r io.Reader, name, dtype string, shape []int32, quantize string) ([]LayerInfo, error) {
+		return []LayerInfo{{}}, nil
+	}
+	writeManifest := func(modelName string, config LayerInfo, layers []LayerInfo) error {
+		return nil
+	}
+	progressFn := func(status string) {}
+
+	err := CreateSafetensorsModel("test-model", dir, "", createLayer, createTensorLayer, writeManifest, progressFn)
+	if err == nil {
+		t.Error("expected error for empty directory, got nil")
+	}
+}
+
+func TestCreateSafetensorsModel_SkipsIndexJson(t *testing.T) {
+	dir := t.TempDir()
+
+	// Create config.json
+	if err := os.WriteFile(filepath.Join(dir, "config.json"), []byte(`{}`), 0o644); err != nil {
+		t.Fatalf("failed to write config.json: %v", err)
+	}
+
+	// Create model.safetensors.index.json (should be skipped)
+	indexJSON := `{"metadata": {"total_size": 100}, "weight_map": {}}`
+	if err := os.WriteFile(filepath.Join(dir, "model.safetensors.index.json"), []byte(indexJSON), 0o644); err != nil {
+		t.Fatalf("failed to write index.json: %v", err)
+	}
+
+	// Create a minimal safetensors file
+	createMinimalSafetensors(t, filepath.Join(dir, "model.safetensors"))
+
+	var configNames []string
+
+	createLayer := func(r io.Reader, mediaType, name string) (LayerInfo, error) {
+		io.ReadAll(r)
+		configNames = append(configNames, name)
+		return LayerInfo{Name: name, Digest: "sha256:test"}, nil
+	}
+	createTensorLayer := func(r io.Reader, name, dtype string, shape []int32, quantize string) ([]LayerInfo, error) {
+		io.ReadAll(r)
+		return []LayerInfo{{Name: name}}, nil
+	}
+	writeManifest := func(modelName string, config LayerInfo, layers []LayerInfo) error {
+		return nil
+	}
+	progressFn := func(status string) {}
+
+	err := CreateSafetensorsModel("test-model", dir, "", createLayer, createTensorLayer, writeManifest, progressFn)
+	if err != nil {
+		t.Fatalf("CreateSafetensorsModel failed: %v", err)
+	}
+
+	// Verify model.safetensors.index.json was not included
+	for _, name := range configNames {
+		if name == "model.safetensors.index.json" {
+			t.Error("model.safetensors.index.json should have been skipped")
+		}
+	}
+}
+
+func TestResolveManifestPath(t *testing.T) {
+	tests := []struct {
+		name      string
+		modelName string
+		wantParts []string // Parts that should appear in the path
+	}{
+		{
+			name:      "simple model name",
+			modelName: "llama2",
+			wantParts: []string{"registry.ollama.ai", "library", "llama2", "latest"},
+		},
+		{
+			name:      "model name with tag",
+			modelName: "llama2:7b",
+			wantParts: []string{"registry.ollama.ai", "library", "llama2", "7b"},
+		},
+		{
+			name:      "model name with namespace",
+			modelName: "myuser/mymodel",
+			wantParts: []string{"registry.ollama.ai", "myuser", "mymodel", "latest"},
+		},
+		{
+			name:      "model name with namespace and tag",
+			modelName: "myuser/mymodel:v1",
+			wantParts: []string{"registry.ollama.ai", "myuser", "mymodel", "v1"},
+		},
+		{
+			name:      "fully qualified model name",
+			modelName: "registry.example.com/namespace/model:tag",
+			wantParts: []string{"registry.example.com", "namespace", "model", "tag"},
+		},
+	}
+
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			got := resolveManifestPath(tt.modelName)
+
+			for _, part := range tt.wantParts {
+				if !strings.Contains(got, part) {
+					t.Errorf("resolveManifestPath(%q) = %q, missing part %q", tt.modelName, got, part)
+				}
+			}
+		})
+	}
+}
+
+func TestLayerInfo(t *testing.T) {
+	layer := LayerInfo{
+		Digest:    "sha256:abc123",
+		Size:      1024,
+		MediaType: "application/vnd.ollama.image.tensor",
+		Name:      "model.weight",
+	}
+
+	if layer.Digest != "sha256:abc123" {
+		t.Errorf("Digest = %q, want %q", layer.Digest, "sha256:abc123")
+	}
+	if layer.Size != 1024 {
+		t.Errorf("Size = %d, want %d", layer.Size, 1024)
+	}
+	if layer.MediaType != "application/vnd.ollama.image.tensor" {
+		t.Errorf("MediaType = %q, want %q", layer.MediaType, "application/vnd.ollama.image.tensor")
+	}
+	if layer.Name != "model.weight" {
+		t.Errorf("Name = %q, want %q", layer.Name, "model.weight")
+	}
+}
+
+func TestModelConfig(t *testing.T) {
+	config := ModelConfig{
+		ModelFormat:  "safetensors",
+		Capabilities: []string{"completion", "chat"},
+	}
+
+	if config.ModelFormat != "safetensors" {
+		t.Errorf("ModelFormat = %q, want %q", config.ModelFormat, "safetensors")
+	}
+	if len(config.Capabilities) != 2 {
+		t.Errorf("Capabilities length = %d, want %d", len(config.Capabilities), 2)
+	}
+}
+
+func TestManifest(t *testing.T) {
+	manifest := Manifest{
+		SchemaVersion: 2,
+		MediaType:     "application/vnd.oci.image.manifest.v1+json",
+		Config: ManifestLayer{
+			MediaType: "application/vnd.docker.container.image.v1+json",
+			Digest:    "sha256:config",
+			Size:      100,
+		},
+		Layers: []ManifestLayer{
+			{
+				MediaType: "application/vnd.ollama.image.tensor",
+				Digest:    "sha256:layer1",
+				Size:      1000,
+				Name:      "weight.bin",
+			},
+		},
+	}
+
+	if manifest.SchemaVersion != 2 {
+		t.Errorf("SchemaVersion = %d, want %d", manifest.SchemaVersion, 2)
+	}
+	if manifest.Config.Digest != "sha256:config" {
+		t.Errorf("Config.Digest = %q, want %q", manifest.Config.Digest, "sha256:config")
+	}
+	if len(manifest.Layers) != 1 {
+		t.Errorf("Layers length = %d, want %d", len(manifest.Layers), 1)
+	}
+	if manifest.Layers[0].Name != "weight.bin" {
+		t.Errorf("Layers[0].Name = %q, want %q", manifest.Layers[0].Name, "weight.bin")
+	}
+}
+
+func TestShouldQuantize(t *testing.T) {
+	tests := []struct {
+		name      string
+		tensor    string
+		component string
+		want      bool
+	}{
+		// VAE component should never be quantized
+		{"vae weight", "decoder.weight", "vae", false},
+		{"vae bias", "decoder.bias", "vae", false},
+
+		// Embeddings should not be quantized
+		{"embedding weight", "embed_tokens.weight", "", false},
+		{"embedding in name", "token_embedding.weight", "", false},
+
+		// Norms should not be quantized
+		{"layer norm", "layer_norm.weight", "", false},
+		{"rms norm", "rms_norm.weight", "", false},
+		{"ln prefix", "ln_1.weight", "", false},
+		{"layernorm in name", "input_layernorm.weight", "", false},
+
+		// Biases should not be quantized
+		{"bias tensor", "attention.bias", "", false},
+		{"proj bias", "o_proj.bias", "", false},
+
+		// Linear weights should be quantized
+		{"linear weight", "q_proj.weight", "", true},
+		{"attention weight", "self_attn.weight", "", true},
+		{"mlp weight", "mlp.gate_proj.weight", "", true},
+
+		// Transformer component weights should be quantized
+		{"transformer weight", "layers.0.weight", "transformer", true},
+		{"text_encoder weight", "encoder.weight", "text_encoder", true},
+	}
+
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			got := ShouldQuantize(tt.tensor, tt.component)
+			if got != tt.want {
+				t.Errorf("ShouldQuantize(%q, %q) = %v, want %v", tt.tensor, tt.component, got, tt.want)
+			}
+		})
+	}
+}
+
+func TestShouldQuantizeTensor(t *testing.T) {
+	tests := []struct {
+		name   string
+		tensor string
+		shape  []int32
+		want   bool
+	}{
+		// 2D tensors with sufficient size should be quantized
+		{"large 2D weight", "q_proj.weight", []int32{4096, 4096}, true},
+		{"medium 2D weight", "small_proj.weight", []int32{128, 128}, true},
+
+		// Small tensors should not be quantized (< 1024 elements)
+		{"tiny 2D weight", "tiny.weight", []int32{16, 16}, false},
+		{"small 2D weight", "small.weight", []int32{31, 31}, false},
+
+		// 1D tensors should not be quantized
+		{"1D tensor", "layer_norm.weight", []int32{4096}, false},
+
+		// 3D+ tensors should not be quantized
+		{"3D tensor", "conv.weight", []int32{64, 64, 3}, false},
+		{"4D tensor", "conv2d.weight", []int32{64, 64, 3, 3}, false},
+
+		// Embeddings should not be quantized regardless of shape
+		{"embedding 2D", "embed_tokens.weight", []int32{32000, 4096}, false},
+
+		// Norms should not be quantized regardless of shape
+		{"norm 2D", "layer_norm.weight", []int32{4096, 1}, false},
+
+		// Biases should not be quantized
+		{"bias 2D", "proj.bias", []int32{4096, 1}, false},
+	}
+
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			got := ShouldQuantizeTensor(tt.tensor, tt.shape)
+			if got != tt.want {
+				t.Errorf("ShouldQuantizeTensor(%q, %v) = %v, want %v", tt.tensor, tt.shape, got, tt.want)
+			}
+		})
+	}
+}
+
+func TestCreateSafetensorsModel_WithQuantize(t *testing.T) {
+	dir := t.TempDir()
+
+	// Create config.json
+	configJSON := `{"model_type": "test", "architectures": ["TestModel"]}`
+	if err := os.WriteFile(filepath.Join(dir, "config.json"), []byte(configJSON), 0o644); err != nil {
+		t.Fatalf("failed to write config.json: %v", err)
+	}
+
+	// Create a minimal safetensors file
+	createMinimalSafetensors(t, filepath.Join(dir, "model.safetensors"))
+
+	var quantizeRequested []string
+
+	createLayer := func(r io.Reader, mediaType, name string) (LayerInfo, error) {
+		io.ReadAll(r)
+		return LayerInfo{Name: name, Digest: "sha256:test"}, nil
+	}
+
+	createTensorLayer := func(r io.Reader, name, dtype string, shape []int32, quantize string) ([]LayerInfo, error) {
+		io.ReadAll(r)
+		quantizeRequested = append(quantizeRequested, quantize)
+		return []LayerInfo{{Name: name}}, nil
+	}
+
+	writeManifest := func(modelName string, config LayerInfo, layers []LayerInfo) error {
+		return nil
+	}
+
+	progressFn := func(status string) {}
+
+	// Run with quantize enabled
+	err := CreateSafetensorsModel("test-model", dir, "fp8", createLayer, createTensorLayer, writeManifest, progressFn)
+	if err != nil {
+		t.Fatalf("CreateSafetensorsModel failed: %v", err)
+	}
+
+	// Verify quantize was passed to callback (will be false for small test tensor)
+	if len(quantizeRequested) == 0 {
+		t.Error("no tensors processed")
+	}
+}
+
+// createMinimalImageGenModel creates a minimal diffusers-style model directory
+func createMinimalImageGenModel(t *testing.T, dir string) {
+	t.Helper()
+
+	// Create model_index.json
+	modelIndex := `{"_class_name": "FluxPipeline", "_diffusers_version": "0.30.0"}`
+	if err := os.WriteFile(filepath.Join(dir, "model_index.json"), []byte(modelIndex), 0o644); err != nil {
+		t.Fatalf("failed to write model_index.json: %v", err)
+	}
+
+	// Create transformer directory with a safetensors file
+	transformerDir := filepath.Join(dir, "transformer")
+	if err := os.MkdirAll(transformerDir, 0o755); err != nil {
+		t.Fatalf("failed to create transformer dir: %v", err)
+	}
+	createMinimalSafetensors(t, filepath.Join(transformerDir, "model.safetensors"))
+
+	// Create transformer config
+	transformerConfig := `{"hidden_size": 3072}`
+	if err := os.WriteFile(filepath.Join(transformerDir, "config.json"), []byte(transformerConfig), 0o644); err != nil {
+		t.Fatalf("failed to write transformer config: %v", err)
+	}
+}
+
+func TestCreateImageGenModel(t *testing.T) {
+	dir := t.TempDir()
+	createMinimalImageGenModel(t, dir)
+
+	var manifestWritten bool
+	var manifestModelName string
+	var statusMessages []string
+
+	createLayer := func(r io.Reader, mediaType, name string) (LayerInfo, error) {
+		io.ReadAll(r)
+		return LayerInfo{Name: name, Digest: "sha256:test"}, nil
+	}
+
+	createTensorLayer := func(r io.Reader, name, dtype string, shape []int32, quantize string) ([]LayerInfo, error) {
+		io.ReadAll(r)
+		return []LayerInfo{{Name: name, Digest: "sha256:tensor"}}, nil
+	}
+
+	writeManifest := func(modelName string, config LayerInfo, layers []LayerInfo) error {
+		manifestWritten = true
+		manifestModelName = modelName
+		return nil
+	}
+
+	progressFn := func(status string) {
+		statusMessages = append(statusMessages, status)
+	}
+
+	err := CreateImageGenModel("test-imagegen", dir, "", createLayer, createTensorLayer, writeManifest, progressFn)
+	if err != nil {
+		t.Fatalf("CreateImageGenModel failed: %v", err)
+	}
+
+	if !manifestWritten {
+		t.Error("manifest was not written")
+	}
+
+	if manifestModelName != "test-imagegen" {
+		t.Errorf("manifest model name = %q, want %q", manifestModelName, "test-imagegen")
+	}
+
+	if len(statusMessages) == 0 {
+		t.Error("no status messages received")
+	}
+}
+
+func TestCreateImageGenModel_NoModelIndex(t *testing.T) {
+	dir := t.TempDir()
+
+	// Create only transformer without model_index.json
+	transformerDir := filepath.Join(dir, "transformer")
+	if err := os.MkdirAll(transformerDir, 0o755); err != nil {
+		t.Fatalf("failed to create transformer dir: %v", err)
+	}
+	createMinimalSafetensors(t, filepath.Join(transformerDir, "model.safetensors"))
+
+	createLayer := func(r io.Reader, mediaType, name string) (LayerInfo, error) {
+		io.ReadAll(r)
+		return LayerInfo{Name: name}, nil
+	}
+	createTensorLayer := func(r io.Reader, name, dtype string, shape []int32, quantize string) ([]LayerInfo, error) {
+		io.ReadAll(r)
+		return []LayerInfo{{Name: name}}, nil
+	}
+	writeManifest := func(modelName string, config LayerInfo, layers []LayerInfo) error {
+		return nil
+	}
+	progressFn := func(status string) {}
+
+	err := CreateImageGenModel("test-imagegen", dir, "", createLayer, createTensorLayer, writeManifest, progressFn)
+	if err == nil {
+		t.Error("expected error for missing model_index.json, got nil")
+	}
+}
+
+func TestCreateImageGenModel_WithQuantize(t *testing.T) {
+	dir := t.TempDir()
+	createMinimalImageGenModel(t, dir)
+
+	var quantizeRequested []string
+
+	createLayer := func(r io.Reader, mediaType, name string) (LayerInfo, error) {
+		io.ReadAll(r)
+		return LayerInfo{Name: name, Digest: "sha256:test"}, nil
+	}
+
+	createTensorLayer := func(r io.Reader, name, dtype string, shape []int32, quantize string) ([]LayerInfo, error) {
+		io.ReadAll(r)
+		quantizeRequested = append(quantizeRequested, quantize)
+		return []LayerInfo{{Name: name}}, nil
+	}
+
+	writeManifest := func(modelName string, config LayerInfo, layers []LayerInfo) error {
+		return nil
+	}
+
+	progressFn := func(status string) {}
+
+	err := CreateImageGenModel("test-imagegen", dir, "fp8", createLayer, createTensorLayer, writeManifest, progressFn)
+	if err != nil {
+		t.Fatalf("CreateImageGenModel failed: %v", err)
+	}
+
+	if len(quantizeRequested) == 0 {
+		t.Error("no tensors processed")
+	}
+}

x/create/imagegen.go

@@ -1,4 +1,4 @@
-package imagegen
+package create
 
 import (
 	"bytes"
@@ -12,38 +12,24 @@ import (
 	"github.com/ollama/ollama/x/imagegen/safetensors"
 )
 
-// IsTensorModelDir checks if the directory contains a tensor model
-// by looking for model_index.json, which is the standard diffusers pipeline config.
-func IsTensorModelDir(dir string) bool {
-	_, err := os.Stat(filepath.Join(dir, "model_index.json"))
-	return err == nil
-}
-
-// LayerInfo holds metadata for a created layer.
-type LayerInfo struct {
-	Digest    string
-	Size      int64
-	MediaType string
-	Name      string // Path-style name: "component/tensor" or "path/to/config.json"
-}
-
-// LayerCreator is called to create a blob layer.
-// name is the path-style name (e.g., "tokenizer/tokenizer.json")
-type LayerCreator func(r io.Reader, mediaType, name string) (LayerInfo, error)
-
-// TensorLayerCreator creates a tensor blob layer with metadata.
-// name is the path-style name including component (e.g., "text_encoder/model.embed_tokens.weight")
-type TensorLayerCreator func(r io.Reader, name, dtype string, shape []int32) (LayerInfo, error)
-
-// ManifestWriter writes the manifest file.
-type ManifestWriter func(modelName string, config LayerInfo, layers []LayerInfo) error
-
-// CreateModel imports an image generation model from a directory.
+// CreateImageGenModel imports an image generation model from a directory.
 // Stores each tensor as a separate blob for fine-grained deduplication.
+// If quantize is specified, linear weights in transformer/text_encoder are quantized.
+// Supported quantization types: fp8 (or empty for no quantization).
 // Layer creation and manifest writing are done via callbacks to avoid import cycles.
-func CreateModel(modelName, modelDir string, createLayer LayerCreator, createTensorLayer TensorLayerCreator, writeManifest ManifestWriter, fn func(status string)) error {
+func CreateImageGenModel(modelName, modelDir, quantize string, createLayer LayerCreator, createTensorLayer QuantizingTensorLayerCreator, writeManifest ManifestWriter, fn func(status string)) error {
+	// Validate quantization type
+	switch quantize {
+	case "", "fp8":
+		// valid
+	default:
+		return fmt.Errorf("unsupported quantization type %q: supported types are fp8", quantize)
+	}
+
 	var layers []LayerInfo
 	var configLayer LayerInfo
+	var totalParams int64 // Count parameters from original tensor shapes
+	var torchDtype string // Read from component config for quantization display
 
 	// Components to process - extract individual tensors from each
 	components := []string{"text_encoder", "transformer", "vae"}
@@ -74,7 +60,11 @@ func CreateModel(modelName, modelDir string, createLayer LayerCreator, createTen
 			}
 
 			tensorNames := extractor.ListTensors()
-			fn(fmt.Sprintf("importing %s/%s (%d tensors)", component, entry.Name(), len(tensorNames)))
+			quantizeMsg := ""
+			if quantize != "" && component != "vae" {
+				quantizeMsg = ", quantizing to " + quantize
+			}
+			fn(fmt.Sprintf("importing %s/%s (%d tensors%s)", component, entry.Name(), len(tensorNames), quantizeMsg))
 
 			for _, tensorName := range tensorNames {
 				td, err := extractor.GetTensor(tensorName)
@@ -83,22 +73,52 @@ func CreateModel(modelName, modelDir string, createLayer LayerCreator, createTen
 					return fmt.Errorf("failed to get tensor %s: %w", tensorName, err)
 				}
 
+				// Count parameters from original tensor shape
+				if len(td.Shape) > 0 {
+					numElements := int64(1)
+					for _, dim := range td.Shape {
+						numElements *= int64(dim)
+					}
+					totalParams += numElements
+				}
+
 				// Store as minimal safetensors format (88 bytes header overhead)
 				// This enables native mmap loading via mlx_load_safetensors
 				// Use path-style name: "component/tensor_name"
 				fullName := component + "/" + tensorName
-				layer, err := createTensorLayer(td.SafetensorsReader(), fullName, td.Dtype, td.Shape)
+
+				// Determine quantization type for this tensor (empty string if not quantizing)
+				quantizeType := ""
+				if quantize != "" && ShouldQuantize(tensorName, component) && canQuantizeShape(td.Shape) {
+					quantizeType = quantize
+				}
+
+				// createTensorLayer returns multiple layers if quantizing (weight + scales)
+				newLayers, err := createTensorLayer(td.SafetensorsReader(), fullName, td.Dtype, td.Shape, quantizeType)
 				if err != nil {
 					extractor.Close()
 					return fmt.Errorf("failed to create layer for %s: %w", fullName, err)
 				}
-				layers = append(layers, layer)
+				layers = append(layers, newLayers...)
 			}
 
 			extractor.Close()
 		}
 	}
 
+	// Read torch_dtype from text_encoder config for quantization display
+	if torchDtype == "" {
+		textEncoderConfig := filepath.Join(modelDir, "text_encoder/config.json")
+		if data, err := os.ReadFile(textEncoderConfig); err == nil {
+			var cfg struct {
+				TorchDtype string `json:"torch_dtype"`
+			}
+			if json.Unmarshal(data, &cfg) == nil && cfg.TorchDtype != "" {
+				torchDtype = cfg.TorchDtype
+			}
+		}
+	}
+
 	// Import config files
 	configFiles := []string{
 		"model_index.json",
@@ -122,7 +142,7 @@ func CreateModel(modelName, modelDir string, createLayer LayerCreator, createTen
 
 		var r io.Reader
 
-		// For model_index.json, normalize to Ollama format
+		// For model_index.json, normalize to Ollama format and add metadata
 		if cfgPath == "model_index.json" {
 			data, err := os.ReadFile(fullPath)
 			if err != nil {
@@ -141,6 +161,16 @@ func CreateModel(modelName, modelDir string, createLayer LayerCreator, createTen
 			}
 			delete(cfg, "_diffusers_version")
 
+			// Add parameter count (counted from tensor shapes during import)
+			cfg["parameter_count"] = totalParams
+
+			// Add quantization info - use quantize type if set, otherwise torch_dtype
+			if quantize != "" {
+				cfg["quantization"] = strings.ToUpper(quantize)
+			} else {
+				cfg["quantization"] = torchDtype
+			}
+
 			data, err = json.MarshalIndent(cfg, "", "    ")
 			if err != nil {
 				return fmt.Errorf("failed to marshal %s: %w", cfgPath, err)
@@ -181,3 +211,12 @@ func CreateModel(modelName, modelDir string, createLayer LayerCreator, createTen
 	fn(fmt.Sprintf("successfully imported %s with %d layers", modelName, len(layers)))
 	return nil
 }
+
+// canQuantizeShape returns true if a tensor shape is compatible with MLX quantization.
+// MLX requires the last dimension to be divisible by the group size (32).
+func canQuantizeShape(shape []int32) bool {
+	if len(shape) < 2 {
+		return false
+	}
+	return shape[len(shape)-1]%32 == 0
+}

x/imagegen/README.md

@@ -234,3 +234,17 @@ ollama create z-image
 3. Copy config files (*.json) as config layers
 4. Write manifest
 ```
+
+## FP8 Quantization
+
+Z-Image supports FP8 quantization to reduce memory usage by ~50% while maintaining image quality.
+
+### Usage
+
+```bash
+cd ./weights/Z-Image-Turbo
+ollama create z-image-fp8 --quantize fp8
+```
+
+This quantizes weights during import. The resulting model will be ~15GB instead of ~31GB.
+

x/imagegen/api/handler.go

@@ -1,235 +0,0 @@
-package api
-
-import (
-	"encoding/base64"
-	"fmt"
-	"net/http"
-	"os"
-	"strconv"
-	"strings"
-	"time"
-
-	"github.com/gin-gonic/gin"
-
-	"github.com/ollama/ollama/api"
-	"github.com/ollama/ollama/llm"
-	"github.com/ollama/ollama/x/imagegen"
-)
-
-// RunnerScheduler is the interface for scheduling a model runner.
-// This is implemented by server.Server to avoid circular imports.
-type RunnerScheduler interface {
-	ScheduleImageGenRunner(ctx *gin.Context, modelName string, opts api.Options, keepAlive *api.Duration) (llm.LlamaServer, error)
-}
-
-// RegisterRoutes registers the image generation API routes.
-func RegisterRoutes(r gin.IRouter, scheduler RunnerScheduler) {
-	r.POST("/v1/images/generations", func(c *gin.Context) {
-		ImageGenerationHandler(c, scheduler)
-	})
-}
-
-// ImageGenerationHandler handles OpenAI-compatible image generation requests.
-func ImageGenerationHandler(c *gin.Context, scheduler RunnerScheduler) {
-	var req ImageGenerationRequest
-	if err := c.BindJSON(&req); err != nil {
-		c.JSON(http.StatusBadRequest, gin.H{"error": gin.H{"message": err.Error()}})
-		return
-	}
-
-	// Validate required fields
-	if req.Model == "" {
-		c.JSON(http.StatusBadRequest, gin.H{"error": gin.H{"message": "model is required"}})
-		return
-	}
-	if req.Prompt == "" {
-		c.JSON(http.StatusBadRequest, gin.H{"error": gin.H{"message": "prompt is required"}})
-		return
-	}
-
-	// Apply defaults
-	if req.N == 0 {
-		req.N = 1
-	}
-	if req.Size == "" {
-		req.Size = "1024x1024"
-	}
-	if req.ResponseFormat == "" {
-		req.ResponseFormat = "b64_json"
-	}
-
-	// Verify model exists
-	if imagegen.ResolveModelName(req.Model) == "" {
-		c.JSON(http.StatusNotFound, gin.H{"error": gin.H{"message": fmt.Sprintf("model %q not found", req.Model)}})
-		return
-	}
-
-	// Parse size
-	width, height := parseSize(req.Size)
-
-	// Build options - we repurpose NumCtx/NumGPU for width/height
-	opts := api.Options{}
-	opts.NumCtx = int(width)
-	opts.NumGPU = int(height)
-
-	// Schedule runner
-	runner, err := scheduler.ScheduleImageGenRunner(c, req.Model, opts, nil)
-	if err != nil {
-		status := http.StatusInternalServerError
-		if strings.Contains(err.Error(), "not found") {
-			status = http.StatusNotFound
-		}
-		c.JSON(status, gin.H{"error": gin.H{"message": err.Error()}})
-		return
-	}
-
-	// Build completion request
-	completionReq := llm.CompletionRequest{
-		Prompt:  req.Prompt,
-		Options: &opts,
-	}
-
-	if req.Stream {
-		handleStreamingResponse(c, runner, completionReq, req.ResponseFormat)
-	} else {
-		handleNonStreamingResponse(c, runner, completionReq, req.ResponseFormat)
-	}
-}
-
-func handleStreamingResponse(c *gin.Context, runner llm.LlamaServer, req llm.CompletionRequest, format string) {
-	c.Header("Content-Type", "text/event-stream")
-	c.Header("Cache-Control", "no-cache")
-	c.Header("Connection", "keep-alive")
-
-	var imagePath string
-	err := runner.Completion(c.Request.Context(), req, func(resp llm.CompletionResponse) {
-		if resp.Done {
-			imagePath = extractPath(resp.Content)
-		} else {
-			progress := parseProgress(resp.Content)
-			if progress.Total > 0 {
-				c.SSEvent("progress", progress)
-				c.Writer.Flush()
-			}
-		}
-	})
-	if err != nil {
-		c.SSEvent("error", gin.H{"error": err.Error()})
-		return
-	}
-
-	c.SSEvent("done", buildResponse(imagePath, format))
-}
-
-func handleNonStreamingResponse(c *gin.Context, runner llm.LlamaServer, req llm.CompletionRequest, format string) {
-	var imagePath string
-	err := runner.Completion(c.Request.Context(), req, func(resp llm.CompletionResponse) {
-		if resp.Done {
-			imagePath = extractPath(resp.Content)
-		}
-	})
-	if err != nil {
-		c.JSON(http.StatusInternalServerError, gin.H{"error": gin.H{"message": err.Error()}})
-		return
-	}
-
-	c.JSON(http.StatusOK, buildResponse(imagePath, format))
-}
-
-func parseSize(size string) (int32, int32) {
-	parts := strings.Split(size, "x")
-	if len(parts) != 2 {
-		return 1024, 1024
-	}
-	w, _ := strconv.Atoi(parts[0])
-	h, _ := strconv.Atoi(parts[1])
-	if w == 0 {
-		w = 1024
-	}
-	if h == 0 {
-		h = 1024
-	}
-	return int32(w), int32(h)
-}
-
-func extractPath(content string) string {
-	if idx := strings.Index(content, "Image saved to: "); idx >= 0 {
-		return strings.TrimSpace(content[idx+16:])
-	}
-	return ""
-}
-
-func parseProgress(content string) ImageProgressEvent {
-	var step, total int
-	fmt.Sscanf(content, "\rGenerating: step %d/%d", &step, &total)
-	return ImageProgressEvent{Step: step, Total: total}
-}
-
-func buildResponse(imagePath, format string) ImageGenerationResponse {
-	resp := ImageGenerationResponse{
-		Created: time.Now().Unix(),
-		Data:    make([]ImageData, 1),
-	}
-
-	if imagePath == "" {
-		return resp
-	}
-
-	if format == "url" {
-		resp.Data[0].URL = "file://" + imagePath
-	} else {
-		data, err := os.ReadFile(imagePath)
-		if err == nil {
-			resp.Data[0].B64JSON = base64.StdEncoding.EncodeToString(data)
-		}
-	}
-
-	return resp
-}
-
-// HandleGenerateRequest handles Ollama /api/generate requests for image gen models.
-// This allows routes.go to delegate image generation with minimal code.
-func HandleGenerateRequest(c *gin.Context, scheduler RunnerScheduler, modelName, prompt string, keepAlive *api.Duration, streamFn func(c *gin.Context, ch chan any)) {
-	opts := api.Options{}
-
-	// Schedule runner
-	runner, err := scheduler.ScheduleImageGenRunner(c, modelName, opts, keepAlive)
-	if err != nil {
-		c.JSON(http.StatusInternalServerError, gin.H{"error": err.Error()})
-		return
-	}
-
-	// Build completion request
-	completionReq := llm.CompletionRequest{
-		Prompt:  prompt,
-		Options: &opts,
-	}
-
-	// Stream responses via channel
-	ch := make(chan any)
-	go func() {
-		defer close(ch)
-		err := runner.Completion(c.Request.Context(), completionReq, func(resp llm.CompletionResponse) {
-			ch <- GenerateResponse{
-				Model:     modelName,
-				CreatedAt: time.Now().UTC(),
-				Response:  resp.Content,
-				Done:      resp.Done,
-			}
-		})
-		if err != nil {
-			// Log error but don't block - channel is already being consumed
-			_ = err
-		}
-	}()
-
-	streamFn(c, ch)
-}
-
-// GenerateResponse matches api.GenerateResponse structure for streaming.
-type GenerateResponse struct {
-	Model     string    `json:"model"`
-	CreatedAt time.Time `json:"created_at"`
-	Response  string    `json:"response"`
-	Done      bool      `json:"done"`
-}

x/imagegen/api/types.go

@@ -1,31 +0,0 @@
-// Package api provides OpenAI-compatible image generation API types.
-package api
-
-// ImageGenerationRequest is an OpenAI-compatible image generation request.
-type ImageGenerationRequest struct {
-	Model          string `json:"model"`
-	Prompt         string `json:"prompt"`
-	N              int    `json:"n,omitempty"`
-	Size           string `json:"size,omitempty"`
-	ResponseFormat string `json:"response_format,omitempty"`
-	Stream         bool   `json:"stream,omitempty"`
-}
-
-// ImageGenerationResponse is an OpenAI-compatible image generation response.
-type ImageGenerationResponse struct {
-	Created int64       `json:"created"`
-	Data    []ImageData `json:"data"`
-}
-
-// ImageData contains the generated image data.
-type ImageData struct {
-	URL           string `json:"url,omitempty"`
-	B64JSON       string `json:"b64_json,omitempty"`
-	RevisedPrompt string `json:"revised_prompt,omitempty"`
-}
-
-// ImageProgressEvent is sent during streaming to indicate generation progress.
-type ImageProgressEvent struct {
-	Step  int `json:"step"`
-	Total int `json:"total"`
-}

x/imagegen/cache/teacache.go

@@ -0,0 +1,197 @@
+//go:build mlx
+
+// Package cache provides caching mechanisms for diffusion model inference.
+package cache
+
+import (
+	"github.com/ollama/ollama/x/imagegen/mlx"
+)
+
+// TeaCache implements Timestep Embedding Aware Caching for diffusion models.
+// It caches the transformer output and reuses it when timestep values
+// are similar between consecutive steps.
+//
+// For CFG (classifier-free guidance), it caches pos and neg predictions
+// separately and always computes CFG fresh to avoid error amplification.
+//
+// Reference: "Timestep Embedding Tells: It's Time to Cache for Video Diffusion Model"
+// https://github.com/ali-vilab/TeaCache
+type TeaCache struct {
+	// Cached transformer output from last computed step (non-CFG mode)
+	cachedOutput *mlx.Array
+
+	// Cached CFG outputs (pos and neg separately)
+	cachedPosOutput *mlx.Array
+	cachedNegOutput *mlx.Array
+
+	// Previous timestep value for difference calculation
+	prevTimestep float32
+
+	// Accumulated difference for rescaling
+	accumulatedDiff float32
+
+	// Configuration
+	threshold      float32 // Threshold for recomputation decision
+	rescaleFactor  float32 // Model-specific rescaling factor
+	skipEarlySteps int     // Number of early steps to never cache
+
+	// Statistics
+	cacheHits   int
+	cacheMisses int
+}
+
+// TeaCacheConfig holds configuration for TeaCache.
+type TeaCacheConfig struct {
+	// Threshold for recomputation. Lower = more cache hits, potential quality loss.
+	// Recommended: 0.05-0.15 for image models
+	Threshold float32
+
+	// Rescale factor to adjust timestep embedding differences.
+	// Model-specific, typically 1.0-2.0
+	RescaleFactor float32
+
+	// SkipEarlySteps: number of early steps to always compute (never cache).
+	// Set to 2-3 for CFG mode to preserve structure. 0 = no skipping.
+	SkipEarlySteps int
+}
+
+// DefaultTeaCacheConfig returns default configuration for TeaCache.
+func DefaultTeaCacheConfig() *TeaCacheConfig {
+	return &TeaCacheConfig{
+		Threshold:     0.1,
+		RescaleFactor: 1.0,
+	}
+}
+
+// NewTeaCache creates a new TeaCache instance.
+func NewTeaCache(cfg *TeaCacheConfig) *TeaCache {
+	if cfg == nil {
+		cfg = DefaultTeaCacheConfig()
+	}
+	return &TeaCache{
+		threshold:      cfg.Threshold,
+		rescaleFactor:  cfg.RescaleFactor,
+		skipEarlySteps: cfg.SkipEarlySteps,
+	}
+}
+
+// ShouldCompute determines if we should compute the full forward pass
+// or reuse the cached output based on timestep similarity.
+//
+// Algorithm:
+// 1. First step always computes
+// 2. Subsequent steps compare |currTimestep - prevTimestep| * rescaleFactor
+// 3. If accumulated difference > threshold, compute new output
+// 4. Otherwise, reuse cached output
+func (tc *TeaCache) ShouldCompute(step int, timestep float32) bool {
+	// Always compute early steps (critical for structure)
+	// Check both regular cache and CFG cache
+	hasCachedOutput := tc.cachedOutput != nil || tc.HasCFGCache()
+	if step < tc.skipEarlySteps || step == 0 || !hasCachedOutput {
+		return true
+	}
+
+	// Compute absolute difference between current and previous timestep
+	diff := timestep - tc.prevTimestep
+	if diff < 0 {
+		diff = -diff
+	}
+
+	// Apply rescaling factor
+	scaledDiff := diff * tc.rescaleFactor
+
+	// Accumulate difference (helps track drift over multiple cached steps)
+	tc.accumulatedDiff += scaledDiff
+
+	// Decision based on accumulated difference
+	if tc.accumulatedDiff > tc.threshold {
+		tc.accumulatedDiff = 0 // Reset accumulator
+		return true
+	}
+
+	return false
+}
+
+// UpdateCache stores the computed output for potential reuse (non-CFG mode).
+func (tc *TeaCache) UpdateCache(output *mlx.Array, timestep float32) {
+	// Free previous cached output
+	if tc.cachedOutput != nil {
+		tc.cachedOutput.Free()
+	}
+
+	// Store new cached values
+	tc.cachedOutput = output
+	tc.prevTimestep = timestep
+	tc.cacheMisses++
+}
+
+// UpdateCFGCache stores pos and neg outputs separately for CFG mode.
+// This allows CFG to be computed fresh each step, avoiding error amplification.
+func (tc *TeaCache) UpdateCFGCache(posOutput, negOutput *mlx.Array, timestep float32) {
+	// Free previous cached outputs
+	if tc.cachedPosOutput != nil {
+		tc.cachedPosOutput.Free()
+	}
+	if tc.cachedNegOutput != nil {
+		tc.cachedNegOutput.Free()
+	}
+
+	// Store new cached values
+	tc.cachedPosOutput = posOutput
+	tc.cachedNegOutput = negOutput
+	tc.prevTimestep = timestep
+	tc.cacheMisses++
+}
+
+// GetCached returns the cached output (non-CFG mode).
+func (tc *TeaCache) GetCached() *mlx.Array {
+	tc.cacheHits++
+	return tc.cachedOutput
+}
+
+// GetCFGCached returns cached pos and neg outputs for CFG mode.
+func (tc *TeaCache) GetCFGCached() (pos, neg *mlx.Array) {
+	tc.cacheHits++
+	return tc.cachedPosOutput, tc.cachedNegOutput
+}
+
+// HasCFGCache returns true if CFG cache is available.
+func (tc *TeaCache) HasCFGCache() bool {
+	return tc.cachedPosOutput != nil && tc.cachedNegOutput != nil
+}
+
+// Arrays returns all arrays that should be kept alive.
+func (tc *TeaCache) Arrays() []*mlx.Array {
+	var arrays []*mlx.Array
+	if tc.cachedOutput != nil {
+		arrays = append(arrays, tc.cachedOutput)
+	}
+	if tc.cachedPosOutput != nil {
+		arrays = append(arrays, tc.cachedPosOutput)
+	}
+	if tc.cachedNegOutput != nil {
+		arrays = append(arrays, tc.cachedNegOutput)
+	}
+	return arrays
+}
+
+// Stats returns cache hit/miss statistics.
+func (tc *TeaCache) Stats() (hits, misses int) {
+	return tc.cacheHits, tc.cacheMisses
+}
+
+// Free releases all cached arrays.
+func (tc *TeaCache) Free() {
+	if tc.cachedOutput != nil {
+		tc.cachedOutput.Free()
+		tc.cachedOutput = nil
+	}
+	if tc.cachedPosOutput != nil {
+		tc.cachedPosOutput.Free()
+		tc.cachedPosOutput = nil
+	}
+	if tc.cachedNegOutput != nil {
+		tc.cachedNegOutput.Free()
+		tc.cachedNegOutput = nil
+	}
+}

x/imagegen/cli.go

@@ -7,7 +7,6 @@ package imagegen
 
 import (
 	"encoding/base64"
-	"encoding/json"
 	"errors"
 	"fmt"
 	"io"
@@ -39,77 +38,20 @@ func DefaultOptions() ImageGenOptions {
 	return ImageGenOptions{
 		Width:  1024,
 		Height: 1024,
-		Steps:  9,
+		Steps:  0, // 0 means model default
 		Seed:   0, // 0 means random
 	}
 }
 
-// Show displays information about an image generation model.
-func Show(modelName string, w io.Writer) error {
-	manifest, err := LoadManifest(modelName)
-	if err != nil {
-		return fmt.Errorf("failed to load manifest: %w", err)
-	}
-
-	// Count total size
-	var totalSize int64
-	for _, layer := range manifest.Manifest.Layers {
-		if layer.MediaType == "application/vnd.ollama.image.tensor" {
-			totalSize += layer.Size
-		}
-	}
-
-	// Read model_index.json for architecture
-	var architecture string
-	if data, err := manifest.ReadConfig("model_index.json"); err == nil {
-		var index struct {
-			Architecture string `json:"architecture"`
-		}
-		if json.Unmarshal(data, &index) == nil {
-			architecture = index.Architecture
-		}
-	}
-
-	// Estimate parameter count from total size (assuming BF16 = 2 bytes per param)
-	paramCount := totalSize / 2
-	paramStr := formatParamCount(paramCount)
-
-	// Print Model info
-	fmt.Fprintln(w, "  Model")
-	if architecture != "" {
-		fmt.Fprintf(w, "    %-20s %s\n", "architecture", architecture)
-	}
-	fmt.Fprintf(w, "    %-20s %s\n", "parameters", paramStr)
-	fmt.Fprintf(w, "    %-20s %s\n", "quantization", "BF16")
-	fmt.Fprintln(w)
-
-	// Print Capabilities
-	fmt.Fprintln(w, "  Capabilities")
-	fmt.Fprintf(w, "    %s\n", "image")
-	fmt.Fprintln(w)
-
-	return nil
-}
-
-// formatParamCount formats parameter count as human-readable string.
-func formatParamCount(count int64) string {
-	if count >= 1_000_000_000 {
-		return fmt.Sprintf("%.1fB", float64(count)/1_000_000_000)
-	}
-	if count >= 1_000_000 {
-		return fmt.Sprintf("%.1fM", float64(count)/1_000_000)
-	}
-	return fmt.Sprintf("%d", count)
-}
-
 // RegisterFlags adds image generation flags to the given command.
 // Flags are hidden since they only apply to image generation models.
 func RegisterFlags(cmd *cobra.Command) {
 	cmd.Flags().Int("width", 1024, "Image width")
 	cmd.Flags().Int("height", 1024, "Image height")
-	cmd.Flags().Int("steps", 9, "Denoising steps")
+	cmd.Flags().Int("steps", 0, "Denoising steps (0 = model default)")
 	cmd.Flags().Int("seed", 0, "Random seed (0 for random)")
 	cmd.Flags().String("negative", "", "Negative prompt")
+	// Hide from main flags section - shown in separate section via AppendFlagsDocs
 	cmd.Flags().MarkHidden("width")
 	cmd.Flags().MarkHidden("height")
 	cmd.Flags().MarkHidden("steps")
@@ -117,15 +59,23 @@ func RegisterFlags(cmd *cobra.Command) {
 	cmd.Flags().MarkHidden("negative")
 }
 
+// AppendFlagsDocs appends image generation flags documentation to the command's usage template.
+func AppendFlagsDocs(cmd *cobra.Command) {
+	usage := `
+Image Generation Flags (experimental):
+      --width int      Image width
+      --height int     Image height
+      --steps int      Denoising steps
+      --seed int       Random seed
+      --negative str   Negative prompt
+`
+	cmd.SetUsageTemplate(cmd.UsageTemplate() + usage)
+}
+
 // RunCLI handles the CLI for image generation models.
 // Returns true if it handled the request, false if the caller should continue with normal flow.
 // Supports flags: --width, --height, --steps, --seed, --negative
 func RunCLI(cmd *cobra.Command, name string, prompt string, interactive bool, keepAlive *api.Duration) error {
-	// Verify it's a valid image gen model
-	if ResolveModelName(name) == "" {
-		return fmt.Errorf("unknown image generation model: %s", name)
-	}
-
 	// Get options from flags (with env var defaults)
 	opts := DefaultOptions()
 	if cmd != nil && cmd.Flags() != nil {
@@ -161,17 +111,15 @@ func generateImageWithOptions(cmd *cobra.Command, modelName, prompt string, keep
 		return err
 	}
 
-	// Build request with image gen options encoded in Options fields
-	// NumCtx=width, NumGPU=height, NumPredict=steps, Seed=seed
 	req := &api.GenerateRequest{
 		Model:  modelName,
 		Prompt: prompt,
-		Options: map[string]any{
-			"num_ctx":     opts.Width,
-			"num_gpu":     opts.Height,
-			"num_predict": opts.Steps,
-			"seed":        opts.Seed,
-		},
+		Width:  int32(opts.Width),
+		Height: int32(opts.Height),
+		Steps:  int32(opts.Steps),
+	}
+	if opts.Seed != 0 {
+		req.Options = map[string]any{"seed": opts.Seed}
 	}
 	if keepAlive != nil {
 		req.KeepAlive = keepAlive
@@ -183,44 +131,52 @@ func generateImageWithOptions(cmd *cobra.Command, modelName, prompt string, keep
 	p.Add("", spinner)
 
 	var stepBar *progress.StepBar
-	var imagePath string
-
+	var imageBase64 string
 	err = client.Generate(cmd.Context(), req, func(resp api.GenerateResponse) error {
-		content := resp.Response
-
-		// Handle progress updates - parse step info and switch to step bar
-		if strings.HasPrefix(content, "\rGenerating:") {
-			var step, total int
-			fmt.Sscanf(content, "\rGenerating: step %d/%d", &step, &total)
-			if stepBar == nil && total > 0 {
+		// Handle progress updates using structured fields
+		if resp.Total > 0 {
+			if stepBar == nil {
 				spinner.Stop()
-				stepBar = progress.NewStepBar("Generating", total)
+				stepBar = progress.NewStepBar("Generating", int(resp.Total))
 				p.Add("", stepBar)
 			}
-			if stepBar != nil {
-				stepBar.Set(step)
-			}
-			return nil
+			stepBar.Set(int(resp.Completed))
 		}
 
-		// Handle final response with image path
-		if resp.Done && strings.Contains(content, "Image saved to:") {
-			if idx := strings.Index(content, "Image saved to: "); idx >= 0 {
-				imagePath = strings.TrimSpace(content[idx+16:])
-			}
+		// Handle final response with image data
+		if resp.Done && resp.Image != "" {
+			imageBase64 = resp.Image
 		}
 
 		return nil
 	})
 
-	p.Stop()
+	p.StopAndClear()
 	if err != nil {
 		return err
 	}
 
-	if imagePath != "" {
-		displayImageInTerminal(imagePath)
-		fmt.Printf("Image saved to: %s\n", imagePath)
+	if imageBase64 != "" {
+		// Decode base64 and save to CWD
+		imageData, err := base64.StdEncoding.DecodeString(imageBase64)
+		if err != nil {
+			return fmt.Errorf("failed to decode image: %w", err)
+		}
+
+		// Create filename from prompt
+		safeName := sanitizeFilename(prompt)
+		if len(safeName) > 50 {
+			safeName = safeName[:50]
+		}
+		timestamp := time.Now().Format("20060102-150405")
+		filename := fmt.Sprintf("%s-%s.png", safeName, timestamp)
+
+		if err := os.WriteFile(filename, imageData, 0o644); err != nil {
+			return fmt.Errorf("failed to save image: %w", err)
+		}
+
+		displayImageInTerminal(filename)
+		fmt.Printf("Image saved to: %s\n", filename)
 	}
 
 	return nil
@@ -233,6 +189,23 @@ func runInteractive(cmd *cobra.Command, modelName string, keepAlive *api.Duratio
 		return err
 	}
 
+	// Preload the model with the specified keepalive
+	p := progress.NewProgress(os.Stderr)
+	spinner := progress.NewSpinner("")
+	p.Add("", spinner)
+
+	preloadReq := &api.GenerateRequest{
+		Model:     modelName,
+		KeepAlive: keepAlive,
+	}
+	if err := client.Generate(cmd.Context(), preloadReq, func(resp api.GenerateResponse) error {
+		return nil
+	}); err != nil {
+		p.StopAndClear()
+		return fmt.Errorf("failed to load model: %w", err)
+	}
+	p.StopAndClear()
+
 	scanner, err := readline.New(readline.Prompt{
 		Prompt:      ">>> ",
 		Placeholder: "Describe an image to generate (/help for commands)",
@@ -270,7 +243,7 @@ func runInteractive(cmd *cobra.Command, modelName string, keepAlive *api.Duratio
 		case strings.HasPrefix(line, "/bye"):
 			return nil
 		case strings.HasPrefix(line, "/?"), strings.HasPrefix(line, "/help"):
-			printInteractiveHelp(opts)
+			printInteractiveHelp()
 			continue
 		case strings.HasPrefix(line, "/set "):
 			if err := handleSetCommand(line[5:], &opts); err != nil {
@@ -289,12 +262,12 @@ func runInteractive(cmd *cobra.Command, modelName string, keepAlive *api.Duratio
 		req := &api.GenerateRequest{
 			Model:  modelName,
 			Prompt: line,
-			Options: map[string]any{
-				"num_ctx":     opts.Width,
-				"num_gpu":     opts.Height,
-				"num_predict": opts.Steps,
-				"seed":        opts.Seed,
-			},
+			Width:  int32(opts.Width),
+			Height: int32(opts.Height),
+			Steps:  int32(opts.Steps),
+		}
+		if opts.Seed != 0 {
+			req.Options = map[string]any{"seed": opts.Seed}
 		}
 		if keepAlive != nil {
 			req.KeepAlive = keepAlive
@@ -306,61 +279,57 @@ func runInteractive(cmd *cobra.Command, modelName string, keepAlive *api.Duratio
 		p.Add("", spinner)
 
 		var stepBar *progress.StepBar
-		var imagePath string
+		var imageBase64 string
 
 		err = client.Generate(cmd.Context(), req, func(resp api.GenerateResponse) error {
-			content := resp.Response
-
-			// Handle progress updates - parse step info and switch to step bar
-			if strings.HasPrefix(content, "\rGenerating:") {
-				var step, total int
-				fmt.Sscanf(content, "\rGenerating: step %d/%d", &step, &total)
-				if stepBar == nil && total > 0 {
+			// Handle progress updates using structured fields
+			if resp.Total > 0 {
+				if stepBar == nil {
 					spinner.Stop()
-					stepBar = progress.NewStepBar("Generating", total)
+					stepBar = progress.NewStepBar("Generating", int(resp.Total))
 					p.Add("", stepBar)
 				}
-				if stepBar != nil {
-					stepBar.Set(step)
-				}
-				return nil
+				stepBar.Set(int(resp.Completed))
 			}
 
-			// Handle final response with image path
-			if resp.Done && strings.Contains(content, "Image saved to:") {
-				if idx := strings.Index(content, "Image saved to: "); idx >= 0 {
-					imagePath = strings.TrimSpace(content[idx+16:])
-				}
+			// Handle final response with image data
+			if resp.Done && resp.Image != "" {
+				imageBase64 = resp.Image
 			}
 
 			return nil
 		})
 
-		p.Stop()
+		p.StopAndClear()
 		if err != nil {
 			fmt.Fprintf(os.Stderr, "Error: %v\n", err)
 			continue
 		}
 
-		// Copy image to current directory with descriptive name
-		if imagePath != "" {
+		// Save image to current directory with descriptive name
+		if imageBase64 != "" {
+			// Decode base64 image data
+			imageData, err := base64.StdEncoding.DecodeString(imageBase64)
+			if err != nil {
+				fmt.Fprintf(os.Stderr, "Error decoding image: %v\n", err)
+				continue
+			}
+
 			// Create filename from prompt (sanitized)
 			safeName := sanitizeFilename(line)
 			if len(safeName) > 50 {
 				safeName = safeName[:50]
 			}
 			timestamp := time.Now().Format("20060102-150405")
-			newName := fmt.Sprintf("%s-%s.png", safeName, timestamp)
+			filename := fmt.Sprintf("%s-%s.png", safeName, timestamp)
 
-			// Copy file to CWD
-			if err := copyFile(imagePath, newName); err != nil {
-				fmt.Fprintf(os.Stderr, "Error saving to current directory: %v\n", err)
-				displayImageInTerminal(imagePath)
-				fmt.Printf("Image saved to: %s\n", imagePath)
-			} else {
-				displayImageInTerminal(newName)
-				fmt.Printf("Image saved to: %s\n", newName)
+			if err := os.WriteFile(filename, imageData, 0o644); err != nil {
+				fmt.Fprintf(os.Stderr, "Error saving image: %v\n", err)
+				continue
 			}
+
+			displayImageInTerminal(filename)
+			fmt.Printf("Image saved to: %s\n", filename)
 		}
 
 		fmt.Println()
@@ -381,31 +350,14 @@ func sanitizeFilename(s string) string {
 	return result.String()
 }
 
-// copyFile copies a file from src to dst.
-func copyFile(src, dst string) error {
-	sourceFile, err := os.Open(src)
-	if err != nil {
-		return err
-	}
-	defer sourceFile.Close()
-
-	destFile, err := os.Create(dst)
-	if err != nil {
-		return err
-	}
-	defer destFile.Close()
-
-	_, err = io.Copy(destFile, sourceFile)
-	return err
-}
-
 // printInteractiveHelp prints help for interactive mode commands.
-func printInteractiveHelp(opts ImageGenOptions) {
+// TODO: reconcile /set commands with /set parameter in text gen REPL (cmd/cmd.go)
+func printInteractiveHelp() {
 	fmt.Fprintln(os.Stderr, "Commands:")
-	fmt.Fprintln(os.Stderr, "  /set width <n>     Set image width (current:", opts.Width, ")")
-	fmt.Fprintln(os.Stderr, "  /set height <n>    Set image height (current:", opts.Height, ")")
-	fmt.Fprintln(os.Stderr, "  /set steps <n>     Set denoising steps (current:", opts.Steps, ")")
-	fmt.Fprintln(os.Stderr, "  /set seed <n>      Set random seed (current:", opts.Seed, ", 0=random)")
+	fmt.Fprintln(os.Stderr, "  /set width <n>     Set image width")
+	fmt.Fprintln(os.Stderr, "  /set height <n>    Set image height")
+	fmt.Fprintln(os.Stderr, "  /set steps <n>     Set denoising steps")
+	fmt.Fprintln(os.Stderr, "  /set seed <n>      Set random seed")
 	fmt.Fprintln(os.Stderr, "  /set negative <s>  Set negative prompt")
 	fmt.Fprintln(os.Stderr, "  /show              Show current settings")
 	fmt.Fprintln(os.Stderr, "  /bye               Exit")
@@ -509,10 +461,7 @@ func displayImageInTerminal(imagePath string) bool {
 		// Send in chunks for large images
 		const chunkSize = 4096
 		for i := 0; i < len(encoded); i += chunkSize {
-			end := i + chunkSize
-			if end > len(encoded) {
-				end = len(encoded)
-			}
+			end := min(i+chunkSize, len(encoded))
 			chunk := encoded[i:end]
 
 			if i == 0 {

x/imagegen/client/create.go

@@ -1,130 +0,0 @@
-// Package client provides client-side model creation for tensor-based models.
-//
-// This package is in x/ because the tensor model storage format is under development.
-// It also exists to break an import cycle: server imports x/imagegen, so x/imagegen
-// cannot import server. This sub-package can import server because server doesn't
-// import it.
-//
-// TODO (jmorganca): This is temporary. When tensor models are promoted to production:
-//  1. Add proper API endpoints for tensor model creation
-//  2. Move tensor extraction to server-side
-//  3. Remove this package
-//  4. Follow the same client→server pattern as regular model creation
-package client
-
-import (
-	"bytes"
-	"encoding/json"
-	"fmt"
-	"io"
-
-	"github.com/ollama/ollama/progress"
-	"github.com/ollama/ollama/server"
-	"github.com/ollama/ollama/types/model"
-	"github.com/ollama/ollama/x/imagegen"
-)
-
-// MinOllamaVersion is the minimum Ollama version required for image generation models.
-const MinOllamaVersion = "0.14.0"
-
-// CreateModel imports a tensor-based model from a local directory.
-// This creates blobs and manifest directly on disk, bypassing the HTTP API.
-//
-// TODO (jmorganca): Replace with API-based creation when promoted to production.
-func CreateModel(modelName, modelDir string, p *progress.Progress) error {
-	if !imagegen.IsTensorModelDir(modelDir) {
-		return fmt.Errorf("%s is not an image generation model directory (model_index.json not found)", modelDir)
-	}
-
-	status := "importing image generation model"
-	spinner := progress.NewSpinner(status)
-	p.Add("imagegen", spinner)
-
-	// Create layer callback for config files
-	createLayer := func(r io.Reader, mediaType, name string) (imagegen.LayerInfo, error) {
-		layer, err := server.NewLayer(r, mediaType)
-		if err != nil {
-			return imagegen.LayerInfo{}, err
-		}
-		layer.Name = name
-
-		return imagegen.LayerInfo{
-			Digest:    layer.Digest,
-			Size:      layer.Size,
-			MediaType: layer.MediaType,
-			Name:      name,
-		}, nil
-	}
-
-	// Create tensor layer callback for individual tensors
-	// name is path-style: "component/tensor_name"
-	createTensorLayer := func(r io.Reader, name, dtype string, shape []int32) (imagegen.LayerInfo, error) {
-		layer, err := server.NewLayer(r, server.MediaTypeImageTensor)
-		if err != nil {
-			return imagegen.LayerInfo{}, err
-		}
-		layer.Name = name
-
-		return imagegen.LayerInfo{
-			Digest:    layer.Digest,
-			Size:      layer.Size,
-			MediaType: layer.MediaType,
-			Name:      name,
-		}, nil
-	}
-
-	// Create manifest writer callback
-	writeManifest := func(modelName string, config imagegen.LayerInfo, layers []imagegen.LayerInfo) error {
-		name := model.ParseName(modelName)
-		if !name.IsValid() {
-			return fmt.Errorf("invalid model name: %s", modelName)
-		}
-
-		// Create a proper config blob with version requirement
-		configData := model.ConfigV2{
-			ModelFormat:  "safetensors",
-			Capabilities: []string{"image"},
-			Requires:     MinOllamaVersion,
-		}
-		configJSON, err := json.Marshal(configData)
-		if err != nil {
-			return fmt.Errorf("failed to marshal config: %w", err)
-		}
-
-		// Create config layer blob
-		configLayer, err := server.NewLayer(bytes.NewReader(configJSON), "application/vnd.docker.container.image.v1+json")
-		if err != nil {
-			return fmt.Errorf("failed to create config layer: %w", err)
-		}
-
-		// Convert LayerInfo to server.Layer (include the original model_index.json in layers)
-		serverLayers := make([]server.Layer, len(layers))
-		for i, l := range layers {
-			serverLayers[i] = server.Layer{
-				MediaType: l.MediaType,
-				Digest:    l.Digest,
-				Size:      l.Size,
-				Name:      l.Name,
-			}
-		}
-
-		return server.WriteManifest(name, configLayer, serverLayers)
-	}
-
-	// Progress callback
-	progressFn := func(msg string) {
-		spinner.Stop()
-		status = msg
-		spinner = progress.NewSpinner(status)
-		p.Add("imagegen", spinner)
-	}
-
-	err := imagegen.CreateModel(modelName, modelDir, createLayer, createTensorLayer, writeManifest, progressFn)
-	spinner.Stop()
-	if err != nil {
-		return err
-	}
-
-	fmt.Printf("Created image generation model '%s'\n", modelName)
-	return nil
-}

x/imagegen/cmd/engine/generate.go

@@ -65,12 +65,12 @@ func (s *utf8Streamer) Flush() string {
 	return result
 }
 
-func init() {
-	generationStream = mlx.NewStream()
-}
-
 // withStream runs fn with the generation stream as default
 func withStream(fn func()) {
+	// Lazy initialization of generationStream
+	if generationStream == nil {
+		generationStream = mlx.NewStream()
+	}
 	orig := mlx.GetDefaultStream()
 	mlx.SetDefaultStream(generationStream)
 	fn()

x/imagegen/cmd/engine/main.go

@@ -48,7 +48,7 @@ func main() {
 	// Image generation params
 	width := flag.Int("width", 1024, "Image width")
 	height := flag.Int("height", 1024, "Image height")
-	steps := flag.Int("steps", 9, "Denoising steps")
+	steps := flag.Int("steps", 0, "Denoising steps (0 = model default)")
 	seed := flag.Int64("seed", 42, "Random seed")
 	out := flag.String("output", "output.png", "Output path")
 
@@ -67,6 +67,9 @@ func main() {
 	flag.Var(&inputImages, "input-image", "Input image for image editing (can be specified multiple times)")
 	negativePrompt := flag.String("negative-prompt", "", "Negative prompt for CFG (empty = no CFG, matching Python)")
 	cfgScale := flag.Float64("cfg-scale", 4.0, "CFG scale for image editing")
+	teaCache := flag.Bool("teacache", false, "Enable TeaCache for faster inference")
+	teaCacheThreshold := flag.Float64("teacache-threshold", 0.1, "TeaCache threshold (lower = more aggressive caching)")
+	fusedQKV := flag.Bool("fused-qkv", false, "Enable fused QKV projection for faster attention")
 
 	flag.Parse()
 
@@ -75,6 +78,11 @@ func main() {
 		return
 	}
 
+	// Check if MLX initialized successfully
+	if !mlx.IsMLXAvailable() {
+		log.Fatalf("MLX initialization failed: %v", mlx.GetMLXInitError())
+	}
+
 	// CPU profiling
 	if *cpuProfile != "" {
 		f, err := os.Create(*cpuProfile)
@@ -99,13 +107,17 @@ func main() {
 		}
 		var img *mlx.Array
 		img, err = m.GenerateFromConfig(context.Background(), &zimage.GenerateConfig{
-			Prompt:      *prompt,
-			Width:       int32(*width),
-			Height:      int32(*height),
-			Steps:       *steps,
-			Seed:        *seed,
-			CapturePath: *gpuCapture,
-			LayerCache:  *layerCache,
+			Prompt:            *prompt,
+			NegativePrompt:    *negativePrompt,
+			CFGScale:          float32(*cfgScale),
+			Width:             int32(*width),
+			Height:            int32(*height),
+			Steps:             *steps,
+			Seed:              *seed,
+			CapturePath:       *gpuCapture,
+			TeaCache:          *teaCache,
+			TeaCacheThreshold: float32(*teaCacheThreshold),
+			FusedQKV:          *fusedQKV,
 		})
 		if err == nil {
 			err = saveImageArray(img, *out)

x/imagegen/image.go

@@ -60,9 +60,12 @@ func ArrayToImage(arr *mlx.Array) (*image.RGBA, error) {
 	}
 
 	// Transform to [H, W, C] for image conversion
-	img := mlx.Squeeze(arr, 0)
-	img = mlx.Transpose(img, 1, 2, 0)
-	img = mlx.Contiguous(img)
+	// Free intermediate arrays to avoid memory leak
+	squeezed := mlx.Squeeze(arr, 0)
+	transposed := mlx.Transpose(squeezed, 1, 2, 0)
+	squeezed.Free()
+	img := mlx.Contiguous(transposed)
+	transposed.Free()
 	mlx.Eval(img)
 
 	imgShape := img.Shape()

x/imagegen/manifest.go

@@ -175,3 +175,63 @@ func (m *ModelManifest) HasTensorLayers() bool {
 	}
 	return false
 }
+
+// ModelInfo contains metadata about an image generation model.
+type ModelInfo struct {
+	Architecture   string
+	ParameterCount int64
+	Quantization   string
+}
+
+// GetModelInfo returns metadata about an image generation model.
+func GetModelInfo(modelName string) (*ModelInfo, error) {
+	manifest, err := LoadManifest(modelName)
+	if err != nil {
+		return nil, fmt.Errorf("failed to load manifest: %w", err)
+	}
+
+	info := &ModelInfo{}
+
+	// Read model_index.json for architecture, parameter count, and quantization
+	if data, err := manifest.ReadConfig("model_index.json"); err == nil {
+		var index struct {
+			Architecture   string `json:"architecture"`
+			ParameterCount int64  `json:"parameter_count"`
+			Quantization   string `json:"quantization"`
+		}
+		if json.Unmarshal(data, &index) == nil {
+			info.Architecture = index.Architecture
+			info.ParameterCount = index.ParameterCount
+			info.Quantization = index.Quantization
+		}
+	}
+
+	// Fallback: detect quantization from tensor names if not in config
+	if info.Quantization == "" {
+		for _, layer := range manifest.Manifest.Layers {
+			if strings.HasSuffix(layer.Name, ".weight_scale") {
+				info.Quantization = "FP8"
+				break
+			}
+		}
+		if info.Quantization == "" {
+			info.Quantization = "BF16"
+		}
+	}
+
+	// Fallback: estimate parameter count if not in config
+	if info.ParameterCount == 0 {
+		var totalSize int64
+		for _, layer := range manifest.Manifest.Layers {
+			if layer.MediaType == "application/vnd.ollama.image.tensor" {
+				if !strings.HasSuffix(layer.Name, "_scale") && !strings.HasSuffix(layer.Name, "_qbias") {
+					totalSize += layer.Size
+				}
+			}
+		}
+		// Assume BF16 (2 bytes/param) as rough estimate
+		info.ParameterCount = totalSize / 2
+	}
+
+	return info, nil
+}

x/imagegen/memory.go

@@ -95,8 +95,3 @@ func EstimateVRAM(modelName string) uint64 {
 	}
 	return 21 * GB
 }
-
-// HasTensorLayers checks if the given model has tensor layers.
-func HasTensorLayers(modelName string) bool {
-	return ResolveModelName(modelName) != ""
-}

x/imagegen/memory_test.go

@@ -94,13 +94,6 @@ func TestEstimateVRAMDefault(t *testing.T) {
 	}
 }
 
-func TestHasTensorLayers(t *testing.T) {
-	// Non-existent model should return false
-	if HasTensorLayers("nonexistent-model") {
-		t.Error("HasTensorLayers() should return false for non-existent model")
-	}
-}
-
 func TestResolveModelName(t *testing.T) {
 	// Non-existent model should return empty string
 	result := ResolveModelName("nonexistent-model")

x/imagegen/mlx/compile.go

@@ -3,7 +3,7 @@
 package mlx
 
 /*
-#include "mlx/c/mlx.h"
+#include "mlx.h"
 #include <stdlib.h>
 
 // Forward declaration for Go callback

x/imagegen/mlx/doc.go

@@ -0,0 +1,6 @@
+//go:build mlx
+
+// Package mlx provides Go bindings for the MLX-C library with dynamic loading support.
+//
+//go:generate go run generate_wrappers.go ../../../build/_deps/mlx-c-src/mlx/c mlx.h mlx.c
+package mlx

x/imagegen/mlx/generate_wrappers.go

@@ -0,0 +1,439 @@
+//go:build ignore
+
+// This tool generates MLX-C dynamic loading wrappers.
+// Usage: go run generate_wrappers.go <mlx-c-include-dir> <output-header> [output-impl]
+package main
+
+import (
+	"bytes"
+	"flag"
+	"fmt"
+	"io/fs"
+	"os"
+	"path/filepath"
+	"regexp"
+	"strings"
+)
+
+type Function struct {
+	Name          string
+	ReturnType    string
+	Params        string
+	ParamNames    []string
+	NeedsARM64Guard bool
+}
+
+func findHeaders(directory string) ([]string, error) {
+	var headers []string
+	err := filepath.WalkDir(directory, func(path string, d fs.DirEntry, err error) error {
+		if err != nil {
+			return err
+		}
+		if !d.IsDir() && strings.HasSuffix(path, ".h") {
+			headers = append(headers, path)
+		}
+		return nil
+	})
+	return headers, err
+}
+
+func cleanContent(content string) string {
+	// Remove single-line comments
+	re := regexp.MustCompile(`//.*?\n`)
+	content = re.ReplaceAllString(content, "\n")
+
+	// Remove multi-line comments
+	re = regexp.MustCompile(`/\*.*?\*/`)
+	content = re.ReplaceAllString(content, "")
+
+	// Remove preprocessor directives (lines starting with #) - use multiline mode
+	re = regexp.MustCompile(`(?m)^\s*#.*?$`)
+	content = re.ReplaceAllString(content, "")
+
+	// Remove extern "C" { and } blocks more conservatively
+	// Only remove the extern "C" { line, not the content inside
+	re = regexp.MustCompile(`extern\s+"C"\s*\{\s*?\n`)
+	content = re.ReplaceAllString(content, "\n")
+	// Remove standalone closing braces that are not part of function declarations
+	re = regexp.MustCompile(`\n\s*\}\s*\n`)
+	content = re.ReplaceAllString(content, "\n")
+
+	// Collapse whitespace and newlines
+	re = regexp.MustCompile(`\s+`)
+	content = re.ReplaceAllString(content, " ")
+
+	return content
+}
+
+func extractParamNames(params string) []string {
+	if params == "" || strings.TrimSpace(params) == "void" {
+		return []string{}
+	}
+
+	var names []string
+
+	// Split by comma, but respect parentheses (for function pointers)
+	parts := splitParams(params)
+
+	// Remove array brackets
+	arrayBrackets := regexp.MustCompile(`\[.*?\]`)
+
+	// Function pointer pattern
+	funcPtrPattern := regexp.MustCompile(`\(\s*\*\s*(\w+)\s*\)`)
+
+	// Type keywords to skip
+	typeKeywords := map[string]bool{
+		"const":     true,
+		"struct":    true,
+		"unsigned":  true,
+		"signed":    true,
+		"long":      true,
+		"short":     true,
+		"int":       true,
+		"char":      true,
+		"float":     true,
+		"double":    true,
+		"void":      true,
+		"size_t":    true,
+		"uint8_t":   true,
+		"uint16_t":  true,
+		"uint32_t":  true,
+		"uint64_t":  true,
+		"int8_t":    true,
+		"int16_t":   true,
+		"int32_t":   true,
+		"int64_t":   true,
+		"intptr_t":  true,
+		"uintptr_t": true,
+	}
+
+	for _, part := range parts {
+		if part == "" {
+			continue
+		}
+
+		// Remove array brackets
+		part = arrayBrackets.ReplaceAllString(part, "")
+
+		// For function pointers like "void (*callback)(int)"
+		if matches := funcPtrPattern.FindStringSubmatch(part); len(matches) > 1 {
+			names = append(names, matches[1])
+			continue
+		}
+
+		// Regular parameter: last identifier
+		tokens := regexp.MustCompile(`\w+`).FindAllString(part, -1)
+		if len(tokens) > 0 {
+			// The last token is usually the parameter name
+			// Skip type keywords
+			for i := len(tokens) - 1; i >= 0; i-- {
+				if !typeKeywords[tokens[i]] {
+					names = append(names, tokens[i])
+					break
+				}
+			}
+		}
+	}
+
+	return names
+}
+
+func splitParams(params string) []string {
+	var parts []string
+	var current bytes.Buffer
+	depth := 0
+
+	for _, char := range params + "," {
+		switch char {
+		case '(':
+			depth++
+			current.WriteRune(char)
+		case ')':
+			depth--
+			current.WriteRune(char)
+		case ',':
+			if depth == 0 {
+				parts = append(parts, strings.TrimSpace(current.String()))
+				current.Reset()
+			} else {
+				current.WriteRune(char)
+			}
+		default:
+			current.WriteRune(char)
+		}
+	}
+
+	return parts
+}
+
+func parseFunctions(content string) []Function {
+	var functions []Function
+
+	// Match function declarations: return_type function_name(params);
+	// Matches both mlx_* and _mlx_* functions
+	pattern := regexp.MustCompile(`\b((?:const\s+)?(?:struct\s+)?[\w\s]+?[\*\s]*)\s+(_?mlx_\w+)\s*\(([^)]*(?:\([^)]*\)[^)]*)*)\)\s*;`)
+
+	matches := pattern.FindAllStringSubmatch(content, -1)
+	for _, match := range matches {
+		returnType := strings.TrimSpace(match[1])
+		funcName := strings.TrimSpace(match[2])
+		params := strings.TrimSpace(match[3])
+
+		// Skip if this looks like a variable declaration
+		if params == "" || strings.Contains(params, "{") {
+			continue
+		}
+
+		// Clean up return type
+		returnType = strings.Join(strings.Fields(returnType), " ")
+
+		// Extract parameter names
+		paramNames := extractParamNames(params)
+
+		// Check if ARM64 guard is needed
+		needsGuard := needsARM64Guard(funcName, returnType, params)
+
+		functions = append(functions, Function{
+			Name:           funcName,
+			ReturnType:     returnType,
+			Params:         params,
+			ParamNames:     paramNames,
+			NeedsARM64Guard: needsGuard,
+		})
+	}
+
+	return functions
+}
+
+func needsARM64Guard(name, retType, params string) bool {
+	return strings.Contains(name, "float16") ||
+		strings.Contains(name, "bfloat16") ||
+		strings.Contains(retType, "float16_t") ||
+		strings.Contains(retType, "bfloat16_t") ||
+		strings.Contains(params, "float16_t") ||
+		strings.Contains(params, "bfloat16_t")
+}
+
+func generateWrapperFiles(functions []Function, headerPath, implPath string) error {
+	// Generate header file
+	var headerBuf bytes.Buffer
+
+	headerBuf.WriteString("// AUTO-GENERATED by generate_wrappers.go - DO NOT EDIT\n")
+	headerBuf.WriteString("// This file provides wrapper declarations for MLX-C functions that use dlopen/dlsym\n")
+	headerBuf.WriteString("//\n")
+	headerBuf.WriteString("// Strategy: Include MLX-C headers for type definitions, then provide wrapper\n")
+	headerBuf.WriteString("// functions that shadow the originals, allowing Go code to call them directly (e.g., C.mlx_add).\n")
+	headerBuf.WriteString("// Function pointers are defined in mlx.c (single compilation unit).\n\n")
+	headerBuf.WriteString("#ifndef MLX_WRAPPERS_H\n")
+	headerBuf.WriteString("#define MLX_WRAPPERS_H\n\n")
+
+	headerBuf.WriteString("// Include MLX headers for type definitions and original declarations\n")
+	headerBuf.WriteString("#include \"mlx/c/mlx.h\"\n")
+	headerBuf.WriteString("#include \"mlx_dynamic.h\"\n")
+	headerBuf.WriteString("#include <stdio.h>\n\n")
+
+	// Undef all MLX functions to avoid conflicts
+	headerBuf.WriteString("// Undefine any existing MLX function macros\n")
+	for _, fn := range functions {
+		headerBuf.WriteString(fmt.Sprintf("#undef %s\n", fn.Name))
+	}
+	headerBuf.WriteString("\n")
+
+	// Function pointer extern declarations
+	headerBuf.WriteString("// Function pointer declarations (defined in mlx.c, loaded via dlsym)\n")
+	for _, fn := range functions {
+		if fn.NeedsARM64Guard {
+			headerBuf.WriteString("#if defined(__aarch64__) || defined(_M_ARM64)\n")
+		}
+		headerBuf.WriteString(fmt.Sprintf("extern %s (*%s_ptr)(%s);\n", fn.ReturnType, fn.Name, fn.Params))
+		if fn.NeedsARM64Guard {
+			headerBuf.WriteString("#endif\n")
+		}
+	}
+	headerBuf.WriteString("\n")
+
+	// Initialization function declaration
+	headerBuf.WriteString("// Initialize all function pointers via dlsym (defined in mlx.c)\n")
+	headerBuf.WriteString("int mlx_load_functions(void* handle);\n\n")
+
+	// Wrapper function declarations
+	headerBuf.WriteString("// Wrapper function declarations that call through function pointers\n")
+	headerBuf.WriteString("// Go code calls these directly as C.mlx_* (no #define redirection needed)\n")
+	for _, fn := range functions {
+		if fn.NeedsARM64Guard {
+			headerBuf.WriteString("#if defined(__aarch64__) || defined(_M_ARM64)\n")
+		}
+		headerBuf.WriteString(fmt.Sprintf("%s %s(%s);\n", fn.ReturnType, fn.Name, fn.Params))
+		if fn.NeedsARM64Guard {
+			headerBuf.WriteString("#endif\n")
+		}
+		headerBuf.WriteString("\n")
+	}
+
+	headerBuf.WriteString("#endif // MLX_WRAPPERS_H\n")
+
+	// Write header file
+	if err := os.WriteFile(headerPath, headerBuf.Bytes(), 0644); err != nil {
+		return fmt.Errorf("failed to write header file: %w", err)
+	}
+
+	// Generate implementation file
+	var implBuf bytes.Buffer
+
+	implBuf.WriteString("// AUTO-GENERATED by generate_wrappers.go - DO NOT EDIT\n")
+	implBuf.WriteString("// This file contains the function pointer definitions and initialization\n")
+	implBuf.WriteString("// All function pointers are in a single compilation unit to avoid duplication\n\n")
+
+	implBuf.WriteString("#include \"mlx/c/mlx.h\"\n")
+	implBuf.WriteString("#include \"mlx_dynamic.h\"\n")
+	implBuf.WriteString("#include <stdio.h>\n")
+	implBuf.WriteString("#include <dlfcn.h>\n\n")
+
+	// Function pointer definitions
+	implBuf.WriteString("// Function pointer definitions\n")
+	for _, fn := range functions {
+		if fn.NeedsARM64Guard {
+			implBuf.WriteString("#if defined(__aarch64__) || defined(_M_ARM64)\n")
+		}
+		implBuf.WriteString(fmt.Sprintf("%s (*%s_ptr)(%s) = NULL;\n", fn.ReturnType, fn.Name, fn.Params))
+		if fn.NeedsARM64Guard {
+			implBuf.WriteString("#endif\n")
+		}
+	}
+	implBuf.WriteString("\n")
+
+	// Initialization function
+	implBuf.WriteString("// Initialize all function pointers via dlsym\n")
+	implBuf.WriteString("int mlx_load_functions(void* handle) {\n")
+	implBuf.WriteString("    if (handle == NULL) {\n")
+	implBuf.WriteString("        fprintf(stderr, \"MLX: Invalid library handle\\n\");\n")
+	implBuf.WriteString("        return -1;\n")
+	implBuf.WriteString("    }\n\n")
+
+	for _, fn := range functions {
+		if fn.NeedsARM64Guard {
+			implBuf.WriteString("#if defined(__aarch64__) || defined(_M_ARM64)\n")
+		}
+		implBuf.WriteString(fmt.Sprintf("    %s_ptr = dlsym(handle, \"%s\");\n", fn.Name, fn.Name))
+		implBuf.WriteString(fmt.Sprintf("    if (%s_ptr == NULL) {\n", fn.Name))
+		implBuf.WriteString(fmt.Sprintf("        fprintf(stderr, \"MLX: Failed to load symbol: %s\\n\");\n", fn.Name))
+		implBuf.WriteString("        return -1;\n")
+		implBuf.WriteString("    }\n")
+		if fn.NeedsARM64Guard {
+			implBuf.WriteString("#endif\n")
+		}
+	}
+
+	implBuf.WriteString("    return 0;\n")
+	implBuf.WriteString("}\n\n")
+
+	// Wrapper function implementations
+	implBuf.WriteString("// Wrapper function implementations that call through function pointers\n")
+	for _, fn := range functions {
+		if fn.NeedsARM64Guard {
+			implBuf.WriteString("#if defined(__aarch64__) || defined(_M_ARM64)\n")
+		}
+		implBuf.WriteString(fmt.Sprintf("%s %s(%s) {\n", fn.ReturnType, fn.Name, fn.Params))
+
+		// Call through function pointer
+		if fn.ReturnType != "void" {
+			implBuf.WriteString(fmt.Sprintf("    return %s_ptr(", fn.Name))
+		} else {
+			implBuf.WriteString(fmt.Sprintf("    %s_ptr(", fn.Name))
+		}
+
+		// Pass parameters
+		implBuf.WriteString(strings.Join(fn.ParamNames, ", "))
+		implBuf.WriteString(");\n")
+		implBuf.WriteString("}\n")
+		if fn.NeedsARM64Guard {
+			implBuf.WriteString("#endif\n")
+		}
+		implBuf.WriteString("\n")
+	}
+
+	// Write implementation file
+	if err := os.WriteFile(implPath, implBuf.Bytes(), 0644); err != nil {
+		return fmt.Errorf("failed to write implementation file: %w", err)
+	}
+
+	return nil
+}
+
+func main() {
+	flag.Usage = func() {
+		fmt.Fprintf(flag.CommandLine.Output(), "Usage: go run generate_wrappers.go <mlx-c-include-dir> <output-header> [output-impl]\n")
+		fmt.Fprintf(flag.CommandLine.Output(), "Generate MLX-C dynamic loading wrappers.\n\n")
+		flag.PrintDefaults()
+	}
+	flag.Parse()
+
+	args := flag.Args()
+	if len(args) < 2 {
+		fmt.Fprintf(flag.CommandLine.Output(), "ERROR: Missing required arguments\n\n")
+		flag.Usage()
+		os.Exit(1)
+	}
+
+	headerDir := args[0]
+	outputHeader := args[1]
+	// Default implementation file is same name with .c extension
+	outputImpl := outputHeader
+	if len(args) > 2 {
+		outputImpl = args[2]
+	} else if strings.HasSuffix(outputHeader, ".h") {
+		outputImpl = outputHeader[:len(outputHeader)-2] + ".c"
+	}
+
+	// Check if header directory exists
+	if _, err := os.Stat(headerDir); os.IsNotExist(err) {
+		fmt.Fprintf(os.Stderr, "ERROR: MLX-C headers directory not found at: %s\n\n", headerDir)
+		fmt.Fprintf(os.Stderr, "Please run CMake first to download MLX-C dependencies:\n")
+		fmt.Fprintf(os.Stderr, "  cmake -B build\n\n")
+		fmt.Fprintf(os.Stderr, "The CMake build will download and extract MLX-C headers needed for wrapper generation.\n")
+		os.Exit(1)
+	}
+
+	fmt.Fprintf(os.Stderr, "Parsing MLX-C headers from: %s\n", headerDir)
+
+	// Find all headers
+	headers, err := findHeaders(headerDir)
+	if err != nil {
+		fmt.Fprintf(os.Stderr, "ERROR: Failed to find header files: %v\n", err)
+		os.Exit(1)
+	}
+	fmt.Fprintf(os.Stderr, "Found %d header files\n", len(headers))
+
+	// Parse all headers
+	var allFunctions []Function
+	seen := make(map[string]bool)
+
+	for _, header := range headers {
+		content, err := os.ReadFile(header)
+		if err != nil {
+			fmt.Fprintf(os.Stderr, "Error reading %s: %v\n", header, err)
+			continue
+		}
+
+		cleaned := cleanContent(string(content))
+		functions := parseFunctions(cleaned)
+
+		// Deduplicate
+		for _, fn := range functions {
+			if !seen[fn.Name] {
+				seen[fn.Name] = true
+				allFunctions = append(allFunctions, fn)
+			}
+		}
+	}
+
+	fmt.Fprintf(os.Stderr, "Found %d unique function declarations\n", len(allFunctions))
+
+	// Generate wrapper files
+	if err := generateWrapperFiles(allFunctions, outputHeader, outputImpl); err != nil {
+		fmt.Fprintf(os.Stderr, "ERROR: Failed to generate wrapper files: %v\n", err)
+		os.Exit(1)
+	}
+
+	fmt.Fprintf(os.Stderr, "Generated %s and %s successfully\n", outputHeader, outputImpl)
+}

x/imagegen/mlx/mlx.go

@@ -3,12 +3,13 @@
 package mlx
 
 /*
-#cgo CFLAGS: -O3 -I${SRCDIR}/../../../build/_deps/mlx-c-src
-#cgo LDFLAGS: -L${SRCDIR}/../../../build/lib/ollama/ -lmlxc -Wl,-rpath,${SRCDIR}/../../../build/lib/ollama/
+#cgo CFLAGS: -O3 -I${SRCDIR}/../../../build/_deps/mlx-c-src -I${SRCDIR}
 #cgo darwin LDFLAGS: -lc++ -framework Metal -framework Foundation -framework Accelerate
-#cgo linux LDFLAGS: -lstdc++ -lcuda -lcudart -lnvrtc
+#cgo linux LDFLAGS: -lstdc++ -ldl
+#cgo windows LDFLAGS: -lstdc++
 
-#include "mlx/c/mlx.h"
+// Use generated wrappers instead of direct MLX headers
+#include "mlx.h"
 #include <stdlib.h>
 #include <stdint.h>
 #include <string.h>
@@ -42,192 +43,6 @@ static inline mlx_stream cpu_stream() {
 // CGO noescape/nocallback hints to reduce CGO overhead
 // noescape: pointers won't escape, no heap allocation needed
 // nocallback: function won't call back into Go
-#cgo noescape mlx_add
-#cgo nocallback mlx_add
-#cgo noescape mlx_subtract
-#cgo nocallback mlx_subtract
-#cgo noescape mlx_multiply
-#cgo nocallback mlx_multiply
-#cgo noescape mlx_divide
-#cgo nocallback mlx_divide
-#cgo noescape mlx_negative
-#cgo nocallback mlx_negative
-#cgo noescape mlx_abs
-#cgo nocallback mlx_abs
-#cgo noescape mlx_exp
-#cgo nocallback mlx_exp
-#cgo noescape mlx_log
-#cgo nocallback mlx_log
-#cgo noescape mlx_sqrt
-#cgo nocallback mlx_sqrt
-#cgo noescape mlx_rsqrt
-#cgo nocallback mlx_rsqrt
-#cgo noescape mlx_square
-#cgo nocallback mlx_square
-#cgo noescape mlx_power
-#cgo nocallback mlx_power
-#cgo noescape mlx_erf
-#cgo nocallback mlx_erf
-#cgo noescape mlx_sigmoid
-#cgo nocallback mlx_sigmoid
-#cgo noescape mlx_tanh
-#cgo nocallback mlx_tanh
-#cgo noescape mlx_sin
-#cgo nocallback mlx_sin
-#cgo noescape mlx_cos
-#cgo nocallback mlx_cos
-#cgo noescape mlx_maximum
-#cgo nocallback mlx_maximum
-#cgo noescape mlx_minimum
-#cgo nocallback mlx_minimum
-#cgo noescape mlx_clip
-#cgo nocallback mlx_clip
-#cgo noescape mlx_sum
-#cgo nocallback mlx_sum
-#cgo noescape mlx_sum_axis
-#cgo nocallback mlx_sum_axis
-#cgo noescape mlx_mean
-#cgo nocallback mlx_mean
-#cgo noescape mlx_mean_axis
-#cgo nocallback mlx_mean_axis
-#cgo noescape mlx_var_axis
-#cgo nocallback mlx_var_axis
-#cgo noescape mlx_argmax
-#cgo nocallback mlx_argmax
-#cgo noescape mlx_argmax_axis
-#cgo nocallback mlx_argmax_axis
-#cgo noescape mlx_softmax_axis
-#cgo nocallback mlx_softmax_axis
-#cgo noescape mlx_cumsum
-#cgo nocallback mlx_cumsum
-#cgo noescape mlx_matmul
-#cgo nocallback mlx_matmul
-#cgo noescape mlx_addmm
-#cgo nocallback mlx_addmm
-#cgo noescape mlx_gather_mm
-#cgo nocallback mlx_gather_mm
-#cgo noescape mlx_gather_qmm
-#cgo nocallback mlx_gather_qmm
-#cgo noescape mlx_reshape
-#cgo nocallback mlx_reshape
-#cgo noescape mlx_transpose_axes
-#cgo nocallback mlx_transpose_axes
-#cgo noescape mlx_expand_dims
-#cgo nocallback mlx_expand_dims
-#cgo noescape mlx_squeeze_axis
-#cgo nocallback mlx_squeeze_axis
-#cgo noescape mlx_flatten
-#cgo nocallback mlx_flatten
-#cgo noescape mlx_concatenate_axis
-#cgo nocallback mlx_concatenate_axis
-#cgo noescape mlx_slice
-#cgo nocallback mlx_slice
-#cgo noescape mlx_slice_update
-#cgo nocallback mlx_slice_update
-#cgo noescape mlx_as_strided
-#cgo nocallback mlx_as_strided
-#cgo noescape mlx_view
-#cgo nocallback mlx_view
-#cgo noescape mlx_contiguous
-#cgo nocallback mlx_contiguous
-#cgo noescape mlx_pad
-#cgo nocallback mlx_pad
-#cgo noescape mlx_tile
-#cgo nocallback mlx_tile
-#cgo noescape mlx_take_axis
-#cgo nocallback mlx_take_axis
-#cgo noescape mlx_take_along_axis
-#cgo nocallback mlx_take_along_axis
-#cgo noescape mlx_put_along_axis
-#cgo nocallback mlx_put_along_axis
-#cgo noescape mlx_where
-#cgo nocallback mlx_where
-#cgo noescape mlx_argsort_axis
-#cgo nocallback mlx_argsort_axis
-#cgo noescape mlx_argpartition_axis
-#cgo nocallback mlx_argpartition_axis
-#cgo noescape mlx_topk_axis
-#cgo nocallback mlx_topk_axis
-#cgo noescape mlx_less
-#cgo nocallback mlx_less
-#cgo noescape mlx_greater_equal
-#cgo nocallback mlx_greater_equal
-#cgo noescape mlx_logical_and
-#cgo nocallback mlx_logical_and
-#cgo noescape mlx_zeros
-#cgo nocallback mlx_zeros
-#cgo noescape mlx_zeros_like
-#cgo nocallback mlx_zeros_like
-#cgo noescape mlx_ones
-#cgo nocallback mlx_ones
-#cgo noescape mlx_full
-#cgo nocallback mlx_full
-#cgo noescape mlx_arange
-#cgo nocallback mlx_arange
-#cgo noescape mlx_linspace
-#cgo nocallback mlx_linspace
-#cgo noescape mlx_tri
-#cgo nocallback mlx_tri
-#cgo noescape mlx_astype
-#cgo nocallback mlx_astype
-#cgo noescape mlx_fast_rms_norm
-#cgo nocallback mlx_fast_rms_norm
-#cgo noescape mlx_fast_rope
-#cgo nocallback mlx_fast_rope
-#cgo noescape mlx_fast_scaled_dot_product_attention
-#cgo nocallback mlx_fast_scaled_dot_product_attention
-#cgo noescape mlx_conv2d
-#cgo nocallback mlx_conv2d
-#cgo noescape mlx_conv3d
-#cgo nocallback mlx_conv3d
-#cgo noescape mlx_random_key
-#cgo nocallback mlx_random_key
-#cgo noescape mlx_random_split
-#cgo nocallback mlx_random_split
-#cgo noescape mlx_random_categorical_num_samples
-#cgo nocallback mlx_random_categorical_num_samples
-#cgo noescape mlx_random_normal
-#cgo nocallback mlx_random_normal
-#cgo noescape mlx_random_uniform
-#cgo nocallback mlx_random_uniform
-#cgo noescape mlx_array_eval
-#cgo nocallback mlx_array_eval
-#cgo noescape mlx_eval
-#cgo nocallback mlx_eval
-#cgo noescape mlx_async_eval
-#cgo nocallback mlx_async_eval
-#cgo noescape mlx_synchronize
-#cgo nocallback mlx_synchronize
-#cgo noescape mlx_array_new
-#cgo nocallback mlx_array_new
-#cgo noescape mlx_array_new_data
-#cgo nocallback mlx_array_new_data
-#cgo noescape mlx_array_new_float
-#cgo nocallback mlx_array_new_float
-#cgo noescape mlx_array_free
-#cgo nocallback mlx_array_free
-#cgo noescape mlx_array_size
-#cgo nocallback mlx_array_size
-#cgo noescape mlx_array_ndim
-#cgo nocallback mlx_array_ndim
-#cgo noescape mlx_array_dim
-#cgo nocallback mlx_array_dim
-#cgo noescape mlx_array_dtype
-#cgo nocallback mlx_array_dtype
-#cgo noescape mlx_array_item_int32
-#cgo nocallback mlx_array_item_int32
-#cgo noescape mlx_vector_array_new_data
-#cgo nocallback mlx_vector_array_new_data
-#cgo noescape mlx_vector_array_free
-#cgo nocallback mlx_vector_array_free
-#cgo noescape mlx_array_new_int
-#cgo nocallback mlx_array_new_int
-#cgo noescape mlx_stream_new_device
-#cgo nocallback mlx_stream_new_device
-#cgo noescape mlx_get_default_stream
-#cgo nocallback mlx_get_default_stream
-#cgo noescape mlx_set_default_stream
-#cgo nocallback mlx_set_default_stream
 */
 import "C"
 import (
@@ -607,6 +422,11 @@ func (a *Array) Valid() bool {
 	return a != nil && a.c.ctx != nil
 }
 
+// Kept returns true if the array is marked to survive Eval() cleanup.
+func (a *Array) Kept() bool {
+	return a != nil && a.kept
+}
+
 func int32ToCInt(s []int32) *C.int {
 	if len(s) == 0 {
 		return nil
@@ -1480,6 +1300,44 @@ func (a *Array) ItemInt32() int32 {
 	return int32(val)
 }
 
+// Bytes copies the raw bytes out of the array without type conversion.
+// Works with common dtypes (float32, int32, uint32, uint8).
+// For non-contiguous arrays, call Contiguous() first.
+// Note: Triggers cleanup of non-kept arrays.
+func (a *Array) Bytes() []byte {
+	cleanup()
+	nbytes := a.Nbytes()
+	if nbytes == 0 {
+		return nil
+	}
+
+	// Get raw pointer based on dtype
+	var ptr unsafe.Pointer
+	switch a.Dtype() {
+	case DtypeFloat32:
+		ptr = unsafe.Pointer(C.mlx_array_data_float32(a.c))
+	case DtypeInt32:
+		ptr = unsafe.Pointer(C.mlx_array_data_int32(a.c))
+	case DtypeUint32:
+		ptr = unsafe.Pointer(C.mlx_array_data_uint32(a.c))
+	case DtypeUint8:
+		ptr = unsafe.Pointer(C.mlx_array_data_uint8(a.c))
+	default:
+		// For other types (bf16, f16, etc), convert to float32
+		arr := AsType(a, DtypeFloat32)
+		arr.Eval()
+		ptr = unsafe.Pointer(C.mlx_array_data_float32(arr.c))
+		nbytes = arr.Nbytes()
+	}
+
+	if ptr == nil {
+		return nil
+	}
+	data := make([]byte, nbytes)
+	copy(data, unsafe.Slice((*byte)(ptr), nbytes))
+	return data
+}
+
 // ============ Utility ============
 
 // String returns a string representation
@@ -1658,6 +1516,34 @@ func (s *SafetensorsFile) Free() {
 	C.mlx_map_string_to_string_free(s.metadata)
 }
 
+// SaveSafetensors saves arrays to a safetensors file using MLX's native implementation.
+// This correctly handles all dtypes including uint32 for quantized weights.
+func SaveSafetensors(path string, arrays map[string]*Array) error {
+	cPath := C.CString(path)
+	defer C.free(unsafe.Pointer(cPath))
+
+	// Create the map
+	cArrays := C.mlx_map_string_to_array_new()
+	defer C.mlx_map_string_to_array_free(cArrays)
+
+	// Add each array to the map
+	for name, arr := range arrays {
+		cName := C.CString(name)
+		C.mlx_map_string_to_array_insert(cArrays, cName, arr.c)
+		C.free(unsafe.Pointer(cName))
+	}
+
+	// Create empty metadata (optional)
+	cMeta := C.mlx_map_string_to_string_new()
+	defer C.mlx_map_string_to_string_free(cMeta)
+
+	// Save
+	if C.mlx_save_safetensors(cPath, cArrays, cMeta) != 0 {
+		return fmt.Errorf("failed to save safetensors: %s", path)
+	}
+	return nil
+}
+
 // ============ NPY Loading ============
 
 // LoadNpy loads a numpy array from an npy file
@@ -1725,7 +1611,57 @@ func ArgmaxKeepArray(logits *Array) *Array {
 var RandomState = []*Array{nil}
 var randomStateMu sync.Mutex
 
+var mlxInitialized bool
+var mlxInitError error
+
+// InitMLX initializes the MLX library by dynamically loading libmlxc.
+// This must be called before using any MLX functions.
+// Returns an error if the library cannot be loaded.
+func InitMLX() error {
+	if mlxInitialized {
+		return mlxInitError
+	}
+
+	// Try to load the MLX dynamic library
+	ret := C.mlx_dynamic_init()
+	if ret != 0 {
+		errMsg := C.GoString(C.mlx_dynamic_error())
+		mlxInitError = fmt.Errorf("failed to initialize MLX: %s", errMsg)
+		return mlxInitError
+	}
+
+	// Initialize all function pointers via dlsym
+	handle := C.mlx_get_handle()
+	ret = C.mlx_load_functions(handle)
+	if ret != 0 {
+		mlxInitError = fmt.Errorf("failed to load MLX function symbols")
+		return mlxInitError
+	}
+
+	mlxInitialized = true
+	mlxInitError = nil
+	return nil
+}
+
+// IsMLXAvailable returns whether MLX was successfully initialized
+func IsMLXAvailable() bool {
+	return mlxInitialized && mlxInitError == nil
+}
+
+// GetMLXInitError returns any error that occurred during MLX initialization
+func GetMLXInitError() error {
+	return mlxInitError
+}
+
 func init() {
+	// Initialize MLX dynamic library first
+	if err := InitMLX(); err != nil {
+		// Don't panic in init - let the caller handle the error
+		// Store the error for later retrieval
+		mlxInitError = err
+		return
+	}
+
 	// Lock main goroutine to OS thread for CUDA context stability.
 	// CUDA contexts are bound to threads; Go can migrate goroutines between threads.
 	runtime.LockOSThread()
@@ -1986,7 +1922,8 @@ func GatherQMM(x, w, scales *Array, biases, lhsIndices, rhsIndices *Array, trans
 // Returns (quantized_weights, scales, biases).
 // groupSize: number of elements quantized together (default 64)
 // bits: bits per element, 2, 4, or 8 (default 4)
-// mode: "affine" (default) or "mxfp4"
+// mode: "affine" (default), "mxfp4", or "mxfp8"
+// Note: mxfp8 mode returns nil biases (only weights and scales)
 func Quantize(w *Array, groupSize, bits int, mode string) (weights, scales, biases *Array) {
 	cMode := C.CString(mode)
 	defer C.free(unsafe.Pointer(cMode))
@@ -1995,14 +1932,21 @@ func Quantize(w *Array, groupSize, bits int, mode string) (weights, scales, bias
 	res := C.mlx_vector_array_new()
 	C.mlx_quantize(&res, w.c, optGroupSize, optBits, cMode, C.default_stream())
 
-	// Result is a vector of 3 arrays: [weights, scales, biases]
+	// Result is a vector of arrays: [weights, scales, biases?]
+	// mxfp8 mode returns only 2 elements (no biases)
+	vecSize := int(C.mlx_vector_array_size(res))
 	var w0, w1, w2 C.mlx_array
 	C.mlx_vector_array_get(&w0, res, 0)
 	C.mlx_vector_array_get(&w1, res, 1)
-	C.mlx_vector_array_get(&w2, res, 2)
+	if vecSize >= 3 {
+		C.mlx_vector_array_get(&w2, res, 2)
+	}
 	C.mlx_vector_array_free(res)
 
-	return newArray(w0), newArray(w1), newArray(w2)
+	if vecSize >= 3 {
+		return newArray(w0), newArray(w1), newArray(w2)
+	}
+	return newArray(w0), newArray(w1), nil
 }
 
 // Dequantize reconstructs weights from quantized form.

x/imagegen/mlx/mlx_dynamic.c

@@ -0,0 +1,144 @@
+// mlx_dynamic.c - Dynamic loading wrapper for MLX-C library
+// This file provides runtime dynamic loading of libmlxc instead of link-time binding
+
+#include "mlx_dynamic.h"
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+
+#ifdef _WIN32
+#include <windows.h>
+typedef HMODULE lib_handle_t;
+#define LOAD_LIB(path) LoadLibraryA(path)
+#define GET_SYMBOL(handle, name) GetProcAddress(handle, name)
+#define CLOSE_LIB(handle) FreeLibrary(handle)
+#define LIB_ERROR() "LoadLibrary failed"
+#else
+#include <dlfcn.h>
+typedef void* lib_handle_t;
+#define LOAD_LIB(path) dlopen(path, RTLD_LAZY | RTLD_GLOBAL)
+#define GET_SYMBOL(handle, name) dlsym(handle, name)
+#define CLOSE_LIB(handle) dlclose(handle)
+#define LIB_ERROR() dlerror()
+#ifdef __APPLE__
+#include <mach-o/dyld.h>
+#include <libgen.h>
+#endif
+#endif
+
+static lib_handle_t mlx_handle = NULL;
+static int mlx_initialized = 0;
+static char mlx_error_buffer[512] = {0};
+
+#ifdef __APPLE__
+// Get path to library in same directory as executable
+static char* get_exe_relative_path(const char* libname) {
+    static char path[1024];
+    uint32_t size = sizeof(path);
+    if (_NSGetExecutablePath(path, &size) != 0) {
+        return NULL;
+    }
+    // Get directory of executable
+    char* dir = dirname(path);
+    static char fullpath[1024];
+    snprintf(fullpath, sizeof(fullpath), "%s/%s", dir, libname);
+    return fullpath;
+}
+#endif
+
+// Try to load library from a specific path
+static int try_load_lib(const char* path) {
+    if (!path) return 0;
+    mlx_handle = LOAD_LIB(path);
+    return mlx_handle != NULL;
+}
+
+// Initialize MLX dynamic library
+// Returns 0 on success, -1 on failure
+// On failure, call mlx_dynamic_error() to get error message
+int mlx_dynamic_init(void) {
+    if (mlx_initialized) {
+        return 0;  // Already initialized
+    }
+
+    const char* lib_path = NULL;
+    const char* tried_paths[8] = {0};
+    int num_tried = 0;
+
+#ifdef _WIN32
+    // Windows: try same directory as executable
+    lib_path = "libmlxc.dll";
+    tried_paths[num_tried++] = lib_path;
+    if (try_load_lib(lib_path)) goto success;
+#elif defined(__APPLE__)
+    // macOS: try executable directory first
+    lib_path = get_exe_relative_path("libmlxc.dylib");
+    if (lib_path) {
+        tried_paths[num_tried++] = lib_path;
+        if (try_load_lib(lib_path)) goto success;
+    }
+    // Try build directory (for tests run from repo root)
+    lib_path = "./build/lib/ollama/libmlxc.dylib";
+    tried_paths[num_tried++] = lib_path;
+    if (try_load_lib(lib_path)) goto success;
+    // Fallback to system paths
+    lib_path = "libmlxc.dylib";
+    tried_paths[num_tried++] = lib_path;
+    if (try_load_lib(lib_path)) goto success;
+#else
+    // Linux: try build directory first (for tests)
+    lib_path = "./build/lib/ollama/libmlxc.so";
+    tried_paths[num_tried++] = lib_path;
+    if (try_load_lib(lib_path)) goto success;
+    // Fallback to system paths
+    lib_path = "libmlxc.so";
+    tried_paths[num_tried++] = lib_path;
+    if (try_load_lib(lib_path)) goto success;
+#endif
+
+    // Failed to load library - build error message with all tried paths
+    {
+        const char* err = LIB_ERROR();
+        int offset = snprintf(mlx_error_buffer, sizeof(mlx_error_buffer),
+                     "MLX: Failed to load libmlxc library. Tried: ");
+        for (int i = 0; i < num_tried && offset < (int)sizeof(mlx_error_buffer) - 50; i++) {
+            offset += snprintf(mlx_error_buffer + offset, sizeof(mlx_error_buffer) - offset,
+                             "%s%s", i > 0 ? ", " : "", tried_paths[i]);
+        }
+        if (err) {
+            snprintf(mlx_error_buffer + offset, sizeof(mlx_error_buffer) - offset,
+                    ". Last error: %s", err);
+        }
+    }
+    return -1;
+
+success:
+    mlx_initialized = 1;
+    snprintf(mlx_error_buffer, sizeof(mlx_error_buffer),
+             "MLX: Successfully loaded %s", lib_path ? lib_path : "library");
+    return 0;
+}
+
+// Get the last error message
+const char* mlx_dynamic_error(void) {
+    return mlx_error_buffer;
+}
+
+// Check if MLX is initialized
+int mlx_dynamic_is_initialized(void) {
+    return mlx_initialized;
+}
+
+// Get the library handle (for use by generated wrappers)
+void* mlx_get_handle(void) {
+    return mlx_handle;
+}
+
+// Cleanup (optional, called at program exit)
+void mlx_dynamic_cleanup(void) {
+    if (mlx_handle != NULL) {
+        CLOSE_LIB(mlx_handle);
+        mlx_handle = NULL;
+        mlx_initialized = 0;
+    }
+}

x/imagegen/mlx/mlx_dynamic.h

@@ -0,0 +1,29 @@
+// mlx_dynamic.h - Dynamic loading interface for MLX-C library
+#ifndef MLX_DYNAMIC_H
+#define MLX_DYNAMIC_H
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+// Initialize the MLX dynamic library
+// Returns 0 on success, -1 on failure
+int mlx_dynamic_init(void);
+
+// Get the last error message from dynamic loading
+const char* mlx_dynamic_error(void);
+
+// Check if MLX is initialized
+int mlx_dynamic_is_initialized(void);
+
+// Get the library handle (for use by generated wrappers)
+void* mlx_get_handle(void);
+
+// Cleanup resources (optional, for clean shutdown)
+void mlx_dynamic_cleanup(void);
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif // MLX_DYNAMIC_H

x/imagegen/mlx/mlx_test.go

@@ -4,9 +4,30 @@ package mlx
 
 import (
 	"fmt"
+	"os"
+	"path/filepath"
+	"runtime"
 	"testing"
 )
 
+// TestMain initializes MLX before running tests.
+// If MLX libraries are not available, tests are skipped.
+func TestMain(m *testing.M) {
+	// Change to repo root so ./build/lib/ollama/ path works
+	_, thisFile, _, _ := runtime.Caller(0)
+	repoRoot := filepath.Join(filepath.Dir(thisFile), "..", "..", "..")
+	if err := os.Chdir(repoRoot); err != nil {
+		fmt.Printf("Failed to change to repo root: %v\n", err)
+		os.Exit(1)
+	}
+
+	if err := InitMLX(); err != nil {
+		fmt.Printf("Skipping MLX tests: %v\n", err)
+		os.Exit(0)
+	}
+	os.Exit(m.Run())
+}
+
 // TestBasicCleanup verifies non-kept arrays are freed and kept arrays survive.
 func TestBasicCleanup(t *testing.T) {
 	weight := NewArrayFloat32([]float32{1, 2, 3, 4}, []int32{2, 2})

x/imagegen/models/qwen_image/pipeline_test.go

@@ -3,12 +3,33 @@
 package qwen_image
 
 import (
+	"fmt"
 	"os"
+	"path/filepath"
+	"runtime"
 	"testing"
 
 	"github.com/ollama/ollama/x/imagegen/mlx"
 )
 
+// TestMain initializes MLX before running tests.
+// If MLX libraries are not available, tests are skipped.
+func TestMain(m *testing.M) {
+	// Change to repo root so ./build/lib/ollama/ path works
+	_, thisFile, _, _ := runtime.Caller(0)
+	repoRoot := filepath.Join(filepath.Dir(thisFile), "..", "..", "..", "..")
+	if err := os.Chdir(repoRoot); err != nil {
+		fmt.Printf("Failed to change to repo root: %v\n", err)
+		os.Exit(1)
+	}
+
+	if err := mlx.InitMLX(); err != nil {
+		fmt.Printf("Skipping qwen_image tests: %v\n", err)
+		os.Exit(0)
+	}
+	os.Exit(m.Run())
+}
+
 // TestPipelineOutput runs the full pipeline (integration test).
 // Skips if model weights not found. Requires ~50GB VRAM.
 func TestPipelineOutput(t *testing.T) {

x/imagegen/models/qwen_image/qwen_image.go

@@ -172,7 +172,7 @@ func (m *Model) generate(cfg *GenerateConfig) (*mlx.Array, error) {
 		cfg.Height = 1024
 	}
 	if cfg.Steps <= 0 {
-		cfg.Steps = 30
+		cfg.Steps = 50
 	}
 	if cfg.CFGScale <= 0 {
 		cfg.CFGScale = 4.0
@@ -222,6 +222,14 @@ func (m *Model) generate(cfg *GenerateConfig) (*mlx.Array, error) {
 		mlx.Keep(posEmb, negEmb)
 	}
 
+	// Pre-compute batched embeddings for CFG (single forward pass optimization)
+	var batchedEmb *mlx.Array
+	if useCFG {
+		batchedEmb = mlx.Concatenate([]*mlx.Array{posEmb, negEmb}, 0)
+		mlx.Keep(batchedEmb)
+		mlx.Eval(batchedEmb)
+	}
+
 	// Scheduler
 	scheduler := NewFlowMatchScheduler(DefaultSchedulerConfig())
 	scheduler.SetTimesteps(cfg.Steps, imgSeqLen)
@@ -264,10 +272,19 @@ func (m *Model) generate(cfg *GenerateConfig) (*mlx.Array, error) {
 
 		var output *mlx.Array
 		if useCFG {
-			// True CFG: run twice and combine with norm rescaling
+			// CFG Batching: single forward pass with batch=2
 			// Note: layer caching with CFG is not supported yet (would need 2 caches)
-			posOutput := m.Transformer.Forward(patches, posEmb, timestep, ropeCache.ImgFreqs, ropeCache.TxtFreqs)
-			negOutput := m.Transformer.Forward(patches, negEmb, timestep, ropeCache.ImgFreqs, ropeCache.TxtFreqs)
+			batchedPatches := mlx.Tile(patches, []int32{2, 1, 1})
+			batchedTimestep := mlx.Tile(timestep, []int32{2})
+
+			// Single batched forward pass
+			batchedOutput := m.Transformer.Forward(batchedPatches, batchedEmb, batchedTimestep, ropeCache.ImgFreqs, ropeCache.TxtFreqs)
+
+			// Split output: [2, L, D] -> pos [1, L, D], neg [1, L, D]
+			L := batchedOutput.Shape()[1]
+			D := batchedOutput.Shape()[2]
+			posOutput := mlx.Slice(batchedOutput, []int32{0, 0, 0}, []int32{1, L, D})
+			negOutput := mlx.Slice(batchedOutput, []int32{1, 0, 0}, []int32{2, L, D})
 
 			diff := mlx.Sub(posOutput, negOutput)
 			scaledDiff := mlx.MulScalar(diff, cfg.CFGScale)
@@ -305,6 +322,9 @@ func (m *Model) generate(cfg *GenerateConfig) (*mlx.Array, error) {
 	if negEmb != nil {
 		negEmb.Free()
 	}
+	if batchedEmb != nil {
+		batchedEmb.Free()
+	}
 	ropeCache.ImgFreqs.Free()
 	ropeCache.TxtFreqs.Free()
 	if stepCache != nil {

x/imagegen/models/qwen_image_edit/qwen_image_edit.go

@@ -241,6 +241,14 @@ func (m *Model) edit(inputImagePaths []string, cfg *GenerateConfig) (*mlx.Array,
 		mlx.Eval(posEmb, negEmb)
 	}
 
+	// Pre-compute batched embeddings for CFG (single forward pass optimization)
+	var batchedEmb *mlx.Array
+	if useCFG {
+		batchedEmb = mlx.Concatenate([]*mlx.Array{posEmb, negEmb}, 0)
+		mlx.Keep(batchedEmb)
+		mlx.Eval(batchedEmb)
+	}
+
 	// Encode all input images to latents and concatenate
 	fmt.Println("Encoding images to latents...")
 	allImageLatentsPacked := make([]*mlx.Array, len(vaeImages))
@@ -291,11 +299,18 @@ func (m *Model) edit(inputImagePaths []string, cfg *GenerateConfig) (*mlx.Array,
 
 		var output *mlx.Array
 		if useCFG {
-			posOutput := m.Transformer.Forward(latentInput, posEmb, timestep, ropeCache.ImgFreqs, ropeCache.TxtFreqs)
-			negOutput := m.Transformer.Forward(latentInput, negEmb, timestep, ropeCache.ImgFreqs, ropeCache.TxtFreqs)
+			// CFG Batching: single forward pass with batch=2
+			// Tile inputs: [1, L, D] -> [2, L, D]
+			batchedLatentInput := mlx.Tile(latentInput, []int32{2, 1, 1})
+			batchedTimestep := mlx.Tile(timestep, []int32{2})
 
-			posOutput = mlx.Slice(posOutput, []int32{0, 0, 0}, []int32{1, imgSeqLen, posOutput.Shape()[2]})
-			negOutput = mlx.Slice(negOutput, []int32{0, 0, 0}, []int32{1, imgSeqLen, negOutput.Shape()[2]})
+			// Single batched forward pass
+			batchedOutput := m.Transformer.Forward(batchedLatentInput, batchedEmb, batchedTimestep, ropeCache.ImgFreqs, ropeCache.TxtFreqs)
+
+			// Split output: [2, L, D] -> pos [1, L, D], neg [1, L, D]
+			D := batchedOutput.Shape()[2]
+			posOutput := mlx.Slice(batchedOutput, []int32{0, 0, 0}, []int32{1, imgSeqLen, D})
+			negOutput := mlx.Slice(batchedOutput, []int32{1, 0, 0}, []int32{2, imgSeqLen, D})
 
 			output = applyCFGWithNormRescale(posOutput, negOutput, cfg.CFGScale)
 		} else {
@@ -317,6 +332,9 @@ func (m *Model) edit(inputImagePaths []string, cfg *GenerateConfig) (*mlx.Array,
 	if negEmb != nil {
 		negEmb.Free()
 	}
+	if batchedEmb != nil {
+		batchedEmb.Free()
+	}
 	ropeCache.ImgFreqs.Free()
 	ropeCache.TxtFreqs.Free()
 	imageLatentsPacked.Free()

x/imagegen/models/qwen_image_edit/rope_test.go

@@ -3,13 +3,35 @@
 package qwen_image_edit
 
 import (
+	"fmt"
 	"math"
+	"os"
+	"path/filepath"
+	"runtime"
 	"testing"
 
 	"github.com/ollama/ollama/x/imagegen/mlx"
 	"github.com/ollama/ollama/x/imagegen/models/qwen_image"
 )
 
+// TestMain initializes MLX before running tests.
+// If MLX libraries are not available, tests are skipped.
+func TestMain(m *testing.M) {
+	// Change to repo root so ./build/lib/ollama/ path works
+	_, thisFile, _, _ := runtime.Caller(0)
+	repoRoot := filepath.Join(filepath.Dir(thisFile), "..", "..", "..", "..")
+	if err := os.Chdir(repoRoot); err != nil {
+		fmt.Printf("Failed to change to repo root: %v\n", err)
+		os.Exit(1)
+	}
+
+	if err := mlx.InitMLX(); err != nil {
+		fmt.Printf("Skipping qwen_image_edit tests: %v\n", err)
+		os.Exit(0)
+	}
+	os.Exit(m.Run())
+}
+
 // TestComputeAxisFreqs verifies frequency computation matches Python reference
 func TestComputeAxisFreqs(t *testing.T) {
 	theta := float64(10000)

x/imagegen/models/zimage/text_encoder.go

@@ -28,12 +28,12 @@ type Qwen3Config struct {
 
 // Qwen3Attention implements Qwen3 attention with QK norms
 type Qwen3Attention struct {
-	QProj *nn.Linear  `weight:"q_proj"`
-	KProj *nn.Linear  `weight:"k_proj"`
-	VProj *nn.Linear  `weight:"v_proj"`
-	OProj *nn.Linear  `weight:"o_proj"`
-	QNorm *nn.RMSNorm `weight:"q_norm"`
-	KNorm *nn.RMSNorm `weight:"k_norm"`
+	QProj nn.LinearLayer `weight:"q_proj"`
+	KProj nn.LinearLayer `weight:"k_proj"`
+	VProj nn.LinearLayer `weight:"v_proj"`
+	OProj nn.LinearLayer `weight:"o_proj"`
+	QNorm *nn.RMSNorm    `weight:"q_norm"`
+	KNorm *nn.RMSNorm    `weight:"k_norm"`
 	// Computed fields
 	NHeads    int32
 	NKVHeads  int32
@@ -136,9 +136,9 @@ func repeatKV(x *mlx.Array, repeats int32) *mlx.Array {
 
 // Qwen3MLP implements Qwen3 SwiGLU MLP
 type Qwen3MLP struct {
-	GateProj *nn.Linear `weight:"gate_proj"`
-	UpProj   *nn.Linear `weight:"up_proj"`
-	DownProj *nn.Linear `weight:"down_proj"`
+	GateProj nn.LinearLayer `weight:"gate_proj"`
+	UpProj   nn.LinearLayer `weight:"up_proj"`
+	DownProj nn.LinearLayer `weight:"down_proj"`
 }
 
 // Forward applies the MLP

x/imagegen/models/zimage/transformer.go

@@ -36,8 +36,8 @@ type TransformerConfig struct {
 // TimestepEmbedder creates sinusoidal timestep embeddings
 // Output dimension is 256 (fixed), used for AdaLN modulation
 type TimestepEmbedder struct {
-	Linear1       *nn.Linear `weight:"mlp.0"`
-	Linear2       *nn.Linear `weight:"mlp.2"`
+	Linear1       nn.LinearLayer `weight:"mlp.0"`
+	Linear2       nn.LinearLayer `weight:"mlp.2"`
 	FreqEmbedSize int32      // 256 (computed)
 }
 
@@ -74,7 +74,7 @@ func (te *TimestepEmbedder) Forward(t *mlx.Array) *mlx.Array {
 
 // XEmbedder embeds image patches to model dimension
 type XEmbedder struct {
-	Linear *nn.Linear `weight:"2-1"`
+	Linear nn.LinearLayer `weight:"2-1"`
 }
 
 // Forward embeds patchified image latents
@@ -86,7 +86,7 @@ func (xe *XEmbedder) Forward(x *mlx.Array) *mlx.Array {
 // CapEmbedder projects caption features to model dimension
 type CapEmbedder struct {
 	Norm     *nn.RMSNorm `weight:"0"`
-	Linear   *nn.Linear  `weight:"1"`
+	Linear   nn.LinearLayer  `weight:"1"`
 	PadToken *mlx.Array  // loaded separately at root level
 }
 
@@ -100,12 +100,13 @@ func (ce *CapEmbedder) Forward(capFeats *mlx.Array) *mlx.Array {
 
 // FeedForward implements SwiGLU FFN
 type FeedForward struct {
-	W1     *nn.Linear `weight:"w1"` // gate projection
-	W2     *nn.Linear `weight:"w2"` // down projection
-	W3     *nn.Linear `weight:"w3"` // up projection
+	W1     nn.LinearLayer `weight:"w1"` // gate projection
+	W2     nn.LinearLayer `weight:"w2"` // down projection
+	W3     nn.LinearLayer `weight:"w3"` // up projection
 	OutDim int32      // computed from W2
 }
 
+
 // Forward applies SwiGLU: silu(W1(x)) * W3(x), then W2
 func (ff *FeedForward) Forward(x *mlx.Array) *mlx.Array {
 	shape := x.Shape()
@@ -115,6 +116,7 @@ func (ff *FeedForward) Forward(x *mlx.Array) *mlx.Array {
 
 	// Reshape for matmul
 	x = mlx.Reshape(x, B*L, D)
+
 	gate := ff.W1.Forward(x)
 	gate = mlx.SiLU(gate)
 	up := ff.W3.Forward(x)
@@ -126,17 +128,69 @@ func (ff *FeedForward) Forward(x *mlx.Array) *mlx.Array {
 
 // Attention implements multi-head attention with QK norm
 type Attention struct {
-	ToQ   *nn.Linear `weight:"to_q"`
-	ToK   *nn.Linear `weight:"to_k"`
-	ToV   *nn.Linear `weight:"to_v"`
-	ToOut *nn.Linear `weight:"to_out.0"`
+	ToQ   nn.LinearLayer `weight:"to_q"`
+	ToK   nn.LinearLayer `weight:"to_k"`
+	ToV   nn.LinearLayer `weight:"to_v"`
+	ToOut nn.LinearLayer `weight:"to_out.0"`
 	NormQ *mlx.Array `weight:"norm_q.weight"` // [head_dim] for per-head RMSNorm
 	NormK *mlx.Array `weight:"norm_k.weight"`
-	// Computed fields
-	NHeads  int32
-	HeadDim int32
-	Dim     int32
-	Scale   float32
+	// Fused QKV (computed at init time for efficiency, not loaded from weights)
+	ToQKV nn.LinearLayer `weight:"-"` // Fused Q+K+V projection (created by FuseQKV)
+	Fused bool       `weight:"-"` // Whether to use fused QKV path
+	// Computed fields (not loaded from weights)
+	NHeads  int32   `weight:"-"`
+	HeadDim int32   `weight:"-"`
+	Dim     int32   `weight:"-"`
+	Scale   float32 `weight:"-"`
+}
+
+// FuseQKV creates a fused QKV projection by concatenating weights.
+// This reduces 3 matmuls to 1 for a ~5-10% speedup.
+// Note: Fusion is skipped for quantized weights as it would require complex
+// dequant-concat-requant operations. The FP8 memory bandwidth savings outweigh
+// the ~5% fusion benefit.
+func (attn *Attention) FuseQKV() {
+	if attn.ToQ == nil || attn.ToK == nil || attn.ToV == nil {
+		return
+	}
+
+	// Skip fusion for quantized weights - type assert to check
+	toQ, qOk := attn.ToQ.(*nn.Linear)
+	toK, kOk := attn.ToK.(*nn.Linear)
+	toV, vOk := attn.ToV.(*nn.Linear)
+	if !qOk || !kOk || !vOk {
+		// One or more are QuantizedLinear, skip fusion
+		return
+	}
+
+	if toQ.Weight == nil || toK.Weight == nil || toV.Weight == nil {
+		return
+	}
+
+	// Concatenate weights: [dim, dim] x 3 -> [3*dim, dim]
+	// Weight shapes: ToQ.Weight [out_dim, in_dim], etc.
+	qWeight := toQ.Weight
+	kWeight := toK.Weight
+	vWeight := toV.Weight
+
+	// Concatenate along output dimension (axis 0)
+	fusedWeight := mlx.Concatenate([]*mlx.Array{qWeight, kWeight, vWeight}, 0)
+
+	// Evaluate fused weight to ensure it's materialized
+	mlx.Eval(fusedWeight)
+
+	// Create fused linear layer
+	fusedLinear := &nn.Linear{Weight: fusedWeight}
+
+	// Handle bias if present
+	if toQ.Bias != nil && toK.Bias != nil && toV.Bias != nil {
+		fusedBias := mlx.Concatenate([]*mlx.Array{toQ.Bias, toK.Bias, toV.Bias}, 0)
+		mlx.Eval(fusedBias)
+		fusedLinear.Bias = fusedBias
+	}
+
+	attn.ToQKV = fusedLinear
+	attn.Fused = true
 }
 
 // Forward computes attention
@@ -146,11 +200,24 @@ func (attn *Attention) Forward(x *mlx.Array, cos, sin *mlx.Array) *mlx.Array {
 	L := shape[1]
 	D := shape[2]
 
-	// Project Q, K, V
 	xFlat := mlx.Reshape(x, B*L, D)
-	q := attn.ToQ.Forward(xFlat)
-	k := attn.ToK.Forward(xFlat)
-	v := attn.ToV.Forward(xFlat)
+
+	var q, k, v *mlx.Array
+	if attn.Fused && attn.ToQKV != nil {
+		// Fused QKV path: single matmul then split
+		qkv := attn.ToQKV.Forward(xFlat) // [B*L, 3*dim]
+
+		// Split into Q, K, V along last dimension
+		// Each has shape [B*L, dim]
+		q = mlx.Slice(qkv, []int32{0, 0}, []int32{B * L, attn.Dim})
+		k = mlx.Slice(qkv, []int32{0, attn.Dim}, []int32{B * L, 2 * attn.Dim})
+		v = mlx.Slice(qkv, []int32{0, 2 * attn.Dim}, []int32{B * L, 3 * attn.Dim})
+	} else {
+		// Separate Q, K, V projections
+		q = attn.ToQ.Forward(xFlat)
+		k = attn.ToK.Forward(xFlat)
+		v = attn.ToV.Forward(xFlat)
+	}
 
 	// Reshape to [B, L, nheads, head_dim]
 	q = mlx.Reshape(q, B, L, attn.NHeads, attn.HeadDim)
@@ -227,7 +294,7 @@ type TransformerBlock struct {
 	AttentionNorm2 *nn.RMSNorm  `weight:"attention_norm2"`
 	FFNNorm1       *nn.RMSNorm  `weight:"ffn_norm1"`
 	FFNNorm2       *nn.RMSNorm  `weight:"ffn_norm2"`
-	AdaLN          *nn.Linear   `weight:"adaLN_modulation.0,optional"` // only if modulation
+	AdaLN          nn.LinearLayer   `weight:"adaLN_modulation.0,optional"` // only if modulation
 	// Computed fields
 	HasModulation bool
 	Dim           int32
@@ -281,8 +348,8 @@ func (tb *TransformerBlock) Forward(x *mlx.Array, adaln *mlx.Array, cos, sin *ml
 
 // FinalLayer outputs the denoised patches
 type FinalLayer struct {
-	AdaLN  *nn.Linear `weight:"adaLN_modulation.1"` // [256] -> [dim]
-	Output *nn.Linear `weight:"linear"`             // [dim] -> [out_channels]
+	AdaLN  nn.LinearLayer `weight:"adaLN_modulation.1"` // [256] -> [dim]
+	Output nn.LinearLayer `weight:"linear"`             // [dim] -> [out_channels]
 	OutDim int32      // computed from Output
 }
 
@@ -350,12 +417,11 @@ func (m *Transformer) Load(manifest *imagegen.ModelManifest) error {
 	m.ContextRefiners = make([]*TransformerBlock, cfg.NRefinerLayers)
 	m.Layers = make([]*TransformerBlock, cfg.NLayers)
 
-	// Load weights from tensor blobs with BF16 conversion
 	weights, err := imagegen.LoadWeightsFromManifest(manifest, "transformer")
 	if err != nil {
 		return fmt.Errorf("weights: %w", err)
 	}
-	if err := weights.Load(mlx.DtypeBFloat16); err != nil {
+	if err := weights.Load(0); err != nil {
 		return fmt.Errorf("load weights: %w", err)
 	}
 	defer weights.ReleaseAll()
@@ -377,7 +443,7 @@ func (m *Transformer) loadWeights(weights safetensors.WeightSource) error {
 func (m *Transformer) initComputedFields() {
 	cfg := m.TransformerConfig
 	m.TEmbed.FreqEmbedSize = 256
-	m.FinalLayer.OutDim = m.FinalLayer.Output.Weight.Shape()[0]
+	m.FinalLayer.OutDim = m.FinalLayer.Output.OutputDim()
 	m.CapEmbed.Norm.Eps = 1e-6
 
 	for _, block := range m.NoiseRefiners {
@@ -391,6 +457,20 @@ func (m *Transformer) initComputedFields() {
 	}
 }
 
+// FuseAllQKV fuses QKV projections in all attention layers for efficiency.
+// This reduces 3 matmuls to 1 per attention layer, providing ~5-10% speedup.
+func (m *Transformer) FuseAllQKV() {
+	for _, block := range m.NoiseRefiners {
+		block.Attention.FuseQKV()
+	}
+	for _, block := range m.ContextRefiners {
+		block.Attention.FuseQKV()
+	}
+	for _, block := range m.Layers {
+		block.Attention.FuseQKV()
+	}
+}
+
 // initTransformerBlock sets computed fields on a transformer block
 func initTransformerBlock(block *TransformerBlock, cfg *TransformerConfig) {
 	block.Dim = cfg.Dim
@@ -404,7 +484,7 @@ func initTransformerBlock(block *TransformerBlock, cfg *TransformerConfig) {
 	attn.Scale = float32(1.0 / math.Sqrt(float64(attn.HeadDim)))
 
 	// Init feedforward OutDim
-	block.FeedForward.OutDim = block.FeedForward.W2.Weight.Shape()[0]
+	block.FeedForward.OutDim = block.FeedForward.W2.OutputDim()
 
 	// Set eps on all RMSNorm layers
 	block.AttentionNorm1.Eps = cfg.NormEps
@@ -423,6 +503,8 @@ type RoPECache struct {
 	UnifiedSin *mlx.Array
 	ImgLen     int32
 	CapLen     int32
+	GridH      int32 // Image token grid height
+	GridW      int32 // Image token grid width
 }
 
 // PrepareRoPECache precomputes RoPE values for the given image and caption lengths.
@@ -456,6 +538,8 @@ func (m *Transformer) PrepareRoPECache(hTok, wTok, capLen int32) *RoPECache {
 		UnifiedSin: unifiedSin,
 		ImgLen:     imgLen,
 		CapLen:     capLen,
+		GridH:      hTok,
+		GridW:      wTok,
 	}
 }
 

x/imagegen/models/zimage/vae.go

@@ -104,6 +104,8 @@ func (gn *GroupNormLayer) forwardTiled(x *mlx.Array, B, H, W, C int32) *mlx.Arra
 	groupSize := C / gn.NumGroups
 
 	// Keep the input - we need it for slicing tiles later
+	// Track if we were the ones who kept it, so we can restore state after
+	wasKept := x.Kept()
 	mlx.Keep(x)
 
 	// Compute per-group mean and variance using flattened spatial dimensions
@@ -205,6 +207,10 @@ func (gn *GroupNormLayer) forwardTiled(x *mlx.Array, B, H, W, C int32) *mlx.Arra
 	}
 
 	// Clean up kept arrays
+	// Restore x's kept state - only free if we were the ones who kept it
+	if !wasKept {
+		x.Free()
+	}
 	mean.Free()
 	invStd.Free()
 	if weightGN != nil {
@@ -734,18 +740,26 @@ func (vae *VAEDecoder) Decode(latents *mlx.Array) *mlx.Array {
 	h := vae.ConvIn.Forward(z)
 	mlx.Eval(h)
 
+	prev := h
 	h = vae.MidBlock.Forward(h)
+	prev.Free()
 
 	for _, upBlock := range vae.UpBlocks {
+		prev = h
 		h = upBlock.Forward(h)
+		prev.Free()
 	}
 
-	prev := h
+	prev = h
 	h = vae.ConvNormOut.Forward(h)
 	mlx.Eval(h) // Eval after GroupNorm to avoid grid dimension issues
+	prev.Free()
+
+	prev = h
 	h = mlx.SiLU(h)
 	h = vae.ConvOut.Forward(h)
 	mlx.Eval(h)
+	prev.Free()
 
 	// VAE outputs [-1, 1], convert to [0, 1]
 	h = mlx.MulScalar(h, 0.5)
@@ -754,7 +768,6 @@ func (vae *VAEDecoder) Decode(latents *mlx.Array) *mlx.Array {
 
 	// Convert NHWC -> NCHW for output
 	h = mlx.Transpose(h, 0, 3, 1, 2)
-	prev.Free()
 	mlx.Eval(h)
 
 	return h

x/imagegen/models/zimage/zimage.go

@@ -26,10 +26,12 @@ type GenerateConfig struct {
 	Progress       ProgressFunc // Optional progress callback
 	CapturePath    string       // GPU capture path (debug)
 
-	// Layer caching options (speedup via shallow layer reuse)
-	LayerCache    bool // Enable layer caching (default: false)
-	CacheInterval int  // Refresh cache every N steps (default: 3)
-	CacheLayers   int  // Number of shallow layers to cache (default: 15)
+	// TeaCache options (timestep embedding aware caching)
+	TeaCache          bool    // TeaCache is always enabled for faster inference
+	TeaCacheThreshold float32 // Threshold for cache reuse (default: 0.1, lower = more aggressive)
+
+	// Fused QKV (fuse Q/K/V projections into single matmul)
+	FusedQKV bool // Enable fused QKV projection (default: false)
 }
 
 // ProgressFunc is called during generation with step progress.
@@ -42,6 +44,7 @@ type Model struct {
 	TextEncoder *Qwen3TextEncoder
 	Transformer *Transformer
 	VAEDecoder  *VAEDecoder
+	qkvFused    bool // Track if QKV has been fused (do only once)
 }
 
 // Load loads the Z-Image model from ollama blob storage.
@@ -191,18 +194,22 @@ func (m *Model) generate(ctx context.Context, cfg *GenerateConfig) (*mlx.Array,
 		cfg.Height = 1024
 	}
 	if cfg.Steps <= 0 {
-		cfg.Steps = 9 // Turbo default
+		cfg.Steps = 9 // Z-Image turbo default
 	}
 	if cfg.CFGScale <= 0 {
 		cfg.CFGScale = 4.0
 	}
-	if cfg.LayerCache {
-		if cfg.CacheInterval <= 0 {
-			cfg.CacheInterval = 3
-		}
-		if cfg.CacheLayers <= 0 {
-			cfg.CacheLayers = 15 // Half of 30 layers
-		}
+	// TeaCache enabled by default
+	cfg.TeaCache = true
+	if cfg.TeaCacheThreshold <= 0 {
+		cfg.TeaCacheThreshold = 0.15
+	}
+
+	// Enable fused QKV if requested (only fuse once)
+	if cfg.FusedQKV && !m.qkvFused {
+		m.Transformer.FuseAllQKV()
+		m.qkvFused = true
+		fmt.Println("  Fused QKV enabled")
 	}
 
 	useCFG := cfg.NegativePrompt != ""
@@ -260,12 +267,54 @@ func (m *Model) generate(ctx context.Context, cfg *GenerateConfig) (*mlx.Array,
 		mlx.Eval(ropeCache.UnifiedCos)
 	}
 
-	// Step cache for shallow layer reuse (DeepCache/Learning-to-Cache style)
-	var stepCache *cache.StepCache
-	if cfg.LayerCache {
-		stepCache = cache.NewStepCache(cfg.CacheLayers)
-		fmt.Printf("  Layer caching enabled: %d layers, refresh every %d steps\n",
-			cfg.CacheLayers, cfg.CacheInterval)
+	// Pre-compute batched embeddings for CFG (outside the loop for efficiency)
+	var batchedEmb *mlx.Array
+	if useCFG {
+		// Concatenate embeddings once: [1, L, D] + [1, L, D] -> [2, L, D]
+		batchedEmb = mlx.Concatenate([]*mlx.Array{posEmb, negEmb}, 0)
+		mlx.Keep(batchedEmb)
+		mlx.Eval(batchedEmb)
+	}
+
+	// TeaCache for timestep-aware caching
+	// For CFG mode, we cache pos/neg separately, skip early steps, and always compute CFG fresh
+	var teaCache *cache.TeaCache
+	if cfg.TeaCache {
+		skipEarly := 0
+		if useCFG {
+			skipEarly = 3 // Skip first 3 steps for CFG to preserve structure
+		}
+		teaCache = cache.NewTeaCache(&cache.TeaCacheConfig{
+			Threshold:      cfg.TeaCacheThreshold,
+			RescaleFactor:  1.0,
+			SkipEarlySteps: skipEarly,
+		})
+		if useCFG {
+			fmt.Printf("  TeaCache enabled (CFG mode): threshold=%.2f, skip first %d steps\n", cfg.TeaCacheThreshold, skipEarly)
+		} else {
+			fmt.Printf("  TeaCache enabled: threshold=%.2f\n", cfg.TeaCacheThreshold)
+		}
+	}
+
+	// cleanup frees all kept arrays when we need to abort early
+	cleanup := func() {
+		posEmb.Free()
+		if negEmb != nil {
+			negEmb.Free()
+		}
+		ropeCache.ImgCos.Free()
+		ropeCache.ImgSin.Free()
+		ropeCache.CapCos.Free()
+		ropeCache.CapSin.Free()
+		ropeCache.UnifiedCos.Free()
+		ropeCache.UnifiedSin.Free()
+		if batchedEmb != nil {
+			batchedEmb.Free()
+		}
+		if teaCache != nil {
+			teaCache.Free()
+		}
+		latents.Free()
 	}
 
 	// Denoising loop
@@ -277,6 +326,7 @@ func (m *Model) generate(ctx context.Context, cfg *GenerateConfig) (*mlx.Array,
 		if ctx != nil {
 			select {
 			case <-ctx.Done():
+				cleanup()
 				return nil, ctx.Err()
 			default:
 			}
@@ -289,50 +339,77 @@ func (m *Model) generate(ctx context.Context, cfg *GenerateConfig) (*mlx.Array,
 		}
 
 		tCurr := scheduler.Timesteps[i]
-		timestep := mlx.ToBFloat16(mlx.NewArray([]float32{1.0 - tCurr}, []int32{1}))
+		var noisePred *mlx.Array
 
-		patches := PatchifyLatents(latents, tcfg.PatchSize)
+		// TeaCache: check if we should compute or reuse cached output
+		shouldCompute := teaCache == nil || teaCache.ShouldCompute(i, tCurr)
 
-		var output *mlx.Array
-		if stepCache != nil {
-			// Use layer caching for faster inference
+		if shouldCompute {
+			timestep := mlx.ToBFloat16(mlx.NewArray([]float32{1.0 - tCurr}, []int32{1}))
+			patches := PatchifyLatents(latents, tcfg.PatchSize)
+
+			var output *mlx.Array
 			if useCFG {
-				posOutput := m.Transformer.ForwardWithCache(patches, timestep, posEmb, ropeCache,
-					stepCache, i, cfg.CacheInterval)
-				// Note: CFG with layer cache shares the cache between pos/neg
-				// This is approximate but fast - neg prompt uses same cached shallow layers
-				negOutput := m.Transformer.ForwardWithCache(patches, timestep, negEmb, ropeCache,
-					stepCache, i, cfg.CacheInterval)
-				diff := mlx.Sub(posOutput, negOutput)
+				// CFG Batching: single forward pass with batch=2
+				// Tile patches: [1, L, D] -> [2, L, D]
+				batchedPatches := mlx.Tile(patches, []int32{2, 1, 1})
+				// Tile timestep: [1] -> [2]
+				batchedTimestep := mlx.Tile(timestep, []int32{2})
+
+				// Single batched forward pass (RoPE broadcasts from [1,L,H,D] to [2,L,H,D])
+				batchedOutput := m.Transformer.Forward(batchedPatches, batchedTimestep, batchedEmb, ropeCache)
+
+				// Split output: [2, L, D] -> pos [1, L, D], neg [1, L, D]
+				outputShape := batchedOutput.Shape()
+				L := outputShape[1]
+				D := outputShape[2]
+				posOutput := mlx.Slice(batchedOutput, []int32{0, 0, 0}, []int32{1, L, D})
+				negOutput := mlx.Slice(batchedOutput, []int32{1, 0, 0}, []int32{2, L, D})
+
+				// Convert to noise predictions (unpatchify and negate)
+				posPred := UnpatchifyLatents(posOutput, tcfg.PatchSize, latentH, latentW, tcfg.InChannels)
+				posPred = mlx.Neg(posPred)
+				negPred := UnpatchifyLatents(negOutput, tcfg.PatchSize, latentH, latentW, tcfg.InChannels)
+				negPred = mlx.Neg(negPred)
+
+				// Cache pos/neg separately for TeaCache
+				if teaCache != nil {
+					teaCache.UpdateCFGCache(posPred, negPred, tCurr)
+					mlx.Keep(teaCache.Arrays()...)
+				}
+
+				// Apply CFG: noisePred = neg + scale * (pos - neg)
+				diff := mlx.Sub(posPred, negPred)
 				scaledDiff := mlx.MulScalar(diff, cfg.CFGScale)
-				output = mlx.Add(negOutput, scaledDiff)
-			} else {
-				output = m.Transformer.ForwardWithCache(patches, timestep, posEmb, ropeCache,
-					stepCache, i, cfg.CacheInterval)
-			}
-		} else {
-			// Standard forward without caching
-			if useCFG {
-				posOutput := m.Transformer.Forward(patches, timestep, posEmb, ropeCache)
-				negOutput := m.Transformer.Forward(patches, timestep, negEmb, ropeCache)
-				diff := mlx.Sub(posOutput, negOutput)
-				scaledDiff := mlx.MulScalar(diff, cfg.CFGScale)
-				output = mlx.Add(negOutput, scaledDiff)
+				noisePred = mlx.Add(negPred, scaledDiff)
 			} else {
+				// Non-CFG forward pass
 				output = m.Transformer.Forward(patches, timestep, posEmb, ropeCache)
-			}
-		}
+				noisePred = UnpatchifyLatents(output, tcfg.PatchSize, latentH, latentW, tcfg.InChannels)
+				noisePred = mlx.Neg(noisePred)
 
-		noisePred := UnpatchifyLatents(output, tcfg.PatchSize, latentH, latentW, tcfg.InChannels)
-		noisePred = mlx.Neg(noisePred)
+				// Update TeaCache
+				if teaCache != nil {
+					teaCache.UpdateCache(noisePred, tCurr)
+					mlx.Keep(teaCache.Arrays()...)
+				}
+			}
+		} else if useCFG && teaCache != nil && teaCache.HasCFGCache() {
+			// CFG mode: get cached pos/neg and compute CFG fresh
+			posPred, negPred := teaCache.GetCFGCached()
+			diff := mlx.Sub(posPred, negPred)
+			scaledDiff := mlx.MulScalar(diff, cfg.CFGScale)
+			noisePred = mlx.Add(negPred, scaledDiff)
+			fmt.Printf("    [TeaCache: reusing cached pos/neg outputs]\n")
+		} else {
+			// Non-CFG mode: reuse cached noise prediction
+			noisePred = teaCache.GetCached()
+			fmt.Printf("    [TeaCache: reusing cached output]\n")
+		}
 
 		oldLatents := latents
 		latents = scheduler.Step(noisePred, latents, i)
 
-		// Keep latents and any cached arrays
-		if stepCache != nil {
-			mlx.Keep(stepCache.Arrays()...)
-		}
 		mlx.Eval(latents)
 		oldLatents.Free()
 
@@ -361,8 +438,14 @@ func (m *Model) generate(ctx context.Context, cfg *GenerateConfig) (*mlx.Array,
 	ropeCache.CapSin.Free()
 	ropeCache.UnifiedCos.Free()
 	ropeCache.UnifiedSin.Free()
-	if stepCache != nil {
-		stepCache.Free()
+	if batchedEmb != nil {
+		batchedEmb.Free()
+	}
+	if teaCache != nil {
+		hits, misses := teaCache.Stats()
+		fmt.Printf("  TeaCache stats: %d hits, %d misses (%.1f%% cache rate)\n",
+			hits, misses, float64(hits)/float64(hits+misses)*100)
+		teaCache.Free()
 	}
 
 	// VAE decode

x/imagegen/nn/nn.go

@@ -10,6 +10,13 @@ type Layer interface {
 	Forward(x *mlx.Array) *mlx.Array
 }
 
+// LinearLayer is an interface for linear layers (both regular and quantized).
+// This allows swapping between Linear and QuantizedLinear at runtime.
+type LinearLayer interface {
+	Forward(x *mlx.Array) *mlx.Array
+	OutputDim() int32 // Returns the output dimension of the layer
+}
+
 // Linear applies an affine transformation: y = x @ W.T + b
 // Weight is stored as [out_features, in_features], matching PyTorch/MLX convention.
 type Linear struct {
@@ -49,6 +56,11 @@ func (l *Linear) Forward(x *mlx.Array) *mlx.Array {
 	return mlx.Linear(x, w)
 }
 
+// OutputDim returns the output dimension of the linear layer.
+func (l *Linear) OutputDim() int32 {
+	return l.Weight.Shape()[0]
+}
+
 // ToQuantized converts this Linear to a QuantizedLinear.
 func (l *Linear) ToQuantized(groupSize, bits int, mode string) *QuantizedLinear {
 	qw, scales, qbiases := mlx.Quantize(l.Weight, groupSize, bits, mode)
@@ -84,6 +96,13 @@ func (ql *QuantizedLinear) Forward(x *mlx.Array) *mlx.Array {
 	return out
 }
 
+// OutputDim returns the output dimension of the quantized linear layer.
+// For mxfp8/mxfp4, quantized weight shape is [out_features, in_features / group_size].
+// The output dimension is the first dimension of the weight.
+func (ql *QuantizedLinear) OutputDim() int32 {
+	return ql.Weight.Shape()[0]
+}
+
 // RMSNorm represents an RMS normalization layer.
 type RMSNorm struct {
 	Weight *mlx.Array `weight:"weight"`

x/imagegen/nn/nn_test.go

@@ -3,12 +3,34 @@
 package nn
 
 import (
+	"fmt"
 	"math"
+	"os"
+	"path/filepath"
+	"runtime"
 	"testing"
 
 	"github.com/ollama/ollama/x/imagegen/mlx"
 )
 
+// TestMain initializes MLX before running tests.
+// If MLX libraries are not available, tests are skipped.
+func TestMain(m *testing.M) {
+	// Change to repo root so ./build/lib/ollama/ path works
+	_, thisFile, _, _ := runtime.Caller(0)
+	repoRoot := filepath.Join(filepath.Dir(thisFile), "..", "..", "..")
+	if err := os.Chdir(repoRoot); err != nil {
+		fmt.Printf("Failed to change to repo root: %v\n", err)
+		os.Exit(1)
+	}
+
+	if err := mlx.InitMLX(); err != nil {
+		fmt.Printf("Skipping nn tests: %v\n", err)
+		os.Exit(0)
+	}
+	os.Exit(m.Run())
+}
+
 // TestLinearNoBias verifies Linear without bias computes x @ w.T correctly.
 func TestLinearNoBias(t *testing.T) {
 	// Weight: [out=2, in=3] -> transposed at forward time

x/imagegen/runner/runner.go

@@ -13,7 +13,6 @@ import (
 	"net/http"
 	"os"
 	"os/signal"
-	"path/filepath"
 	"sync"
 	"syscall"
 	"time"
@@ -34,8 +33,11 @@ type Request struct {
 
 // Response is streamed back for each progress update
 type Response struct {
-	Content string `json:"content"`
+	Content string `json:"content,omitempty"`
+	Image   string `json:"image,omitempty"` // Base64-encoded PNG
 	Done    bool   `json:"done"`
+	Step    int    `json:"step,omitempty"`
+	Total   int    `json:"total,omitempty"`
 }
 
 // Server holds the model and handles requests
@@ -62,6 +64,12 @@ func Execute(args []string) error {
 		return fmt.Errorf("--port is required")
 	}
 
+	err := mlx.InitMLX()
+	if err != nil {
+		slog.Error("unable to initialize MLX", "error", err)
+		return err
+	}
+	slog.Info("MLX library initialized")
 	slog.Info("starting image runner", "model", *modelName, "port", *port)
 
 	// Check memory requirements before loading
@@ -136,16 +144,8 @@ func (s *Server) completionHandler(w http.ResponseWriter, r *http.Request) {
 	s.mu.Lock()
 	defer s.mu.Unlock()
 
-	// Apply defaults
-	if req.Width <= 0 {
-		req.Width = 1024
-	}
-	if req.Height <= 0 {
-		req.Height = 1024
-	}
-	if req.Steps <= 0 {
-		req.Steps = 9
-	}
+	// Model applies its own defaults for width/height/steps
+	// Only seed needs to be set here if not provided
 	if req.Seed <= 0 {
 		req.Seed = time.Now().UnixNano()
 	}
@@ -169,8 +169,9 @@ func (s *Server) completionHandler(w http.ResponseWriter, r *http.Request) {
 		Seed:   req.Seed,
 		Progress: func(step, total int) {
 			resp := Response{
-				Content: fmt.Sprintf("\rGenerating: step %d/%d", step, total),
-				Done:    false,
+				Step:  step,
+				Total: total,
+				Done:  false,
 			}
 			data, _ := json.Marshal(resp)
 			w.Write(data)
@@ -191,10 +192,10 @@ func (s *Server) completionHandler(w http.ResponseWriter, r *http.Request) {
 		return
 	}
 
-	// Save image
-	outPath := filepath.Join(os.TempDir(), fmt.Sprintf("ollama-image-%d.png", time.Now().UnixNano()))
-	if err := imagegen.SaveImage(img, outPath); err != nil {
-		resp := Response{Content: fmt.Sprintf("error saving: %v", err), Done: true}
+	// Encode image as base64 PNG
+	imageData, err := imagegen.EncodeImageBase64(img)
+	if err != nil {
+		resp := Response{Content: fmt.Sprintf("error encoding: %v", err), Done: true}
 		data, _ := json.Marshal(resp)
 		w.Write(data)
 		w.Write([]byte("\n"))
@@ -204,11 +205,12 @@ func (s *Server) completionHandler(w http.ResponseWriter, r *http.Request) {
 	// Free the generated image array and clean up MLX state
 	img.Free()
 	mlx.ClearCache()
+	mlx.MetalResetPeakMemory()
 
-	// Send final response
+	// Send final response with image data
 	resp := Response{
-		Content: fmt.Sprintf("\n\nImage saved to: %s\n", outPath),
-		Done:    true,
+		Image: imageData,
+		Done:  true,
 	}
 	data, _ := json.Marshal(resp)
 	w.Write(data)