Add experimental image generation fields to /api/generate

Request fields (experimental):
- width: image width (max 4096)
- height: image height (max 4096)
- steps: denoising steps
- seed: random seed

Response fields (experimental):
- images: base64-encoded generated images
- completed: current step progress
- total: total steps

Other changes:
- Fix lifecycle bug where image models wouldn't unload (refCount issue)
- Fix "headers already written" error on Ctrl+C during streaming
- Add gin middleware for OpenAI /v1/images/generations compatibility
- Update CLI to use /api/generate with progress bar
- Add preload support in interactive mode

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
@@ -156,14 +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
-RUN mkdir -p dist/bin
-RUN --mount=type=cache,target=/root/.cache/go-build \
-    go build -tags mlx -trimpath -buildmode=pie -o dist/bin/ollama-mlx .
 
 FROM base AS build
 WORKDIR /go/src/github.com/ollama/ollama
@@ -172,12 +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/
@@ -185,7 +180,6 @@ COPY --from=cuda-12 dist/lib/ollama /lib/ollama/
 COPY --from=cuda-13 dist/lib/ollama /lib/ollama/
 COPY --from=vulkan  dist/lib/ollama  /lib/ollama/
 COPY --from=mlx     /go/src/github.com/ollama/ollama/dist/lib/ollama /lib/ollama/
-COPY --from=mlx     /go/src/github.com/ollama/ollama/dist/bin/ /bin/
 
 FROM --platform=linux/arm64 scratch AS arm64
 # COPY --from=cuda-11 dist/lib/ollama/ /lib/ollama/

MLX_VERSION

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

README.md

@@ -270,10 +270,10 @@ cmake --build --preset MLX --parallel
 cmake --install build --component MLX
 ```
 
-Next, build the `ollama-mlx` binary, which is a separate build of the Ollama runtime with MLX support enabled (needs to be in the same directory as `ollama`):
+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 -o ollama-mlx .
+go build -tags mlx .
 ```
 
 Finally, start the server:
@@ -322,6 +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)

api/types.go

@@ -127,6 +127,25 @@ 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"`
+
+	// Seed is the random seed for reproducible image generation.
+	// If 0 or not specified, a random seed will be used.
+	// Only used for image generation models.
+	Seed int64 `json:"seed,omitempty"`
 }
 
 // ChatRequest describes a request sent by [Client.Chat].
@@ -860,6 +879,20 @@ 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)
+
+	// Images contains base64-encoded generated images.
+	// Only present for image generation models.
+	Images []string `json:"images,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"
@@ -93,15 +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(".") {
+			if create.IsTensorModelDir(".") {
 				quantize, _ := cmd.Flags().GetString("quantize")
-				return imagegenclient.CreateModel(args[0], ".", quantize, p)
+				return xcreateclient.CreateModel(xcreateclient.CreateOptions{
+					ModelName: modelName,
+					ModelDir:  ".",
+					Quantize:  quantize,
+				}, p)
 			}
 			reader = strings.NewReader("FROM .\n")
 		} else {
@@ -134,7 +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
@@ -527,7 +600,7 @@ func RunHandler(cmd *cobra.Command, args []string) error {
 	}
 
 	// Check if this is an image generation model
-	if slices.Contains(info.Capabilities, model.CapabilityImageGeneration) {
+	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\"")
 		}
@@ -1742,15 +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",
@@ -1905,6 +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

@@ -1555,7 +1555,7 @@ func TestShowInfoImageGen(t *testing.T) {
 			ParameterSize:     "10.3B",
 			QuantizationLevel: "FP8",
 		},
-		Capabilities: []model.Capability{model.CapabilityImageGeneration},
+		Capabilities: []model.Capability{model.CapabilityImage},
 		Requires:     "0.14.0",
 	}, false, &b)
 	if err != nil {

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 +59,16 @@ 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 (default: model-specific, typically 1024)
+- `height`: height of the generated image in pixels (default: model-specific, typically 1024)
+- `steps`: number of diffusion steps (default: model-specific)
+- `seed`: random seed for reproducible image generation (default: random)
+
 #### 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 +1878,55 @@ 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 (such as Flux). 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`, `seed`) are documented there.
+
+#### Example
+
+##### Request
+
+```shell
+curl http://localhost:11434/api/generate -d '{
+  "model": "flux",
+  "prompt": "a sunset over mountains",
+  "width": 1024,
+  "height": 768
+}'
+```
+
+##### Response (streaming)
+
+Progress updates during generation:
+
+```json
+{
+  "model": "flux",
+  "created_at": "2024-01-15T10:30:00.000000Z",
+  "completed": 5,
+  "total": 20,
+  "done": false
+}
+```
+
+##### Final Response
+
+```json
+{
+  "model": "flux",
+  "created_at": "2024-01-15T10:30:15.000000Z",
+  "images": ["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/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
-```
+```

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/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 images
+	if generateResponse.Done && len(generateResponse.Images) > 0 {
+		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,
+			Images:    []string{"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)
+	}
+}

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 +737,57 @@ 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 {
+		req.Seed = *r.Seed
+	}
+	return req
+}
+
+// ToImageGenerationResponse converts an Ollama GenerateResponse to an OpenAI ImageGenerationResponse.
+func ToImageGenerationResponse(resp api.GenerateResponse) ImageGenerationResponse {
+	data := make([]ImageURLOrData, 0)
+	for _, img := range resp.Images {
+		data = append(data, ImageURLOrData{B64JSON: img})
+	}
+	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 $INSTALL_PREFIX/ollama-mlx .
-        GOOS=darwin GOARCH=$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
 }
 
@@ -82,12 +84,10 @@ _sign_darwin() {
     status "Creating universal binary..."
     mkdir -p dist/darwin
     lipo -create -output dist/darwin/ollama dist/darwin-*/ollama
-    lipo -create -output dist/darwin/ollama-mlx dist/darwin-*/ollama-mlx
     chmod +x dist/darwin/ollama
-    chmod +x dist/darwin/ollama-mlx
 
     if [ -n "$APPLE_IDENTITY" ]; then
-        for F in dist/darwin/ollama dist/darwin-*/lib/ollama/* dist/darwin/ollama-mlx; do
+        for F in dist/darwin/ollama dist/darwin-*/lib/ollama/*; do
             codesign -f --timestamp -s "$APPLE_IDENTITY" --identifier ai.ollama.ollama --options=runtime $F
         done
 
@@ -154,7 +154,6 @@ _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/ollama-mlx dist/darwin-amd64/ollama-mlx dist/darwin-arm64/ollama-mlx
         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
@@ -166,28 +165,27 @@ _build_macapp() {
         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/ollama-mlx dist/Ollama.app/Contents/Resources/ollama-mlx
     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/*.metallib dist/Ollama.app/Contents/Resources/ollama-mlx ; 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 ollama-mlx *.so *.dylib *.metallib 2>/dev/null) | 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
         $(xcrun -f notarytool) submit dist/Ollama-darwin.zip --wait --timeout 20m --apple-id "$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
 

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/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
@@ -1125,7 +1102,7 @@ func GetModelInfo(req api.ShowRequest) (*api.ShowResponse, error) {
 	}
 
 	// For image generation models, populate details from imagegen package
-	if slices.Contains(m.Capabilities(), model.CapabilityImageGeneration) {
+	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))
@@ -1133,6 +1110,22 @@ func GetModelInfo(req api.ShowRequest) (*api.ShowResponse, error) {
 		}
 	}
 
+	// 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
 	}
@@ -1215,7 +1208,27 @@ func GetModelInfo(req api.ShowRequest) (*api.ShowResponse, error) {
 		return resp, nil
 	}
 
-	if slices.Contains(m.Capabilities(), model.CapabilityImageGeneration) {
+	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
 	}
 
@@ -1587,13 +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{
@@ -2460,3 +2472,78 @@ 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 ImageGeneration 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")
+
+	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:   req.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.Images = []string{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/cmd/run.go

@@ -25,14 +25,6 @@ import (
 	"github.com/ollama/ollama/x/tools"
 )
 
-// MultilineState tracks the state of multiline input
-type MultilineState int
-
-const (
-	MultilineNone MultilineState = iota
-	MultilineSystem
-)
-
 // Tool output capping constants
 const (
 	// localModelTokenLimit is the token limit for local models (smaller context).
@@ -656,7 +648,7 @@ func GenerateInteractive(cmd *cobra.Command, modelName string, wordWrap bool, op
 		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
@@ -707,7 +699,6 @@ func GenerateInteractive(cmd *cobra.Command, modelName string, wordWrap bool, op
 	var sb strings.Builder
 	var format string
 	var system string
-	var multiline MultilineState = MultilineNone
 
 	for {
 		line, err := scanner.Readline()
@@ -721,37 +712,12 @@ func GenerateInteractive(cmd *cobra.Command, modelName string, wordWrap bool, op
 			}
 			scanner.Prompt.UseAlt = false
 			sb.Reset()
-			multiline = MultilineNone
 			continue
 		case err != nil:
 			return err
 		}
 
 		switch {
-		case multiline != MultilineNone:
-			// check if there's a multiline terminating string
-			before, ok := strings.CutSuffix(line, `"""`)
-			sb.WriteString(before)
-			if !ok {
-				fmt.Fprintln(&sb)
-				continue
-			}
-
-			switch multiline {
-			case MultilineSystem:
-				system = sb.String()
-				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.")
-				sb.Reset()
-			}
-
-			multiline = MultilineNone
-			scanner.Prompt.UseAlt = false
 		case strings.HasPrefix(line, "/exit"), strings.HasPrefix(line, "/bye"):
 			return nil
 		case strings.HasPrefix(line, "/clear"):
@@ -860,41 +826,18 @@ func GenerateInteractive(cmd *cobra.Command, modelName string, wordWrap bool, op
 					options[args[2]] = fp[args[2]]
 				case "system":
 					if len(args) < 3 {
-						fmt.Println("Usage: /set system <message> or /set system \"\"\"<multi-line message>\"\"\"")
+						fmt.Println("Usage: /set system <message>")
 						continue
 					}
 
-					multiline = MultilineSystem
-
-					line := strings.Join(args[2:], " ")
-					line, ok := strings.CutPrefix(line, `"""`)
-					if !ok {
-						multiline = MultilineNone
-					} else {
-						// only cut suffix if the line is multiline
-						line, ok = strings.CutSuffix(line, `"""`)
-						if ok {
-							multiline = MultilineNone
-						}
-					}
-
-					sb.WriteString(line)
-					if multiline != MultilineNone {
-						scanner.Prompt.UseAlt = true
-						continue
-					}
-
-					system = sb.String()
-					newMessage := api.Message{Role: "system", Content: sb.String()}
-					// Check if the slice is not empty and the last message is from 'system'
+					system = strings.Join(args[2:], " ")
+					newMessage := api.Message{Role: "system", Content: system}
 					if len(messages) > 0 && messages[len(messages)-1].Role == "system" {
-						// Replace the last message
 						messages[len(messages)-1] = newMessage
 					} else {
 						messages = append(messages, newMessage)
 					}
 					fmt.Println("Set system message.")
-					sb.Reset()
 					continue
 				default:
 					fmt.Printf("Unknown command '/set %s'. Type /? for help\n", args[1])
@@ -1081,7 +1024,7 @@ func GenerateInteractive(cmd *cobra.Command, modelName string, wordWrap bool, op
 			sb.WriteString(line)
 		}
 
-		if sb.Len() > 0 && multiline == MultilineNone {
+		if sb.Len() > 0 {
 			newMessage := api.Message{Role: "user", Content: sb.String()}
 			messages = append(messages, newMessage)
 

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

@@ -11,10 +11,11 @@ import (
 	"github.com/ollama/ollama/x/imagegen/mlx"
 )
 
-// quantizeTensor loads a tensor from safetensors format, quantizes it to affine int8,
+// 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) (qweightData, scalesData, qbiasData []byte, qweightShape, scalesShape, qbiasShape []int32, err error) {
+func quantizeTensor(r io.Reader, name, dtype string, shape []int32, quantize string) (qweightData, scalesData, qbiasData []byte, qweightShape, scalesShape, qbiasShape []int32, err error) {
 	tmpDir := ensureTempDir()
 
 	// Read safetensors data to a temp file (LoadSafetensorsNative needs a path)
@@ -50,9 +51,15 @@ func quantizeTensor(r io.Reader, name, dtype string, shape []int32) (qweightData
 		mlx.Eval(arr)
 	}
 
-	// Quantize with affine mode: group_size=32, bits=8
-	// Note: mxfp8 mode doesn't have matmul kernels in MLX, affine mode does
-	qweight, scales, qbiases := mlx.Quantize(arr, 32, 8, "affine")
+	// 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)

x/create/client/quantize_stub.go

@@ -8,7 +8,7 @@ import (
 )
 
 // quantizeTensor is not available without MLX
-func quantizeTensor(r io.Reader, name, dtype string, shape []int32) (qweightData, scalesData, qbiasData []byte, qweightShape, scalesShape, qbiasShape []int32, err error) {
+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)")
 }
 

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"
@@ -12,43 +12,27 @@ 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 "fp8", linear weights in transformer/text_encoder are quantized to mxfp8 format.
+// 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, quantize string, createLayer LayerCreator, createTensorLayer QuantizingTensorLayerCreator, 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", "vision_language_encoder"}
+	components := []string{"text_encoder", "transformer", "vae"}
 
 	for _, component := range components {
 		componentDir := filepath.Join(modelDir, component)
@@ -77,8 +61,8 @@ func CreateModel(modelName, modelDir, quantize string, createLayer LayerCreator,
 
 			tensorNames := extractor.ListTensors()
 			quantizeMsg := ""
-			if quantize == "fp8" && component != "vae" {
-				quantizeMsg = ", quantizing to fp8"
+			if quantize != "" && component != "vae" {
+				quantizeMsg = ", quantizing to " + quantize
 			}
 			fn(fmt.Sprintf("importing %s/%s (%d tensors%s)", component, entry.Name(), len(tensorNames), quantizeMsg))
 
@@ -103,11 +87,14 @@ func CreateModel(modelName, modelDir, quantize string, createLayer LayerCreator,
 				// Use path-style name: "component/tensor_name"
 				fullName := component + "/" + tensorName
 
-				// Determine if this tensor should be quantized
-				doQuantize := quantize == "fp8" && ShouldQuantize(tensorName, component)
+				// 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, doQuantize)
+				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)
@@ -119,6 +106,19 @@ func CreateModel(modelName, modelDir, quantize string, createLayer LayerCreator,
 		}
 	}
 
+	// 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",
@@ -126,13 +126,10 @@ func CreateModel(modelName, modelDir, quantize string, createLayer LayerCreator,
 		"text_encoder/generation_config.json",
 		"transformer/config.json",
 		"vae/config.json",
-		"vision_language_encoder/config.json",
 		"scheduler/scheduler_config.json",
 		"tokenizer/tokenizer.json",
 		"tokenizer/tokenizer_config.json",
 		"tokenizer/vocab.json",
-		"processor/tokenizer.json",        // GLM-Image main tokenizer
-		"processor/tokenizer_config.json", // GLM-Image tokenizer config
 	}
 
 	for _, cfgPath := range configFiles {
@@ -167,11 +164,11 @@ func CreateModel(modelName, modelDir, quantize string, createLayer LayerCreator,
 			// Add parameter count (counted from tensor shapes during import)
 			cfg["parameter_count"] = totalParams
 
-			// Add quantization info
-			if quantize == "fp8" {
-				cfg["quantization"] = "FP8"
+			// Add quantization info - use quantize type if set, otherwise torch_dtype
+			if quantize != "" {
+				cfg["quantization"] = strings.ToUpper(quantize)
 			} else {
-				cfg["quantization"] = "BF16"
+				cfg["quantization"] = torchDtype
 			}
 
 			data, err = json.MarshalIndent(cfg, "", "    ")
@@ -214,3 +211,12 @@ func CreateModel(modelName, modelDir, quantize string, createLayer LayerCreator,
 	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/api/handler.go

@@ -1,231 +0,0 @@
-package api
-
-import (
-	"fmt"
-	"net/http"
-	"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 imageBase64 string
-	err := runner.Completion(c.Request.Context(), req, func(resp llm.CompletionResponse) {
-		if resp.Done {
-			imageBase64 = extractBase64(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(imageBase64, format))
-}
-
-func handleNonStreamingResponse(c *gin.Context, runner llm.LlamaServer, req llm.CompletionRequest, format string) {
-	var imageBase64 string
-	err := runner.Completion(c.Request.Context(), req, func(resp llm.CompletionResponse) {
-		if resp.Done {
-			imageBase64 = extractBase64(resp.Content)
-		}
-	})
-	if err != nil {
-		c.JSON(http.StatusInternalServerError, gin.H{"error": gin.H{"message": err.Error()}})
-		return
-	}
-
-	c.JSON(http.StatusOK, buildResponse(imageBase64, 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 extractBase64(content string) string {
-	if strings.HasPrefix(content, "IMAGE_BASE64:") {
-		return content[13:]
-	}
-	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(imageBase64, format string) ImageGenerationResponse {
-	resp := ImageGenerationResponse{
-		Created: time.Now().Unix(),
-		Data:    make([]ImageData, 1),
-	}
-
-	if imageBase64 == "" {
-		return resp
-	}
-
-	if format == "url" {
-		// URL format not supported when using base64 transfer
-		resp.Data[0].B64JSON = imageBase64
-	} else {
-		resp.Data[0].B64JSON = imageBase64
-	}
-
-	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/cli.go

@@ -7,7 +7,6 @@ package imagegen
 
 import (
 	"encoding/base64"
-	"encoding/json"
 	"errors"
 	"fmt"
 	"io"
@@ -39,79 +38,20 @@ func DefaultOptions() ImageGenOptions {
 	return ImageGenOptions{
 		Width:  1024,
 		Height: 1024,
-		Steps:  9,
+		Steps:  0, // 0 means model default
 		Seed:   0, // 0 means random
 	}
 }
 
-// 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
-}
-
 // 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")
@@ -119,6 +59,19 @@ 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
@@ -158,17 +111,13 @@ 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),
+		Seed:   int64(opts.Seed),
 	}
 	if keepAlive != nil {
 		req.KeepAlive = keepAlive
@@ -182,32 +131,25 @@ func generateImageWithOptions(cmd *cobra.Command, modelName, prompt string, keep
 	var stepBar *progress.StepBar
 	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 base64 image data
-		if resp.Done && strings.HasPrefix(content, "IMAGE_BASE64:") {
-			imageBase64 = content[13:]
+		// Handle final response with image data
+		if resp.Done && len(resp.Images) > 0 {
+			imageBase64 = resp.Images[0]
 		}
 
 		return nil
 	})
 
-	p.Stop()
+	p.StopAndClear()
 	if err != nil {
 		return err
 	}
@@ -245,6 +187,23 @@ 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)",
@@ -301,12 +260,10 @@ 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),
+			Seed:   int64(opts.Seed),
 		}
 		if keepAlive != nil {
 			req.KeepAlive = keepAlive
@@ -321,32 +278,25 @@ func runInteractive(cmd *cobra.Command, modelName string, keepAlive *api.Duratio
 		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 base64 image data
-			if resp.Done && strings.HasPrefix(content, "IMAGE_BASE64:") {
-				imageBase64 = content[13:]
+			// Handle final response with image data
+			if resp.Done && len(resp.Images) > 0 {
+				imageBase64 = resp.Images[0]
 			}
 
 			return nil
 		})
 
-		p.Stop()
+		p.StopAndClear()
 		if err != nil {
 			fmt.Fprintf(os.Stderr, "Error: %v\n", err)
 			continue

x/imagegen/client/create.go

@@ -1,190 +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.
-// If quantize is "fp8", weights will be quantized to mxfp8 format during import.
-//
-// TODO (jmorganca): Replace with API-based creation when promoted to production.
-func CreateModel(modelName, modelDir, quantize 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"
-	// When quantize is true, returns multiple layers (weight + scales)
-	createTensorLayer := func(r io.Reader, name, dtype string, shape []int32, doQuantize bool) ([]imagegen.LayerInfo, error) {
-		if doQuantize {
-			// Check if quantization is supported
-			if !QuantizeSupported() {
-				return nil, fmt.Errorf("quantization requires MLX support")
-			}
-
-			// Quantize the tensor (affine mode returns weight, scales, qbiases)
-			qweightData, scalesData, qbiasData, _, _, _, err := quantizeTensor(r, name, dtype, shape)
-			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 (use _scale suffix convention)
-			scalesLayer, err := server.NewLayer(bytes.NewReader(scalesData), server.MediaTypeImageTensor)
-			if err != nil {
-				return nil, err
-			}
-
-			layers := []imagegen.LayerInfo{
-				{
-					Digest:    weightLayer.Digest,
-					Size:      weightLayer.Size,
-					MediaType: weightLayer.MediaType,
-					Name:      name, // Keep original name for weight
-				},
-				{
-					Digest:    scalesLayer.Digest,
-					Size:      scalesLayer.Size,
-					MediaType: scalesLayer.MediaType,
-					Name:      name + "_scale", // Add _scale suffix
-				},
-			}
-
-			// 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, imagegen.LayerInfo{
-					Digest:    qbiasLayer.Digest,
-					Size:      qbiasLayer.Size,
-					MediaType: qbiasLayer.MediaType,
-					Name:      name + "_qbias", // Add _qbias suffix
-				})
-			}
-
-			return layers, nil
-		}
-
-		// Non-quantized path: just create a single layer
-		layer, err := server.NewLayer(r, server.MediaTypeImageTensor)
-		if err != nil {
-			return nil, err
-		}
-
-		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, quantize, 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

@@ -11,11 +11,9 @@ import (
 	"os"
 	"path/filepath"
 	"runtime/pprof"
-	"strings"
 
 	"github.com/ollama/ollama/x/imagegen/mlx"
 	"github.com/ollama/ollama/x/imagegen/models/gemma3"
-	"github.com/ollama/ollama/x/imagegen/models/glm_image"
 	"github.com/ollama/ollama/x/imagegen/models/gpt_oss"
 	"github.com/ollama/ollama/x/imagegen/models/llama"
 	"github.com/ollama/ollama/x/imagegen/models/qwen_image"
@@ -50,7 +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")
 
@@ -63,7 +61,6 @@ func main() {
 
 	// Legacy mode flags
 	zimageFlag := flag.Bool("zimage", false, "Z-Image generation")
-	glmImageFlag := flag.Bool("glm-image", false, "GLM-Image generation")
 	qwenImage := flag.Bool("qwen-image", false, "Qwen-Image text-to-image generation")
 	qwenImageEdit := flag.Bool("qwen-image-edit", false, "Qwen-Image-Edit image editing")
 	var inputImages stringSlice
@@ -81,6 +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)
@@ -120,33 +122,6 @@ func main() {
 		if err == nil {
 			err = saveImageArray(img, *out)
 		}
-	case *glmImageFlag:
-		m := &glm_image.Model{}
-		// Use LoadFromPath if model path looks like a directory, otherwise use Load (ollama manifest)
-		var loadErr error
-		if strings.HasPrefix(*modelPath, ".") || strings.HasPrefix(*modelPath, "/") {
-			loadErr = m.LoadFromPath(*modelPath)
-		} else {
-			loadErr = m.Load(*modelPath)
-		}
-		if loadErr != nil {
-			log.Fatal(loadErr)
-		}
-		var img *mlx.Array
-		img, err = m.GenerateFromConfig(context.Background(), &glm_image.GenerateConfig{
-			Prompt:          *prompt,
-			Width:           int32(*width),
-			Height:          int32(*height),
-			Steps:           *steps,
-			Seed:            *seed,
-			Temperature:     float32(*temperature),
-			TopP:            float32(*topP),
-			GuidanceScale:   float32(*cfgScale),
-			MaxVisualTokens: int32(*maxTokens),
-		})
-		if err == nil {
-			err = saveImageArray(img, *out)
-		}
 	case *qwenImage:
 		m, loadErr := qwen_image.LoadPersistent(*modelPath)
 		if loadErr != nil {

x/imagegen/imagegen.md

@@ -1,19 +0,0 @@
-# Image generation models (experimental)
-
-Experimental image generation models are available for **macOS** in Ollama:
-
-## Available models
-
-- [Z-Image-Turbo](https://ollama.com/x/z-image-turbo)
-
-```
-ollama run x/z-image-turbo
-```
-
-> **Note**: [`x`](https://ollama.com/x) is a username on ollama.com where the maintainer team uploads experimental models
-
-More models coming soon:
-
-1. Qwen-Image-2512
-2. Qwen-Image-Edit-2511
-3. GLM-Image

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

@@ -27,7 +27,6 @@ var modelVRAMEstimates = map[string]uint64{
 	"ZImagePipeline":    21 * GB, // ~21GB for Z-Image (text encoder + transformer + VAE)
 	"FluxPipeline":      21 * GB, // ~21GB for Flux (same architecture)
 	"QwenImagePipeline": 80 * GB, // TODO: verify actual requirements, using conservative estimate for now
-	"GlmImagePipeline":  80 * GB, // ~34GB weights + ~46GB working memory for 9B+7B hybrid model
 }
 
 // CheckPlatformSupport validates that image generation is supported on the current platform.
@@ -96,8 +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 (
@@ -1796,7 +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()

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/glm_image/glm_image.go

@@ -1,693 +0,0 @@
-//go:build mlx
-
-// Package glm_image implements the GLM-Image hybrid AR + diffusion model.
-package glm_image
-
-import (
-	"context"
-	"fmt"
-	"math"
-	"path/filepath"
-	"time"
-
-	"github.com/ollama/ollama/x/imagegen"
-	"github.com/ollama/ollama/x/imagegen/mlx"
-)
-
-// ByT5Tokenizer is a simple byte-level tokenizer for ByT5
-// ByT5 uses bytes as tokens: each byte (0-255) maps to token ID (3-258)
-// Special tokens: 0=pad, 1=eos, 2=unk
-type ByT5Tokenizer struct {
-	PadTokenID int32
-	EOSTokenID int32
-	UNKTokenID int32
-}
-
-// NewByT5Tokenizer creates a new ByT5 tokenizer
-func NewByT5Tokenizer() *ByT5Tokenizer {
-	return &ByT5Tokenizer{
-		PadTokenID: 0,
-		EOSTokenID: 1,
-		UNKTokenID: 2,
-	}
-}
-
-// Encode converts a string to token IDs
-func (t *ByT5Tokenizer) Encode(text string) []int32 {
-	bytes := []byte(text)
-	tokens := make([]int32, len(bytes))
-	for i, b := range bytes {
-		// Standard ByT5 tokenization: bytes 0-255 map to tokens 3-258
-		// (tokens 0, 1, 2 are PAD, EOS, UNK)
-		tokens[i] = int32(b) + 3
-	}
-	return tokens
-}
-
-// Decode converts token IDs back to a string
-func (t *ByT5Tokenizer) Decode(tokens []int32) string {
-	bytes := make([]byte, 0, len(tokens))
-	for _, tok := range tokens {
-		if tok >= 3 && tok < 259 {
-			bytes = append(bytes, byte(tok-3))
-		}
-	}
-	return string(bytes)
-}
-
-// GenerateConfig holds all options for image generation.
-type GenerateConfig struct {
-	Prompt         string
-	NegativePrompt string       // For CFG (optional, not typically used with GLM-Image)
-	GuidanceScale  float32      // Guidance scale (default: 1.5)
-	Width          int32        // Image width (default: 1024, must be divisible by 32)
-	Height         int32        // Image height (default: 1024, must be divisible by 32)
-	Steps          int          // Diffusion denoising steps (default: 50)
-	Seed           int64        // Random seed
-	Progress       ProgressFunc // Optional progress callback
-
-	// AR generation options
-	MaxVisualTokens int32   // Max visual tokens to generate (default: 256)
-	Temperature     float32 // AR sampling temperature (default: 0.9)
-	TopP            float32 // Nucleus sampling (default: 0.75)
-}
-
-// ProgressFunc is called during generation with stage and step progress.
-type ProgressFunc func(stage string, step, totalSteps int)
-
-// Model represents a GLM-Image hybrid model.
-type Model struct {
-	ModelName             string
-	Tokenizer             *ByT5Tokenizer   // For T5 text encoder (glyph embeddings)
-	GLMTokenizer          *GLMTokenizer    // For AR model (visual token generation)
-	TextEncoder           *T5TextEncoder
-	VisionLanguageEncoder *VisionLanguageEncoder
-	Transformer           *DiffusionTransformer
-	VAEDecoder            *VAEDecoder
-}
-
-// Load loads the GLM-Image model from ollama blob storage.
-func (m *Model) Load(modelName string) error {
-	fmt.Printf("Loading GLM-Image model from manifest: %s...\n", modelName)
-	start := time.Now()
-
-	if mlx.GPUIsAvailable() {
-		mlx.SetDefaultDeviceGPU()
-		mlx.EnableCompile()
-	}
-
-	m.ModelName = modelName
-
-	// Load manifest
-	manifest, err := imagegen.LoadManifest(modelName)
-	if err != nil {
-		return fmt.Errorf("load manifest: %w", err)
-	}
-
-	// Create ByT5 tokenizer (byte-level, no vocabulary file needed)
-	// Used for T5 text encoder (glyph embeddings)
-	fmt.Print("  Creating ByT5 tokenizer... ")
-	m.Tokenizer = NewByT5Tokenizer()
-	fmt.Println("✓")
-
-	// Load GLM tokenizer for AR model (visual token generation)
-	fmt.Print("  Loading GLM tokenizer... ")
-	glmTok, err := NewGLMTokenizer(manifest)
-	if err != nil {
-		return fmt.Errorf("glm tokenizer: %w", err)
-	}
-	m.GLMTokenizer = glmTok
-	fmt.Println("✓")
-
-	// Load T5 text encoder (~830MB)
-	m.TextEncoder = &T5TextEncoder{}
-	if err := m.TextEncoder.Load(manifest); err != nil {
-		return fmt.Errorf("text encoder: %w", err)
-	}
-	mlx.Eval(mlx.Collect(m.TextEncoder)...)
-	fmt.Printf("  (%.1f GB, peak %.1f GB)\n",
-		float64(mlx.MetalGetActiveMemory())/(1024*1024*1024),
-		float64(mlx.MetalGetPeakMemory())/(1024*1024*1024))
-
-	// Load vision-language encoder (~19GB, 9B params)
-	m.VisionLanguageEncoder = &VisionLanguageEncoder{}
-	if err := m.VisionLanguageEncoder.Load(manifest); err != nil {
-		return fmt.Errorf("vision language encoder: %w", err)
-	}
-	mlx.Eval(mlx.Collect(m.VisionLanguageEncoder)...)
-	fmt.Printf("  (%.1f GB, peak %.1f GB)\n",
-		float64(mlx.MetalGetActiveMemory())/(1024*1024*1024),
-		float64(mlx.MetalGetPeakMemory())/(1024*1024*1024))
-
-	// Load diffusion transformer (~13GB, 7B params)
-	m.Transformer = &DiffusionTransformer{}
-	if err := m.Transformer.Load(manifest); err != nil {
-		return fmt.Errorf("transformer: %w", err)
-	}
-	mlx.Eval(mlx.Collect(m.Transformer)...)
-	fmt.Printf("  (%.1f GB, peak %.1f GB)\n",
-		float64(mlx.MetalGetActiveMemory())/(1024*1024*1024),
-		float64(mlx.MetalGetPeakMemory())/(1024*1024*1024))
-
-	// Load VAE decoder (~775MB)
-	m.VAEDecoder = &VAEDecoder{}
-	if err := m.VAEDecoder.Load(manifest); err != nil {
-		return fmt.Errorf("VAE decoder: %w", err)
-	}
-	mlx.Eval(mlx.Collect(m.VAEDecoder)...)
-	fmt.Printf("  (%.1f GB, peak %.1f GB)\n",
-		float64(mlx.MetalGetActiveMemory())/(1024*1024*1024),
-		float64(mlx.MetalGetPeakMemory())/(1024*1024*1024))
-
-	mem := mlx.MetalGetActiveMemory()
-	fmt.Printf("  Loaded in %.2fs (%.1f GB VRAM)\n", time.Since(start).Seconds(), float64(mem)/(1024*1024*1024))
-
-	return nil
-}
-
-// LoadFromPath loads the model from a directory path (not ollama manifest)
-func (m *Model) LoadFromPath(modelPath string) error {
-	fmt.Printf("Loading GLM-Image model from path: %s...\n", modelPath)
-	start := time.Now()
-
-	if mlx.GPUIsAvailable() {
-		mlx.SetDefaultDeviceGPU()
-		mlx.EnableCompile()
-	}
-
-	m.ModelName = modelPath
-
-	// Create ByT5 tokenizer (byte-level, no vocabulary file needed)
-	fmt.Print("  Creating ByT5 tokenizer... ")
-	m.Tokenizer = NewByT5Tokenizer()
-	fmt.Println("✓")
-
-	// Load GLM tokenizer for AR model (visual token generation)
-	fmt.Print("  Loading GLM tokenizer... ")
-	glmTok, err := NewGLMTokenizerFromPath(modelPath)
-	if err != nil {
-		return fmt.Errorf("glm tokenizer: %w", err)
-	}
-	m.GLMTokenizer = glmTok
-	fmt.Println("✓")
-
-	// Load T5 text encoder
-	m.TextEncoder = &T5TextEncoder{}
-	if err := m.TextEncoder.LoadFromPath(filepath.Join(modelPath, "text_encoder")); err != nil {
-		return fmt.Errorf("text encoder: %w", err)
-	}
-	mlx.Eval(mlx.Collect(m.TextEncoder)...)
-	fmt.Printf("  (%.1f GB, peak %.1f GB)\n",
-		float64(mlx.MetalGetActiveMemory())/(1024*1024*1024),
-		float64(mlx.MetalGetPeakMemory())/(1024*1024*1024))
-
-	// Load vision-language encoder
-	m.VisionLanguageEncoder = &VisionLanguageEncoder{}
-	if err := m.VisionLanguageEncoder.LoadFromPath(filepath.Join(modelPath, "vision_language_encoder")); err != nil {
-		return fmt.Errorf("vision language encoder: %w", err)
-	}
-	mlx.Eval(mlx.Collect(m.VisionLanguageEncoder)...)
-	fmt.Printf("  (%.1f GB, peak %.1f GB)\n",
-		float64(mlx.MetalGetActiveMemory())/(1024*1024*1024),
-		float64(mlx.MetalGetPeakMemory())/(1024*1024*1024))
-
-	// Load diffusion transformer
-	m.Transformer = &DiffusionTransformer{}
-	if err := m.Transformer.LoadFromPath(filepath.Join(modelPath, "transformer")); err != nil {
-		return fmt.Errorf("transformer: %w", err)
-	}
-	mlx.Eval(mlx.Collect(m.Transformer)...)
-	fmt.Printf("  (%.1f GB, peak %.1f GB)\n",
-		float64(mlx.MetalGetActiveMemory())/(1024*1024*1024),
-		float64(mlx.MetalGetPeakMemory())/(1024*1024*1024))
-
-	// Load VAE decoder
-	m.VAEDecoder = &VAEDecoder{}
-	if err := m.VAEDecoder.LoadFromPath(filepath.Join(modelPath, "vae")); err != nil {
-		return fmt.Errorf("VAE decoder: %w", err)
-	}
-	mlx.Eval(mlx.Collect(m.VAEDecoder)...)
-	fmt.Printf("  (%.1f GB, peak %.1f GB)\n",
-		float64(mlx.MetalGetActiveMemory())/(1024*1024*1024),
-		float64(mlx.MetalGetPeakMemory())/(1024*1024*1024))
-
-	mem := mlx.MetalGetActiveMemory()
-	fmt.Printf("  Loaded in %.2fs (%.1f GB VRAM)\n", time.Since(start).Seconds(), float64(mem)/(1024*1024*1024))
-
-	return nil
-}
-
-// Generate creates an image from a prompt.
-func (m *Model) Generate(prompt string, width, height int32, steps int, seed int64) (*mlx.Array, error) {
-	return m.GenerateFromConfig(context.Background(), &GenerateConfig{
-		Prompt: prompt,
-		Width:  width,
-		Height: height,
-		Steps:  steps,
-		Seed:   seed,
-	})
-}
-
-// GenerateWithProgress creates an image with progress callback.
-func (m *Model) GenerateWithProgress(prompt string, width, height int32, steps int, seed int64, progress ProgressFunc) (*mlx.Array, error) {
-	return m.GenerateFromConfig(context.Background(), &GenerateConfig{
-		Prompt:   prompt,
-		Width:    width,
-		Height:   height,
-		Steps:    steps,
-		Seed:     seed,
-		Progress: progress,
-	})
-}
-
-// GenerateFromConfig generates an image using the unified config struct.
-func (m *Model) GenerateFromConfig(ctx context.Context, cfg *GenerateConfig) (*mlx.Array, error) {
-	start := time.Now()
-	result, err := m.generate(ctx, cfg)
-	if err != nil {
-		return nil, err
-	}
-	fmt.Printf("Generated in %.2fs (%d diffusion steps)\n", time.Since(start).Seconds(), cfg.Steps)
-	return result, nil
-}
-
-// GenerateImage implements model.ImageModel interface.
-func (m *Model) GenerateImage(ctx context.Context, prompt string, width, height int32, steps int, seed int64) (*mlx.Array, error) {
-	return m.Generate(prompt, width, height, steps, seed)
-}
-
-// generate is the internal generation pipeline.
-func (m *Model) generate(ctx context.Context, cfg *GenerateConfig) (*mlx.Array, error) {
-	// Apply defaults
-	if cfg.Width <= 0 {
-		cfg.Width = 1024
-	}
-	if cfg.Height <= 0 {
-		cfg.Height = 1024
-	}
-	if cfg.Steps <= 0 {
-		cfg.Steps = 50
-	}
-	if cfg.GuidanceScale <= 0 {
-		cfg.GuidanceScale = 1.5
-	}
-	// Calculate MaxVisualTokens based on image dimensions
-	// GLM-Image generates TWO grids of visual tokens:
-	//   1. First: prev (small) grid - prevTokenH × prevTokenW tokens
-	//   2. Then: target (large) grid - tokenH × tokenW tokens
-	// After generation, we extract only the TARGET grid tokens for diffusion.
-	factor := int32(32)
-	tokenH := cfg.Height / factor
-	tokenW := cfg.Width / factor
-	targetGridTokens := tokenH * tokenW
-
-	// Compute prev grid dimensions using diffusers formula:
-	// ratio = token_h / token_w
-	// prev_token_h = int(sqrt(ratio) * 16)
-	// prev_token_w = int(sqrt(1/ratio) * 16)
-	ratio := float64(tokenH) / float64(tokenW)
-	prevTokenH := int32(math.Sqrt(ratio) * 16)
-	prevTokenW := int32(math.Sqrt(1/ratio) * 16)
-	prevGridTokens := prevTokenH * prevTokenW
-
-	// Total tokens to generate = prev grid + target grid
-	// (diffusers does max_new_tokens = total + 1 for EOS, but we stop on EOS anyway)
-	cfg.MaxVisualTokens = prevGridTokens + targetGridTokens
-	if cfg.Temperature <= 0 {
-		cfg.Temperature = 0.9
-	}
-	if cfg.TopP <= 0 {
-		cfg.TopP = 0.75
-	}
-
-	// Ensure dimensions are divisible by 32
-	cfg.Width = (cfg.Width / 32) * 32
-	cfg.Height = (cfg.Height / 32) * 32
-
-	tcfg := m.Transformer.Config
-	latentH := cfg.Height / 8
-	latentW := cfg.Width / 8
-
-	// Progress callback helper
-	progress := func(stage string, step, total int) {
-		if cfg.Progress != nil {
-			cfg.Progress(stage, step, total)
-		}
-	}
-
-	// === PHASE 1: T5 Text Encoding ===
-	fmt.Println("[T5] Encoding glyph text...")
-	progress("text_encoding", 0, 1)
-	textEmbed := m.TextEncoder.EncodePrompt(m.Tokenizer, cfg.Prompt)
-	mlx.Keep(textEmbed)
-	mlx.Eval(textEmbed)
-	fmt.Printf("[T5] Done, shape: %v\n", textEmbed.Shape())
-	progress("text_encoding", 1, 1)
-
-	// === PHASE 2: AR Visual Token Generation ===
-	fmt.Printf("[AR] Generating %d visual tokens...\n", cfg.MaxVisualTokens)
-	progress("ar_generation", 0, int(cfg.MaxVisualTokens))
-	visualTokens := m.VisionLanguageEncoder.Generate(
-		cfg.Prompt,
-		m.GLMTokenizer,
-		cfg.MaxVisualTokens,
-		cfg.Temperature,
-		cfg.TopP,
-		cfg.Seed,
-		cfg.Height,
-		cfg.Width,
-		func(step int) {
-			if step%100 == 0 || step < 10 {
-				fmt.Printf("[AR] Step %d/%d\n", step, cfg.MaxVisualTokens)
-			}
-			progress("ar_generation", step, int(cfg.MaxVisualTokens))
-		},
-	)
-	mlx.Keep(visualTokens)
-	mlx.Eval(visualTokens)
-	fmt.Printf("[AR] Done generating visual tokens\n")
-	progress("ar_generation", int(cfg.MaxVisualTokens), int(cfg.MaxVisualTokens))
-
-	vtShape := visualTokens.Shape()
-	totalGenerated := vtShape[1]
-	fmt.Printf("[AR] Generated %d tokens total\n", totalGenerated)
-
-	// Extract only the TARGET grid tokens (skip the prev grid tokens)
-	// diffusers: large_image_tokens = outputs[input_length + large_image_start_offset : ...]
-	// large_image_start_offset = prev_grid_size
-	var targetGridVisualTokens *mlx.Array
-	if totalGenerated >= prevGridTokens+targetGridTokens {
-		// Full generation completed - extract target grid
-		targetGridVisualTokens = mlx.Slice(visualTokens,
-			[]int32{0, prevGridTokens},
-			[]int32{1, prevGridTokens + targetGridTokens})
-		mlx.Keep(targetGridVisualTokens)
-		mlx.Eval(targetGridVisualTokens)
-	} else if totalGenerated > prevGridTokens {
-		// Partial target grid - take what we have
-		actualTargetTokens := totalGenerated - prevGridTokens
-		targetGridVisualTokens = mlx.Slice(visualTokens,
-			[]int32{0, prevGridTokens},
-			[]int32{1, totalGenerated})
-		mlx.Keep(targetGridVisualTokens)
-		mlx.Eval(targetGridVisualTokens)
-		fmt.Printf("WARNING: Partial target grid: got %d/%d target tokens\n",
-			actualTargetTokens, targetGridTokens)
-	} else {
-		// Not enough tokens - EOS came too early
-		return nil, fmt.Errorf("AR generation stopped too early: got %d tokens, need at least %d (prev grid) + 1",
-			totalGenerated, prevGridTokens)
-	}
-
-	// === PHASE 3: Diffusion Decoding ===
-	// Setup scheduler with dynamic shift based on image size
-	scheduler := NewFlowMatchScheduler(DefaultSchedulerConfig())
-	imgSeqLen := (latentH / tcfg.PatchSize) * (latentW / tcfg.PatchSize)
-	scheduler.SetTimestepsWithDynamicShift(cfg.Steps, imgSeqLen)
-
-	// Initialize noise latents [B, C, H, W]
-	latents := scheduler.InitNoise([]int32{1, tcfg.InChannels, latentH, latentW}, cfg.Seed)
-	mlx.Eval(latents)
-
-	// Upsample TARGET grid visual tokens 2x to match patch count (matching diffusers)
-	// target_grid tokens -> 2x upsample -> patch_count
-	// e.g., 32x32=1024 tokens -> 64x64=4096 patches for 1024x1024
-	visualTokensUpsampled := upsampleTokens(targetGridVisualTokens, tokenH, tokenW, 2)
-
-	// Prepare prior embeddings from upsampled visual tokens (VQ codebook lookup + projection)
-	priorEmbed := m.Transformer.EmbedPriorTokens(visualTokensUpsampled)
-	mlx.Keep(priorEmbed)
-	mlx.Eval(priorEmbed)
-
-	// Prepare text conditioning (project T5 embeddings)
-	textCond := m.Transformer.ProjectTextEmbeddings(textEmbed)
-	mlx.Keep(textCond)
-	mlx.Eval(textCond)
-
-	// === CFG Setup ===
-	// For classifier-free guidance, we need unconditional (negative) text embeddings
-	// GLM-Image uses empty string "" for negative prompt
-	doCFG := cfg.GuidanceScale > 1.0
-	var negativeTextCond *mlx.Array
-	if doCFG {
-		// Encode empty string for negative prompt
-		negativeTextEmbed := m.TextEncoder.EncodePrompt(m.Tokenizer, "")
-		mlx.Keep(negativeTextEmbed)
-		mlx.Eval(negativeTextEmbed)
-		negativeTextCond = m.Transformer.ProjectTextEmbeddings(negativeTextEmbed)
-		mlx.Keep(negativeTextCond)
-		mlx.Eval(negativeTextCond)
-		negativeTextEmbed.Free()
-	}
-
-	// Prepare conditioning inputs
-	targetSize := mlx.NewArray([]float32{float32(cfg.Height), float32(cfg.Width)}, []int32{1, 2})
-	cropCoords := mlx.NewArray([]float32{0, 0}, []int32{1, 2}) // Default: no crop offset
-	targetSize = mlx.ToBFloat16(targetSize)
-	cropCoords = mlx.ToBFloat16(cropCoords)
-	mlx.Keep(targetSize)
-	mlx.Keep(cropCoords)
-	mlx.Eval(targetSize, cropCoords)
-
-	pH := latentH / tcfg.PatchSize
-	pW := latentW / tcfg.PatchSize
-
-	// Denoising loop
-	fmt.Printf("[Diffusion] Starting %d denoising steps...\n", cfg.Steps)
-	progress("diffusion", 0, cfg.Steps)
-	for i := 0; i < cfg.Steps; i++ {
-		fmt.Printf("[Diffusion] Step %d/%d (timestep=%.1f)\n", i+1, cfg.Steps, scheduler.Timesteps[i]-1)
-		// Check for cancellation
-		if ctx != nil {
-			select {
-			case <-ctx.Done():
-				textEmbed.Free()
-				visualTokens.Free()
-				// visualTokensUpsampled points to visualTokens, don't double-free
-				priorEmbed.Free()
-				textCond.Free()
-				latents.Free()
-				return nil, ctx.Err()
-			default:
-			}
-		}
-
-		// Get timestep value for the transformer
-		// scheduler.Timesteps contains raw timestep values (1000 down to ~20)
-		// Pass timestep - 1 to match diffusers: timestep = t.expand(latents.shape[0]) - 1
-		timestepVal := scheduler.Timesteps[i] - 1
-		timestep := mlx.ToBFloat16(mlx.NewArray([]float32{timestepVal}, []int32{1}))
-
-		// Patchify latents [B, C, H, W] -> [B, L, C*p*p]
-		patches := PatchifyLatents(latents, tcfg.PatchSize)
-
-		// Transformer forward with MMDiT architecture
-		// Conditional pass (with text + prior embeddings)
-		outputCond := m.Transformer.ForwardWithPriorDrop(
-			patches,
-			priorEmbed,
-			textCond,
-			timestep,
-			targetSize,
-			cropCoords,
-			pH,
-			pW,
-			false, // priorTokenDrop = false for conditional
-		)
-
-		// Unpatchify [B, L, C*p*p] -> [B, C, H, W]
-		noisePredCond := UnpatchifyLatents(outputCond, latentH, latentW, tcfg.PatchSize, tcfg.OutChannels)
-
-		var noisePred *mlx.Array
-		if doCFG {
-			// Unconditional pass (empty text, dropped prior embeddings)
-			outputUncond := m.Transformer.ForwardWithPriorDrop(
-				patches,
-				priorEmbed, // Still passed but will be ignored due to priorTokenDrop=true
-				negativeTextCond,
-				timestep,
-				targetSize,
-				cropCoords,
-				pH,
-				pW,
-				true, // priorTokenDrop = true for unconditional
-			)
-			noisePredUncond := UnpatchifyLatents(outputUncond, latentH, latentW, tcfg.PatchSize, tcfg.OutChannels)
-
-			// CFG formula: noise_pred = uncond + guidance_scale * (cond - uncond)
-			diff := mlx.Sub(noisePredCond, noisePredUncond)
-			scaled := mlx.MulScalar(diff, cfg.GuidanceScale)
-			noisePred = mlx.Add(noisePredUncond, scaled)
-		} else {
-			noisePred = noisePredCond
-		}
-
-		// Scheduler step
-		oldLatents := latents
-		latents = scheduler.Step(noisePred, latents, i)
-		mlx.Eval(latents)
-		oldLatents.Free()
-
-		progress("diffusion", i+1, cfg.Steps)
-	}
-
-	// Cleanup intermediate arrays
-	textEmbed.Free()
-	visualTokens.Free()
-	// visualTokensUpsampled points to visualTokens, don't double-free
-	priorEmbed.Free()
-	textCond.Free()
-	if negativeTextCond != nil {
-		negativeTextCond.Free()
-	}
-	targetSize.Free()
-	cropCoords.Free()
-
-	// === PHASE 4: VAE Decode ===
-	progress("vae_decode", 0, 1)
-	decoded := m.VAEDecoder.Decode(latents)
-	mlx.Eval(decoded)
-	latents.Free()
-	progress("vae_decode", 1, 1)
-
-	return decoded, nil
-}
-
-// upsampleTokens performs nearest-neighbor upsampling of visual tokens
-// Converts from prev_grid (e.g., 16x16) to target_grid (e.g., 32x32 for 2x, 64x64 for 4x)
-// scale must be 2 or 4
-//
-// Handles early EOS gracefully: if tokens has fewer than prevH*prevW elements,
-// missing tokens are padded with 0 (visual token padding value).
-func upsampleTokens(tokens *mlx.Array, prevH, prevW int32, scale int32) *mlx.Array {
-	// tokens: [1, N] where N <= prevH*prevW (may be shorter if early EOS)
-	// Each token at (i, j) becomes scale*scale tokens in the output
-
-	mlx.Eval(tokens)
-	tokenData := tokens.DataInt32()
-	numTokens := int32(len(tokenData))
-	expectedTokens := prevH * prevW
-
-	// Warn if we got fewer tokens than expected (early EOS)
-	if numTokens < expectedTokens {
-		fmt.Printf("WARNING: upsampleTokens got %d tokens, expected %d (padding with 0)\n",
-			numTokens, expectedTokens)
-	}
-
-	targetH := prevH * scale
-	targetW := prevW * scale
-	upsampled := make([]int32, targetH*targetW)
-
-	for i := int32(0); i < prevH; i++ {
-		for j := int32(0); j < prevW; j++ {
-			srcIdx := i*prevW + j
-
-			// Handle early EOS: use 0 (padding) for missing tokens
-			var val int32
-			if srcIdx < numTokens {
-				val = tokenData[srcIdx]
-			} else {
-				val = 0 // Padding token
-			}
-
-			// Place in scale*scale positions
-			dstI := i * scale
-			dstJ := j * scale
-			for di := int32(0); di < scale; di++ {
-				for dj := int32(0); dj < scale; dj++ {
-					upsampled[(dstI+di)*targetW+(dstJ+dj)] = val
-				}
-			}
-		}
-	}
-
-	return mlx.NewArrayInt32(upsampled, []int32{1, targetH * targetW})
-}
-
-// PatchifyLatents converts [B, C, H, W] to [B, L, C*p*p]
-func PatchifyLatents(latents *mlx.Array, patchSize int32) *mlx.Array {
-	shape := latents.Shape()
-	B := shape[0]
-	C := shape[1]
-	H := shape[2]
-	W := shape[3]
-
-	pH := H / patchSize
-	pW := W / patchSize
-
-	// Reshape: [B, C, H, W] -> [B, C, pH, p, pW, p]
-	x := mlx.Reshape(latents, B, C, pH, patchSize, pW, patchSize)
-	// Transpose: -> [B, pH, pW, C, p, p]
-	x = mlx.Transpose(x, 0, 2, 4, 1, 3, 5)
-	// Flatten: -> [B, pH*pW, C*p*p]
-	return mlx.Reshape(x, B, pH*pW, C*patchSize*patchSize)
-}
-
-// UnpatchifyLatents converts [B, L, C*p*p] back to [B, C, H, W]
-func UnpatchifyLatents(patches *mlx.Array, H, W, patchSize, channels int32) *mlx.Array {
-	shape := patches.Shape()
-	B := shape[0]
-
-	pH := H / patchSize
-	pW := W / patchSize
-
-	// Reshape: [B, L, C*p*p] -> [B, pH, pW, C, p, p]
-	x := mlx.Reshape(patches, B, pH, pW, channels, patchSize, patchSize)
-	// Transpose: -> [B, C, pH, p, pW, p]
-	x = mlx.Transpose(x, 0, 3, 1, 4, 2, 5)
-	// Reshape: -> [B, C, H, W]
-	return mlx.Reshape(x, B, channels, pH*patchSize, pW*patchSize)
-}
-
-// CalculateShift computes the dynamic shift for flow matching based on image sequence length.
-func CalculateShift(imgSeqLen int32) float32 {
-	cfg := DefaultSchedulerConfig()
-	if !cfg.UseDynamicShifting {
-		return 0
-	}
-
-	// Sqrt-based shift calculation (matches diffusers)
-	m := float32(math.Sqrt(float64(imgSeqLen) / float64(cfg.BaseImageSeqLen)))
-	return m*cfg.MaxShift + cfg.BaseShift
-}
-
-// UpsampleTokens2x upsamples token IDs by 2x using nearest neighbor interpolation
-// tokens: [B, H*W] -> [B, (H*2)*(W*2)]
-// This matches diffusers' _upsample_token_ids function
-func UpsampleTokens2x(tokens *mlx.Array, gridH, gridW int32) *mlx.Array {
-	shape := tokens.Shape()
-	B := shape[0]
-
-	// Reshape to [B, 1, H, W] for interpolation
-	tokens = mlx.Reshape(tokens, B, 1, gridH, gridW)
-
-	// Convert to float for interpolation
-	tokensFloat := mlx.AsType(tokens, mlx.DtypeFloat32)
-
-	// 2x nearest neighbor upsample
-	// [B, 1, H, W] -> [B, 1, H*2, W*2]
-	upsampled := nearestUpsample2x(tokensFloat)
-
-	// Convert back to int and reshape to [B, H*2*W*2]
-	upsampled = mlx.AsType(upsampled, mlx.DtypeInt32)
-	return mlx.Reshape(upsampled, B, gridH*2*gridW*2)
-}
-
-// nearestUpsample2x performs 2x nearest neighbor upsampling on NCHW tensor
-func nearestUpsample2x(x *mlx.Array) *mlx.Array {
-	shape := x.Shape()
-	B := shape[0]
-	C := shape[1]
-	H := shape[2]
-	W := shape[3]
-
-	// Repeat each element 2x2
-	// [B, C, H, W] -> [B, C, H, 1, W, 1] -> [B, C, H, 2, W, 2] -> [B, C, H*2, W*2]
-	x = mlx.Reshape(x, B, C, H, 1, W, 1)
-
-	// Tile to repeat each pixel 2x2
-	x = mlx.Tile(x, []int32{1, 1, 1, 2, 1, 2})
-
-	// Reshape to final size
-	return mlx.Reshape(x, B, C, H*2, W*2)
-}

x/imagegen/models/glm_image/glm_tokenizer.go

@@ -1,358 +0,0 @@
-//go:build mlx
-
-package glm_image
-
-import (
-	"encoding/json"
-	"fmt"
-	"os"
-	"path/filepath"
-	"sort"
-	"strings"
-
-	"github.com/ollama/ollama/x/imagegen"
-)
-
-// GLMTokenizer implements the GLM tokenizer for the AR model
-// This is a BPE-style tokenizer with ignore_merges=true, meaning it does
-// greedy longest-match tokenization from the vocab without runtime merging.
-type GLMTokenizer struct {
-	Vocab        map[string]int32 // token string -> token ID
-	VocabReverse map[int32]string // token ID -> token string
-	SpecialTokens map[string]int32 // special token strings -> IDs
-
-	// Special token IDs
-	SopTokenID  int32 // <sop> = grid_bos_token (167845)
-	EopTokenID  int32 // <eop> = grid_eos_token (167846)
-	BosTokenID  int32 // <|dit_token_16384|> = visual BOS (16384)
-	EosTokenID  int32 // <|dit_token_16385|> = visual EOS (16385)
-	PadTokenID  int32
-
-	// Sorted vocab keys by length (longest first) for greedy matching
-	sortedTokens []string
-}
-
-// tokenizerJSON represents the structure of tokenizer.json
-type tokenizerJSON struct {
-	Model struct {
-		Vocab map[string]int32 `json:"vocab"`
-	} `json:"model"`
-	AddedTokens []struct {
-		ID      int32  `json:"id"`
-		Content string `json:"content"`
-		Special bool   `json:"special"`
-	} `json:"added_tokens"`
-}
-
-// NewGLMTokenizer creates a GLM tokenizer from the model manifest
-func NewGLMTokenizer(manifest *imagegen.ModelManifest) (*GLMTokenizer, error) {
-	// Read tokenizer.json from processor directory in manifest
-	data, err := manifest.ReadConfig("processor/tokenizer.json")
-	if err != nil {
-		return nil, fmt.Errorf("failed to read tokenizer.json from manifest: %w", err)
-	}
-
-	var tj tokenizerJSON
-	if err := json.Unmarshal(data, &tj); err != nil {
-		return nil, fmt.Errorf("failed to parse tokenizer.json: %w", err)
-	}
-
-	tok := &GLMTokenizer{
-		Vocab:         make(map[string]int32),
-		VocabReverse:  make(map[int32]string),
-		SpecialTokens: make(map[string]int32),
-	}
-
-	// Load vocab from model section
-	for token, id := range tj.Model.Vocab {
-		tok.Vocab[token] = id
-		tok.VocabReverse[id] = token
-	}
-
-	// Load added tokens (special tokens including dit_tokens)
-	for _, at := range tj.AddedTokens {
-		tok.Vocab[at.Content] = at.ID
-		tok.VocabReverse[at.ID] = at.Content
-		if at.Special {
-			tok.SpecialTokens[at.Content] = at.ID
-		}
-	}
-
-	// Set special token IDs
-	tok.SopTokenID = 167845   // <sop>
-	tok.EopTokenID = 167846   // <eop>
-	tok.BosTokenID = 16384    // <|dit_token_16384|>
-	tok.EosTokenID = 16385    // <|dit_token_16385|>
-	tok.PadTokenID = 16385    // Same as EOS
-
-	// Build sorted token list for greedy matching (longest first)
-	tok.sortedTokens = make([]string, 0, len(tok.Vocab))
-	for token := range tok.Vocab {
-		tok.sortedTokens = append(tok.sortedTokens, token)
-	}
-	sort.Slice(tok.sortedTokens, func(i, j int) bool {
-		return len(tok.sortedTokens[i]) > len(tok.sortedTokens[j])
-	})
-
-	fmt.Printf("Loaded GLM tokenizer with %d tokens\n", len(tok.Vocab))
-
-	return tok, nil
-}
-
-// NewGLMTokenizerFromPath creates a GLM tokenizer from a directory path
-func NewGLMTokenizerFromPath(modelPath string) (*GLMTokenizer, error) {
-	// Read tokenizer.json from processor directory
-	tokenizerPath := filepath.Join(modelPath, "processor", "tokenizer.json")
-	data, err := os.ReadFile(tokenizerPath)
-	if err != nil {
-		return nil, fmt.Errorf("failed to read tokenizer.json: %w", err)
-	}
-
-	var tj tokenizerJSON
-	if err := json.Unmarshal(data, &tj); err != nil {
-		return nil, fmt.Errorf("failed to parse tokenizer.json: %w", err)
-	}
-
-	tok := &GLMTokenizer{
-		Vocab:         make(map[string]int32),
-		VocabReverse:  make(map[int32]string),
-		SpecialTokens: make(map[string]int32),
-	}
-
-	// Load vocab from model section
-	for token, id := range tj.Model.Vocab {
-		tok.Vocab[token] = id
-		tok.VocabReverse[id] = token
-	}
-
-	// Load added tokens (special tokens including dit_tokens)
-	for _, at := range tj.AddedTokens {
-		tok.Vocab[at.Content] = at.ID
-		tok.VocabReverse[at.ID] = at.Content
-		if at.Special {
-			tok.SpecialTokens[at.Content] = at.ID
-		}
-	}
-
-	// Set special token IDs
-	tok.SopTokenID = 167845 // <sop>
-	tok.EopTokenID = 167846 // <eop>
-	tok.BosTokenID = 16384  // <|dit_token_16384|>
-	tok.EosTokenID = 16385  // <|dit_token_16385|>
-	tok.PadTokenID = 16385  // Same as EOS
-
-	// Build sorted token list for greedy matching (longest first)
-	tok.sortedTokens = make([]string, 0, len(tok.Vocab))
-	for token := range tok.Vocab {
-		tok.sortedTokens = append(tok.sortedTokens, token)
-	}
-	sort.Slice(tok.sortedTokens, func(i, j int) bool {
-		return len(tok.sortedTokens[i]) > len(tok.sortedTokens[j])
-	})
-
-	fmt.Printf("Loaded GLM tokenizer with %d tokens\n", len(tok.Vocab))
-
-	return tok, nil
-}
-
-// Encode tokenizes a string into token IDs
-// This uses greedy longest-match tokenization with GPT-2 style space handling
-func (t *GLMTokenizer) Encode(text string) []int32 {
-	if text == "" {
-		return []int32{}
-	}
-
-	var tokens []int32
-
-	// First, check for and handle special tokens
-	// Replace special tokens with placeholders, encode, then restore
-	specialReplacements := make(map[string]int32)
-	for special, id := range t.SpecialTokens {
-		if strings.Contains(text, special) {
-			specialReplacements[special] = id
-		}
-	}
-
-	// Process text character by character with special token handling
-	i := 0
-	isFirstToken := true
-
-	for i < len(text) {
-		// Check for special tokens first
-		foundSpecial := false
-		for special, id := range specialReplacements {
-			if strings.HasPrefix(text[i:], special) {
-				tokens = append(tokens, id)
-				i += len(special)
-				isFirstToken = false
-				foundSpecial = true
-				break
-			}
-		}
-		if foundSpecial {
-			continue
-		}
-
-		// Handle regular text with GPT-2 style space prefix
-		// "Ġ" (U+0120) represents a space before a token
-		remaining := text[i:]
-
-		// Try to find the longest matching token
-		matched := false
-		for _, token := range t.sortedTokens {
-			// Skip special tokens in regular matching
-			if _, isSpecial := t.SpecialTokens[token]; isSpecial {
-				continue
-			}
-
-			// Check if this token matches
-			tokenText := token
-
-			// Handle the Ġ prefix (represents space)
-			if strings.HasPrefix(token, "Ġ") {
-				// This token expects a leading space
-				if i > 0 || !isFirstToken {
-					// Check if remaining starts with space + token content
-					tokenContent := token[len("Ġ"):]
-					if strings.HasPrefix(remaining, " "+tokenContent) {
-						tokens = append(tokens, t.Vocab[token])
-						i += 1 + len(tokenContent) // space + content
-						isFirstToken = false
-						matched = true
-						break
-					}
-				}
-			} else {
-				// Regular token without space prefix
-				if strings.HasPrefix(remaining, tokenText) {
-					tokens = append(tokens, t.Vocab[token])
-					i += len(tokenText)
-					isFirstToken = false
-					matched = true
-					break
-				}
-			}
-		}
-
-		if !matched {
-			// No token found - skip this character (or use UNK)
-			// For now, just skip unknown characters
-			i++
-		}
-	}
-
-	return tokens
-}
-
-// EncodeForGeneration encodes a prompt with grid tokens for image generation
-// Format: {prompt}<sop>{token_h} {token_w}<eop><sop>{prev_h} {prev_w}<eop><|dit_token_16384|>
-//
-// Uses GPT-2 style tokenization where " 32" becomes "Ġ32" (a single token with
-// space prefix), matching the HuggingFace tokenizer behavior.
-func (t *GLMTokenizer) EncodeForGeneration(prompt string, targetHeight, targetWidth int32) []int32 {
-	// Calculate grid dimensions
-	factor := int32(32)
-	height := (targetHeight / factor) * factor
-	width := (targetWidth / factor) * factor
-	tokenH := height / factor
-	tokenW := width / factor
-
-	// Calculate previous grid dimensions
-	ratio := float64(tokenH) / float64(tokenW)
-	prevTokenH := int32(sqrt(ratio) * 16)
-	prevTokenW := int32(sqrt(1.0/ratio) * 16)
-
-	// Encode the prompt text
-	promptTokens := t.Encode(prompt)
-
-	// Build the full sequence:
-	// [prompt tokens] <sop> [tokenH] [Ġ+tokenW] <eop> <sop> [prevH] [Ġ+prevW] <eop> <bos>
-	// Note: HF tokenizer treats " 32" as "Ġ32" (single token), not "Ġ" + "32"
-	var tokens []int32
-	tokens = append(tokens, promptTokens...)
-
-	// First grid: <sop> H W <eop>
-	// First number has no space prefix, second number has space prefix (Ġ)
-	tokens = append(tokens, t.SopTokenID)
-	tokens = append(tokens, t.encodeNumber(tokenH)...)
-	tokens = append(tokens, t.encodeSpaceNumber(tokenW)...) // " W" as Ġ+W
-	tokens = append(tokens, t.EopTokenID)
-
-	// Second grid: <sop> prevH prevW <eop>
-	tokens = append(tokens, t.SopTokenID)
-	tokens = append(tokens, t.encodeNumber(prevTokenH)...)
-	tokens = append(tokens, t.encodeSpaceNumber(prevTokenW)...) // " prevW" as Ġ+prevW
-	tokens = append(tokens, t.EopTokenID)
-
-	// BOS token (start of image generation)
-	tokens = append(tokens, t.BosTokenID)
-
-	return tokens
-}
-
-// encodeNumber encodes a number - first tries as a whole token, falls back to digit-by-digit
-func (t *GLMTokenizer) encodeNumber(n int32) []int32 {
-	s := fmt.Sprintf("%d", n)
-	// First try: look up the whole number as a single token
-	if id, ok := t.Vocab[s]; ok {
-		return []int32{id}
-	}
-	// Fallback: encode digit by digit
-	var tokens []int32
-	for _, c := range s {
-		if id, ok := t.Vocab[string(c)]; ok {
-			tokens = append(tokens, id)
-		}
-	}
-	return tokens
-}
-
-// encodeSpaceNumber encodes " N" as "ĠN" (space-prefixed number) matching HF tokenizer
-// GPT-2 style: " 32" becomes single token "Ġ32", not "Ġ" + "32"
-func (t *GLMTokenizer) encodeSpaceNumber(n int32) []int32 {
-	s := fmt.Sprintf("%d", n)
-
-	// First try: look up "Ġ{number}" as a single token (e.g., "Ġ32")
-	spaceToken := "Ġ" + s
-	if id, ok := t.Vocab[spaceToken]; ok {
-		return []int32{id}
-	}
-
-	// Fallback: bare space Ġ + number tokens
-	var tokens []int32
-	if spaceID, ok := t.Vocab["Ġ"]; ok {
-		tokens = append(tokens, spaceID)
-	}
-	tokens = append(tokens, t.encodeNumber(n)...)
-	return tokens
-}
-
-// sqrt is a helper for float64 sqrt
-func sqrt(x float64) float64 {
-	if x <= 0 {
-		return 0
-	}
-	// Newton's method
-	z := x
-	for i := 0; i < 10; i++ {
-		z = z - (z*z-x)/(2*z)
-	}
-	return z
-}
-
-// Decode converts token IDs back to a string
-func (t *GLMTokenizer) Decode(tokens []int32) string {
-	var sb strings.Builder
-	for _, id := range tokens {
-		if token, ok := t.VocabReverse[id]; ok {
-			// Handle Ġ prefix (convert back to space)
-			if strings.HasPrefix(token, "Ġ") {
-				sb.WriteString(" ")
-				sb.WriteString(token[len("Ġ"):])
-			} else {
-				sb.WriteString(token)
-			}
-		}
-	}
-	return sb.String()
-}

x/imagegen/models/glm_image/scheduler.go

@@ -1,159 +0,0 @@
-//go:build mlx
-
-package glm_image
-
-import (
-	"math"
-
-	"github.com/ollama/ollama/x/imagegen/mlx"
-)
-
-// FlowMatchSchedulerConfig holds scheduler configuration
-type FlowMatchSchedulerConfig struct {
-	NumTrainTimesteps  int32   `json:"num_train_timesteps"`   // 1000
-	BaseShift          float32 `json:"base_shift"`            // 0.25
-	MaxShift           float32 `json:"max_shift"`             // 0.75
-	BaseImageSeqLen    int32   `json:"base_image_seq_len"`    // 256
-	MaxImageSeqLen     int32   `json:"max_image_seq_len"`     // 4096
-	UseDynamicShifting bool    `json:"use_dynamic_shifting"`  // true
-	TimeShiftType      string  `json:"time_shift_type"`       // "linear"
-}
-
-// DefaultSchedulerConfig returns the default config for GLM-Image
-func DefaultSchedulerConfig() *FlowMatchSchedulerConfig {
-	return &FlowMatchSchedulerConfig{
-		NumTrainTimesteps:  1000,
-		BaseShift:          0.25,
-		MaxShift:           0.75,
-		BaseImageSeqLen:    256,
-		MaxImageSeqLen:     4096,
-		UseDynamicShifting: true,
-		TimeShiftType:      "linear",
-	}
-}
-
-// FlowMatchScheduler implements FlowMatchEulerDiscreteScheduler
-type FlowMatchScheduler struct {
-	Config    *FlowMatchSchedulerConfig
-	Timesteps []float32 // Raw timesteps for transformer conditioning (unshifted)
-	Sigmas    []float32 // Shifted sigmas for Euler step calculation
-	NumSteps  int
-}
-
-// NewFlowMatchScheduler creates a new scheduler
-func NewFlowMatchScheduler(cfg *FlowMatchSchedulerConfig) *FlowMatchScheduler {
-	return &FlowMatchScheduler{Config: cfg}
-}
-
-// SetTimestepsWithDynamicShift sets timesteps with dynamic shifting based on image size
-// Following diffusers: raw timesteps are used for conditioning, shifted sigmas for step calculation
-func (s *FlowMatchScheduler) SetTimestepsWithDynamicShift(numSteps int, imgSeqLen int32) {
-	s.NumSteps = numSteps
-
-	// Calculate shift (mu) based on image sequence length
-	mu := s.calculateShift(imgSeqLen)
-
-	// Create timesteps: linspace from sigma_max_t to sigma_min_t
-	// sigma_max = 1.0, sigma_min ~= 0.001 (near 0 but not exactly 0)
-	// Then apply time shift and append terminal sigma=0
-	s.Timesteps = make([]float32, numSteps)
-	s.Sigmas = make([]float32, numSteps+1) // +1 for terminal sigma
-
-	numTrainTimesteps := float32(s.Config.NumTrainTimesteps)
-
-	// Create base sigmas: linspace from 1.0 to small value (matching diffusers)
-	for i := 0; i < numSteps; i++ {
-		// linspace from 1000 to ~20 (sigma_min * num_train_timesteps)
-		tRaw := numTrainTimesteps - float32(i)*(numTrainTimesteps-1.0)/float32(numSteps-1)
-		s.Timesteps[i] = tRaw
-
-		// Convert to sigma [0, 1]
-		sigma := tRaw / numTrainTimesteps
-
-		// Apply time shift if enabled
-		if s.Config.UseDynamicShifting && mu > 0 {
-			sigma = s.applyShift(mu, sigma)
-		}
-
-		s.Sigmas[i] = sigma
-	}
-
-	// Append terminal sigma = 0 (the final clean image)
-	s.Sigmas[numSteps] = 0
-}
-
-// calculateShift computes dynamic shift based on image sequence length
-// Uses the sqrt-based formula from diffusers:
-// m = (image_seq_len / base_seq_len) ** 0.5
-// mu = m * max_shift + base_shift
-func (s *FlowMatchScheduler) calculateShift(imgSeqLen int32) float32 {
-	cfg := s.Config
-
-	if !cfg.UseDynamicShifting {
-		return 0
-	}
-
-	// Sqrt-based shift calculation (matches diffusers pipeline_glm_image.py)
-	m := float32(math.Sqrt(float64(imgSeqLen) / float64(cfg.BaseImageSeqLen)))
-	mu := m*cfg.MaxShift + cfg.BaseShift
-	return mu
-}
-
-// applyShift applies time shift transformation
-// mu: the computed shift value
-// t: sigma value in [0, 1]
-func (s *FlowMatchScheduler) applyShift(mu float32, t float32) float32 {
-	if t <= 0 {
-		return 0
-	}
-	if t >= 1 {
-		return 1
-	}
-
-	// sigma=1.0 for both shift types
-	sigma := float32(1.0)
-
-	if s.Config.TimeShiftType == "linear" {
-		// Linear: mu / (mu + (1/t - 1)^sigma)
-		return mu / (mu + float32(math.Pow(float64(1.0/t-1.0), float64(sigma))))
-	}
-
-	// Exponential (default): exp(mu) / (exp(mu) + (1/t - 1)^sigma)
-	expMu := float32(math.Exp(float64(mu)))
-	return expMu / (expMu + float32(math.Pow(float64(1.0/t-1.0), float64(sigma))))
-}
-
-// Step performs one denoising step
-func (s *FlowMatchScheduler) Step(modelOutput, sample *mlx.Array, stepIdx int) *mlx.Array {
-	sigma := s.Sigmas[stepIdx]
-	sigmaNext := s.Sigmas[stepIdx+1]
-
-	// Euler step: x_{t-dt} = x_t + dt * v_t
-	dt := sigmaNext - sigma // Negative (going from noise to clean)
-
-	scaledOutput := mlx.MulScalar(modelOutput, dt)
-	return mlx.Add(sample, scaledOutput)
-}
-
-// InitNoise creates initial noise
-func (s *FlowMatchScheduler) InitNoise(shape []int32, seed int64) *mlx.Array {
-	return mlx.RandomNormalWithDtype(shape, uint64(seed), mlx.DtypeBFloat16)
-}
-
-// AddNoise adds noise to clean samples for a given timestep (for img2img)
-func (s *FlowMatchScheduler) AddNoise(cleanSample, noise *mlx.Array, timestepIdx int) *mlx.Array {
-	// In flow matching: x_t = (1-sigma) * x_0 + sigma * noise
-	// Use sigmas (shifted) for the interpolation
-	sigma := s.Sigmas[timestepIdx]
-	oneMinusSigma := 1.0 - sigma
-
-	scaledClean := mlx.MulScalar(cleanSample, oneMinusSigma)
-	scaledNoise := mlx.MulScalar(noise, sigma)
-
-	return mlx.Add(scaledClean, scaledNoise)
-}
-
-// GetTimesteps returns all timesteps
-func (s *FlowMatchScheduler) GetTimesteps() []float32 {
-	return s.Timesteps
-}

x/imagegen/models/glm_image/text_encoder.go

@@ -1,497 +0,0 @@
-//go:build mlx
-
-package glm_image
-
-import (
-	"encoding/json"
-	"fmt"
-	"math"
-	"os"
-	"path/filepath"
-	"regexp"
-
-	"github.com/ollama/ollama/x/imagegen"
-	"github.com/ollama/ollama/x/imagegen/mlx"
-	"github.com/ollama/ollama/x/imagegen/nn"
-	"github.com/ollama/ollama/x/imagegen/safetensors"
-)
-
-// T5Config holds T5 encoder configuration
-type T5Config struct {
-	DModel      int32   `json:"d_model"`               // 1472
-	DFF         int32   `json:"d_ff"`                  // 3584
-	DKV         int32   `json:"d_kv"`                  // 64
-	NumHeads    int32   `json:"num_heads"`             // 6
-	NumLayers   int32   `json:"num_layers"`            // 12
-	VocabSize   int32   `json:"vocab_size"`            // 384 (byte-level)
-	LayerNormEps float32 `json:"layer_norm_epsilon"`   // 1e-6
-	IsGatedAct  bool    `json:"is_gated_act"`          // true (gated-gelu)
-
-	// Relative position bias
-	RelativeAttentionNumBuckets  int32 `json:"relative_attention_num_buckets"`  // 32
-	RelativeAttentionMaxDistance int32 `json:"relative_attention_max_distance"` // 128
-}
-
-// T5TextEncoder is the T5 encoder for text conditioning
-type T5TextEncoder struct {
-	Config *T5Config
-
-	// Embedding (shared for ByT5)
-	SharedEmbed *nn.Embedding `weight:"shared"`
-
-	// Encoder layers
-	Layers []*T5Block `weight:"encoder.block"`
-
-	// Final layer norm
-	FinalNorm *T5LayerNorm `weight:"encoder.final_layer_norm"`
-
-	// Relative position bias (from first layer, shared across all)
-	RelativeAttentionBias *mlx.Array `weight:"encoder.block.0.layer.0.SelfAttention.relative_attention_bias.weight"`
-}
-
-// T5Block is a single T5 encoder block
-type T5Block struct {
-	// Self attention
-	Layer0 *T5LayerSelfAttention `weight:"layer.0"`
-	// FFN
-	Layer1 *T5LayerFF `weight:"layer.1"`
-}
-
-// T5LayerSelfAttention is T5's self-attention layer
-type T5LayerSelfAttention struct {
-	SelfAttention *T5Attention `weight:"SelfAttention"`
-	LayerNorm     *T5LayerNorm `weight:"layer_norm"`
-}
-
-// T5Attention implements T5's relative attention
-type T5Attention struct {
-	Q *mlx.Array `weight:"q.weight"` // No bias in T5
-	K *mlx.Array `weight:"k.weight"`
-	V *mlx.Array `weight:"v.weight"`
-	O *mlx.Array `weight:"o.weight"`
-
-	NHeads int32
-	DKV    int32
-	Scale  float32
-}
-
-// T5LayerFF is T5's feedforward layer with gated-gelu
-type T5LayerFF struct {
-	DenseReluDense *T5DenseGatedGelu `weight:"DenseReluDense"`
-	LayerNorm      *T5LayerNorm      `weight:"layer_norm"`
-}
-
-// T5DenseGatedGelu is T5's gated-gelu FFN
-type T5DenseGatedGelu struct {
-	Wi0 *mlx.Array `weight:"wi_0.weight"` // gate projection
-	Wi1 *mlx.Array `weight:"wi_1.weight"` // up projection
-	Wo  *mlx.Array `weight:"wo.weight"`   // down projection
-}
-
-// T5LayerNorm is T5's RMSNorm variant (no bias, no mean subtraction)
-type T5LayerNorm struct {
-	Weight *mlx.Array `weight:"weight"`
-	Eps    float32
-}
-
-// Load loads the T5 text encoder from manifest
-func (m *T5TextEncoder) Load(manifest *imagegen.ModelManifest) error {
-	fmt.Print("  Loading T5 text encoder... ")
-
-	// Load config
-	var cfg T5Config
-	if err := manifest.ReadConfigJSON("text_encoder/config.json", &cfg); err != nil {
-		return fmt.Errorf("config: %w", err)
-	}
-	m.Config = &cfg
-
-	// Pre-allocate layers
-	m.Layers = make([]*T5Block, cfg.NumLayers)
-
-	// Load weights
-	weights, err := imagegen.LoadWeightsFromManifest(manifest, "text_encoder")
-	if err != nil {
-		return fmt.Errorf("weights: %w", err)
-	}
-	if err := weights.Load(0); err != nil {
-		return fmt.Errorf("load weights: %w", err)
-	}
-	defer weights.ReleaseAll()
-
-	if err := safetensors.LoadModule(m, weights, ""); err != nil {
-		return fmt.Errorf("load module: %w", err)
-	}
-
-	m.initComputedFields()
-	fmt.Println("✓")
-	return nil
-}
-
-// LoadFromPath loads the T5 text encoder from a directory path
-func (m *T5TextEncoder) LoadFromPath(path string) error {
-	fmt.Print("  Loading T5 text encoder... ")
-
-	// Load config
-	var cfg T5Config
-	configPath := filepath.Join(path, "config.json")
-	data, err := os.ReadFile(configPath)
-	if err != nil {
-		return fmt.Errorf("read config: %w", err)
-	}
-	if err := json.Unmarshal(data, &cfg); err != nil {
-		return fmt.Errorf("parse config: %w", err)
-	}
-	m.Config = &cfg
-
-	// Pre-allocate layers
-	m.Layers = make([]*T5Block, cfg.NumLayers)
-
-	// Load weights from safetensors files
-	weights, err := safetensors.LoadModelWeights(path)
-	if err != nil {
-		return fmt.Errorf("weights: %w", err)
-	}
-	if err := weights.Load(0); err != nil {
-		return fmt.Errorf("load weights: %w", err)
-	}
-	defer weights.ReleaseAll()
-
-	if err := safetensors.LoadModule(m, weights, ""); err != nil {
-		return fmt.Errorf("load module: %w", err)
-	}
-
-	m.initComputedFields()
-	fmt.Println("✓")
-	return nil
-}
-
-func (m *T5TextEncoder) initComputedFields() {
-	cfg := m.Config
-	m.FinalNorm.Eps = cfg.LayerNormEps
-	for _, block := range m.Layers {
-		attn := block.Layer0.SelfAttention
-		attn.NHeads = cfg.NumHeads
-		attn.DKV = cfg.DKV
-		attn.Scale = float32(1.0 / math.Sqrt(float64(cfg.DKV)))
-
-		block.Layer0.LayerNorm.Eps = cfg.LayerNormEps
-		block.Layer1.LayerNorm.Eps = cfg.LayerNormEps
-	}
-}
-
-// Forward encodes text tokens
-func (m *T5TextEncoder) Forward(tokens *mlx.Array) *mlx.Array {
-	cfg := m.Config
-
-	// Get embeddings
-	h := m.SharedEmbed.Forward(tokens)
-
-	// Compute relative position bias once
-	seqLen := tokens.Shape()[1]
-	posBias := m.computeRelativePositionBias(seqLen)
-
-	// Forward through layers
-	for _, block := range m.Layers {
-		h = block.Forward(h, posBias, cfg.LayerNormEps)
-	}
-
-	// Final norm
-	h = m.FinalNorm.Forward(h)
-
-	return h
-}
-
-// extractGlyphTexts extracts quoted text (glyphs) from the prompt
-// This matches diffusers' get_glyph_texts from pipeline_glm_image.py
-// Glyph texts are used for text rendering guidance in the generated image
-func extractGlyphTexts(prompt string) []string {
-	var glyphTexts []string
-
-	// Extract text in single quotes: 'text'
-	re1 := regexp.MustCompile(`'([^']*)'`)
-	for _, match := range re1.FindAllStringSubmatch(prompt, -1) {
-		if len(match) > 1 {
-			glyphTexts = append(glyphTexts, match[1])
-		}
-	}
-
-	// Extract text in Unicode curly double quotes: "text"
-	re2 := regexp.MustCompile(`"([^""]*)"`)
-	for _, match := range re2.FindAllStringSubmatch(prompt, -1) {
-		if len(match) > 1 {
-			glyphTexts = append(glyphTexts, match[1])
-		}
-	}
-
-	// Extract text in ASCII double quotes: "text"
-	re3 := regexp.MustCompile(`"([^"]*)"`)
-	for _, match := range re3.FindAllStringSubmatch(prompt, -1) {
-		if len(match) > 1 {
-			glyphTexts = append(glyphTexts, match[1])
-		}
-	}
-
-	// Extract text in Japanese quotes: 「text」
-	re4 := regexp.MustCompile(`「([^「」]*)」`)
-	for _, match := range re4.FindAllStringSubmatch(prompt, -1) {
-		if len(match) > 1 {
-			glyphTexts = append(glyphTexts, match[1])
-		}
-	}
-
-	return glyphTexts
-}
-
-// EncodePrompt encodes the prompt text using the ByT5 tokenizer and encoder
-// This provides text conditioning for the diffusion transformer via the glyph projector
-//
-// IMPORTANT: This encodes only the GLYPH TEXTS (quoted strings in the prompt), not the
-// full prompt. Glyph texts are used for text rendering guidance in the generated image.
-// Multiple glyph texts are encoded and concatenated to form the conditioning signal.
-// This matches diffusers' _get_glyph_embeds() behavior.
-func (m *T5TextEncoder) EncodePrompt(tok *ByT5Tokenizer, prompt string) *mlx.Array {
-	// Extract glyph texts from prompt (text in quotes)
-	glyphTexts := extractGlyphTexts(prompt)
-
-	// If no glyph texts found, encode empty string (matches diffusers: [""] fallback)
-	if len(glyphTexts) == 0 {
-		glyphTexts = []string{""}
-	}
-
-	// Encode each glyph text and collect token sequences
-	// Matching diffusers' _get_glyph_embeds() which batches all glyph texts
-	var allTokenSeqs [][]int32
-
-	for _, glyphText := range glyphTexts {
-		// ByT5 uses byte-level encoding: each byte (0-255) -> token (3-258)
-		tokens := tok.Encode(glyphText)
-
-		// Add EOS token (1) at the end to match HuggingFace tokenizer behavior
-		tokens = append(tokens, tok.EOSTokenID)
-
-		allTokenSeqs = append(allTokenSeqs, tokens)
-	}
-
-	// Process each glyph text through the encoder
-	var allEmbeddings []*mlx.Array
-	for _, tokens := range allTokenSeqs {
-		tokenLen := len(tokens)
-		if tokenLen == 0 {
-			continue
-		}
-
-		// Create token array [1, L]
-		tokensArr := mlx.NewArrayInt32(tokens, []int32{1, int32(tokenLen)})
-
-		// Forward through encoder
-		output := m.Forward(tokensArr)
-		mlx.Eval(output)
-
-		allEmbeddings = append(allEmbeddings, output)
-	}
-
-	// Concatenate all glyph embeddings along sequence dimension
-	var output *mlx.Array
-	if len(allEmbeddings) == 0 {
-		// Fallback: return single zero embedding
-		output = mlx.Zeros([]int32{1, 1, m.Config.DModel}, mlx.DtypeBFloat16)
-	} else if len(allEmbeddings) == 1 {
-		output = allEmbeddings[0]
-	} else {
-		output = mlx.Concatenate(allEmbeddings, 1)
-	}
-	mlx.Eval(output)
-
-	return output
-}
-
-// computeRelativePositionBias computes T5's relative position encoding
-func (m *T5TextEncoder) computeRelativePositionBias(seqLen int32) *mlx.Array {
-	cfg := m.Config
-
-	// Create relative position matrix
-	// For each (query_pos, key_pos) pair, compute bucketed relative position
-	numBuckets := cfg.RelativeAttentionNumBuckets
-	maxDistance := cfg.RelativeAttentionMaxDistance
-
-	// Create position indices
-	contextPos := make([]int32, seqLen*seqLen)
-	memoryPos := make([]int32, seqLen*seqLen)
-	for i := int32(0); i < seqLen; i++ {
-		for j := int32(0); j < seqLen; j++ {
-			contextPos[i*seqLen+j] = i
-			memoryPos[i*seqLen+j] = j
-		}
-	}
-
-	// Compute relative positions and bucket them
-	buckets := make([]int32, seqLen*seqLen)
-	for i := int32(0); i < seqLen*seqLen; i++ {
-		relPos := memoryPos[i] - contextPos[i]
-		buckets[i] = relativePosistionBucket(relPos, numBuckets, maxDistance, false)
-	}
-
-	// Create bucket indices array
-	bucketsArr := mlx.NewArrayInt32(buckets, []int32{seqLen, seqLen})
-
-	// Look up bias: RelativeAttentionBias shape is [numBuckets, numHeads] = [32, 6]
-	// Take along axis 0 (buckets dimension) -> [seqLen, seqLen, numHeads]
-	bias := mlx.Take(m.RelativeAttentionBias, bucketsArr, 0) // [seqLen, seqLen, numHeads]
-
-	// Transpose to [numHeads, seqLen, seqLen]
-	bias = mlx.Transpose(bias, 2, 0, 1) // [numHeads, seqLen, seqLen]
-	bias = mlx.ExpandDims(bias, 0)      // [1, numHeads, seqLen, seqLen]
-
-	return bias
-}
-
-// relativePosistionBucket computes the bucket for a relative position
-func relativePosistionBucket(relativePosition int32, numBuckets int32, maxDistance int32, bidirectional bool) int32 {
-	var bucket int32 = 0
-	var n int32 = -relativePosition
-
-	if bidirectional {
-		numBuckets /= 2
-		if n < 0 {
-			bucket += numBuckets
-			n = -n
-		}
-	} else {
-		if n < 0 {
-			n = 0
-		}
-	}
-
-	// Half buckets are for exact positions, half are for log-spaced
-	maxExact := numBuckets / 2
-	if n < maxExact {
-		bucket += n
-	} else {
-		// Log-spaced buckets
-		logVal := math.Log(float64(n)/float64(maxExact)) / math.Log(float64(maxDistance)/float64(maxExact))
-		bucket += maxExact + int32(logVal*float64(numBuckets-maxExact))
-		if bucket > numBuckets-1 {
-			bucket = numBuckets - 1
-		}
-	}
-
-	return bucket
-}
-
-// Forward for T5Block
-func (b *T5Block) Forward(x *mlx.Array, posBias *mlx.Array, eps float32) *mlx.Array {
-	// Self attention with residual
-	h := b.Layer0.Forward(x, posBias, eps)
-
-	// FFN with residual
-	h = b.Layer1.Forward(h, eps)
-
-	return h
-}
-
-// Forward for T5LayerSelfAttention
-func (l *T5LayerSelfAttention) Forward(x *mlx.Array, posBias *mlx.Array, eps float32) *mlx.Array {
-	// Pre-norm
-	normed := l.LayerNorm.Forward(x)
-
-	// Attention
-	attnOut := l.SelfAttention.Forward(normed, posBias)
-
-	// Residual
-	return mlx.Add(x, attnOut)
-}
-
-// Forward for T5Attention
-func (attn *T5Attention) Forward(x *mlx.Array, posBias *mlx.Array) *mlx.Array {
-	shape := x.Shape()
-	B := shape[0]
-	L := shape[1]
-	D := shape[2]
-
-	// Q, K, V projections (no bias)
-	// Weights are [out_features, in_features], so we use matmul with transpose
-	q := mlx.Matmul(x, mlx.Transpose(attn.Q, 1, 0))
-	k := mlx.Matmul(x, mlx.Transpose(attn.K, 1, 0))
-	v := mlx.Matmul(x, mlx.Transpose(attn.V, 1, 0))
-
-	// Reshape to [B, L, nheads, d_kv]
-	q = mlx.Reshape(q, B, L, attn.NHeads, attn.DKV)
-	k = mlx.Reshape(k, B, L, attn.NHeads, attn.DKV)
-	v = mlx.Reshape(v, B, L, attn.NHeads, attn.DKV)
-
-	// Transpose to [B, nheads, L, d_kv]
-	q = mlx.Transpose(q, 0, 2, 1, 3)
-	k = mlx.Transpose(k, 0, 2, 1, 3)
-	v = mlx.Transpose(v, 0, 2, 1, 3)
-
-	// Attention scores with relative position bias
-	// T5 uses UNSCALED dot-product attention: scores = q @ k.T + pos_bias
-	// (no 1/sqrt(d_k) scale factor like in standard transformers)
-	scores := mlx.Matmul(q, mlx.Transpose(k, 0, 1, 3, 2))
-	scores = mlx.Add(scores, posBias)
-
-	// Softmax
-	attnWeights := mlx.Softmax(scores, -1)
-
-	// Attend to values
-	out := mlx.Matmul(attnWeights, v)
-
-	// Transpose back [B, nheads, L, d_kv] -> [B, L, nheads, d_kv]
-	out = mlx.Transpose(out, 0, 2, 1, 3)
-	// Reshape to [B, L, D]
-	out = mlx.Reshape(out, B, L, attn.NHeads*attn.DKV)
-
-	// Output projection
-	out = mlx.Matmul(out, mlx.Transpose(attn.O, 1, 0))
-
-	_ = D // Silence unused warning
-	return out
-}
-
-// Forward for T5LayerFF
-func (l *T5LayerFF) Forward(x *mlx.Array, eps float32) *mlx.Array {
-	// Pre-norm
-	normed := l.LayerNorm.Forward(x)
-
-	// FFN
-	ffOut := l.DenseReluDense.Forward(normed)
-
-	// Residual
-	return mlx.Add(x, ffOut)
-}
-
-// geluNew implements the GELU activation with tanh approximation (gelu_new)
-// This matches HuggingFace transformers' gelu_new/OpenAI GPT implementation
-// Formula: 0.5 * x * (1 + tanh(sqrt(2/π) * (x + 0.044715 * x³)))
-func geluNew(x *mlx.Array) *mlx.Array {
-	sqrt2OverPi := float32(0.7978845608) // sqrt(2/π)
-	coeff := float32(0.044715)
-
-	x3 := mlx.Mul(mlx.Mul(x, x), x)
-	inner := mlx.MulScalar(mlx.Add(x, mlx.MulScalar(x3, coeff)), sqrt2OverPi)
-	return mlx.Mul(mlx.MulScalar(x, 0.5), mlx.AddScalar(mlx.Tanh(inner), 1.0))
-}
-
-// Forward for T5DenseGatedGelu (gated-gelu activation)
-func (d *T5DenseGatedGelu) Forward(x *mlx.Array) *mlx.Array {
-	// Gate projection with GELU activation (T5 v1.1/ByT5 uses gelu_new)
-	gate := mlx.Matmul(x, mlx.Transpose(d.Wi0, 1, 0))
-	gate = geluNew(gate)
-
-	// Up projection
-	up := mlx.Matmul(x, mlx.Transpose(d.Wi1, 1, 0))
-
-	// Gated output
-	h := mlx.Mul(gate, up)
-
-	// Down projection
-	return mlx.Matmul(h, mlx.Transpose(d.Wo, 1, 0))
-}
-
-// Forward for T5LayerNorm (RMSNorm variant)
-func (ln *T5LayerNorm) Forward(x *mlx.Array) *mlx.Array {
-	// T5 uses RMSNorm: x * rsqrt(mean(x^2) + eps) * weight
-	variance := mlx.Mean(mlx.Square(x), -1, true)
-	x = mlx.Mul(x, mlx.RSqrt(mlx.AddScalar(variance, ln.Eps)))
-	return mlx.Mul(x, ln.Weight)
-}

x/imagegen/models/glm_image/vae.go

@@ -1,477 +0,0 @@
-//go:build mlx
-
-package glm_image
-
-import (
-	"encoding/json"
-	"fmt"
-	"os"
-	"path/filepath"
-
-	"github.com/ollama/ollama/x/imagegen"
-	"github.com/ollama/ollama/x/imagegen/mlx"
-	"github.com/ollama/ollama/x/imagegen/safetensors"
-)
-
-// VAEConfig holds VAE decoder configuration
-type VAEConfig struct {
-	InChannels       int32     `json:"in_channels"`        // 3
-	OutChannels      int32     `json:"out_channels"`       // 3
-	LatentChannels   int32     `json:"latent_channels"`    // 16
-	BlockOutChannels []int32   `json:"block_out_channels"` // [128, 512, 1024, 1024]
-	LayersPerBlock   int32     `json:"layers_per_block"`   // 3
-	NormNumGroups    int32     `json:"norm_num_groups"`    // 32
-	ScalingFactor    float32   `json:"scaling_factor"`     // 0.18215
-	ShiftFactor      *float32  `json:"shift_factor"`       // null
-	LatentsMean      []float32 `json:"latents_mean"`       // [16 values]
-	LatentsStd       []float32 `json:"latents_std"`        // [16 values]
-}
-
-// VAEDecoder is the VAE latent decoder
-type VAEDecoder struct {
-	Config *VAEConfig
-
-	// Decoder components
-	ConvIn  *VAEConv2d    `weight:"decoder.conv_in"`
-	MidBlock *VAEMidBlock `weight:"decoder.mid_block"`
-	UpBlocks []*VAEUpBlock `weight:"decoder.up_blocks"`
-	ConvNormOut *GroupNorm `weight:"decoder.conv_norm_out"`
-	ConvOut *VAEConv2d    `weight:"decoder.conv_out"`
-}
-
-// VAEConv2d is a 2D convolution layer
-type VAEConv2d struct {
-	Weight *mlx.Array `weight:"weight"`
-	Bias   *mlx.Array `weight:"bias"`
-	Stride int32
-	Padding int32
-}
-
-// GroupNorm is group normalization
-type GroupNorm struct {
-	Weight    *mlx.Array `weight:"weight"`
-	Bias      *mlx.Array `weight:"bias"`
-	NumGroups int32
-	Eps       float32
-}
-
-// VAEMidBlock is the middle block of the VAE
-type VAEMidBlock struct {
-	Resnets []*VAEResnetBlock `weight:"resnets"`
-}
-
-// VAEUpBlock is an upsampling block
-type VAEUpBlock struct {
-	Resnets []*VAEResnetBlock `weight:"resnets"`
-	Upsamplers []*VAEUpsampler `weight:"upsamplers"`
-}
-
-// VAEResnetBlock is a residual block
-type VAEResnetBlock struct {
-	Norm1   *GroupNorm `weight:"norm1"`
-	Conv1   *VAEConv2d `weight:"conv1"`
-	Norm2   *GroupNorm `weight:"norm2"`
-	Conv2   *VAEConv2d `weight:"conv2"`
-	ConvShortcut *VAEConv2d `weight:"conv_shortcut,optional"` // Optional, for channel mismatch
-}
-
-// VAEUpsampler is an upsampling layer
-type VAEUpsampler struct {
-	Conv *VAEConv2d `weight:"conv"`
-}
-
-// Load loads the VAE decoder from manifest
-func (m *VAEDecoder) Load(manifest *imagegen.ModelManifest) error {
-	fmt.Print("  Loading VAE decoder... ")
-
-	// Load config
-	var cfg VAEConfig
-	if err := manifest.ReadConfigJSON("vae/config.json", &cfg); err != nil {
-		return fmt.Errorf("config: %w", err)
-	}
-	m.Config = &cfg
-
-	// Initialize structure based on config
-	numBlocks := len(cfg.BlockOutChannels)
-	m.UpBlocks = make([]*VAEUpBlock, numBlocks)
-
-	// Pre-allocate MidBlock resnets (VAE mid_block typically has 2 resnets)
-	m.MidBlock = &VAEMidBlock{
-		Resnets: make([]*VAEResnetBlock, 2),
-	}
-
-	// Pre-allocate UpBlocks with their resnets and upsamplers
-	// VAE decoder has layers_per_block+1 resnets per up_block (to match encoder)
-	// And all but the last up_block has an upsampler
-	for i := 0; i < numBlocks; i++ {
-		numResnets := cfg.LayersPerBlock + 1 // typically 4 resnets
-		m.UpBlocks[i] = &VAEUpBlock{
-			Resnets: make([]*VAEResnetBlock, numResnets),
-		}
-		// All but the last block has upsamplers
-		if i < numBlocks-1 {
-			m.UpBlocks[i].Upsamplers = make([]*VAEUpsampler, 1)
-		}
-	}
-
-	// Load weights
-	weights, err := imagegen.LoadWeightsFromManifest(manifest, "vae")
-	if err != nil {
-		return fmt.Errorf("weights: %w", err)
-	}
-	if err := weights.Load(mlx.DtypeBFloat16); err != nil {
-		return fmt.Errorf("load weights: %w", err)
-	}
-	defer weights.ReleaseAll()
-
-	if err := safetensors.LoadModule(m, weights, ""); err != nil {
-		return fmt.Errorf("load module: %w", err)
-	}
-
-	// Initialize GroupNorm parameters
-	m.initGroupNorms()
-
-	fmt.Println("✓")
-	return nil
-}
-
-// LoadFromPath loads the VAE decoder from a directory path
-func (m *VAEDecoder) LoadFromPath(path string) error {
-	fmt.Print("  Loading VAE decoder... ")
-
-	// Load config
-	var cfg VAEConfig
-	configPath := filepath.Join(path, "config.json")
-	data, err := os.ReadFile(configPath)
-	if err != nil {
-		return fmt.Errorf("read config: %w", err)
-	}
-	if err := json.Unmarshal(data, &cfg); err != nil {
-		return fmt.Errorf("parse config: %w", err)
-	}
-	m.Config = &cfg
-
-	// Initialize structure based on config
-	numBlocks := len(cfg.BlockOutChannels)
-	m.UpBlocks = make([]*VAEUpBlock, numBlocks)
-
-	// Pre-allocate MidBlock resnets (VAE mid_block typically has 2 resnets)
-	m.MidBlock = &VAEMidBlock{
-		Resnets: make([]*VAEResnetBlock, 2),
-	}
-
-	// Pre-allocate UpBlocks with their resnets and upsamplers
-	for i := 0; i < numBlocks; i++ {
-		numResnets := cfg.LayersPerBlock + 1
-		m.UpBlocks[i] = &VAEUpBlock{
-			Resnets: make([]*VAEResnetBlock, numResnets),
-		}
-		if i < numBlocks-1 {
-			m.UpBlocks[i].Upsamplers = make([]*VAEUpsampler, 1)
-		}
-	}
-
-	// Load weights from safetensors files
-	weights, err := safetensors.LoadModelWeights(path)
-	if err != nil {
-		return fmt.Errorf("weights: %w", err)
-	}
-	if err := weights.Load(mlx.DtypeBFloat16); err != nil {
-		return fmt.Errorf("load weights: %w", err)
-	}
-	defer weights.ReleaseAll()
-
-	if err := safetensors.LoadModule(m, weights, ""); err != nil {
-		return fmt.Errorf("load module: %w", err)
-	}
-
-	// Initialize GroupNorm parameters
-	m.initGroupNorms()
-
-	fmt.Println("✓")
-	return nil
-}
-
-func (m *VAEDecoder) initGroupNorms() {
-	cfg := m.Config
-	numGroups := cfg.NormNumGroups
-	eps := float32(1e-6) // Must match diffusers VAE (1e-6, not 1e-5)
-
-	if m.ConvNormOut != nil {
-		m.ConvNormOut.NumGroups = numGroups
-		m.ConvNormOut.Eps = eps
-	}
-
-	if m.MidBlock != nil {
-		for _, resnet := range m.MidBlock.Resnets {
-			if resnet.Norm1 != nil {
-				resnet.Norm1.NumGroups = numGroups
-				resnet.Norm1.Eps = eps
-			}
-			if resnet.Norm2 != nil {
-				resnet.Norm2.NumGroups = numGroups
-				resnet.Norm2.Eps = eps
-			}
-		}
-	}
-
-	for _, upBlock := range m.UpBlocks {
-		if upBlock == nil {
-			continue
-		}
-		for _, resnet := range upBlock.Resnets {
-			if resnet == nil {
-				continue
-			}
-			if resnet.Norm1 != nil {
-				resnet.Norm1.NumGroups = numGroups
-				resnet.Norm1.Eps = eps
-			}
-			if resnet.Norm2 != nil {
-				resnet.Norm2.NumGroups = numGroups
-				resnet.Norm2.Eps = eps
-			}
-		}
-	}
-}
-
-// Decode decodes latents to an image
-func (m *VAEDecoder) Decode(latents *mlx.Array) *mlx.Array {
-	cfg := m.Config
-
-	// Apply latent denormalization if mean/std are provided
-	// This matches diffusers GLM-Image: latents = latents * std + mean
-	// Note: GLM-Image does NOT divide by scaling_factor (unlike standard SD VAEs)
-	if len(cfg.LatentsMean) > 0 && len(cfg.LatentsStd) > 0 {
-		latents = m.denormalizeLatents(latents)
-	}
-
-	// Convert from NCHW to NHWC for processing
-	// [B, C, H, W] -> [B, H, W, C]
-	x := mlx.Transpose(latents, 0, 2, 3, 1)
-
-	// Initial convolution
-	x = m.ConvIn.Forward(x)
-
-	// Mid block
-	x = m.MidBlock.Forward(x)
-
-	// Up blocks (forward order - index 0 is at lowest resolution/highest channels)
-	for i := 0; i < len(m.UpBlocks); i++ {
-		if m.UpBlocks[i] != nil {
-			x = m.UpBlocks[i].Forward(x)
-		}
-	}
-
-	// Final normalization and convolution
-	x = m.ConvNormOut.Forward(x)
-	x = mlx.SiLU(x)
-	x = m.ConvOut.Forward(x)
-
-	// Convert back to NCHW
-	// [B, H, W, C] -> [B, C, H, W]
-	x = mlx.Transpose(x, 0, 3, 1, 2)
-
-	// Clamp to valid range and convert to [0, 1]
-	x = mlx.ClipScalar(x, -1.0, 1.0, true, true)
-	x = mlx.AddScalar(x, 1.0)
-	x = mlx.DivScalar(x, 2.0)
-
-	return x
-}
-
-// denormalizeLatents applies the latent mean/std denormalization
-func (m *VAEDecoder) denormalizeLatents(latents *mlx.Array) *mlx.Array {
-	cfg := m.Config
-
-	// Create mean and std arrays [1, C, 1, 1] for broadcasting
-	mean := mlx.NewArray(cfg.LatentsMean, []int32{1, int32(len(cfg.LatentsMean)), 1, 1})
-	std := mlx.NewArray(cfg.LatentsStd, []int32{1, int32(len(cfg.LatentsStd)), 1, 1})
-
-	// Denormalize: latents * std + mean
-	latents = mlx.Mul(latents, std)
-	latents = mlx.Add(latents, mean)
-
-	return latents
-}
-
-// Forward for VAEConv2d
-func (c *VAEConv2d) Forward(x *mlx.Array) *mlx.Array {
-	// x: [B, H, W, C_in] (NHWC)
-	// PyTorch weight: [C_out, C_in, kH, kW] (OIHW)
-	// MLX conv2d expects weight: [C_out, kH, kW, C_in] (OHWI)
-	// So we need to transpose from OIHW to OHWI
-
-	stride := c.Stride
-	if stride == 0 {
-		stride = 1
-	}
-	padding := c.Padding
-	if padding == 0 {
-		// Default to same padding for 3x3 kernels
-		wShape := c.Weight.Shape()
-		if len(wShape) >= 3 && wShape[2] == 3 {
-			padding = 1
-		}
-	}
-
-	// Transpose weight from OIHW [out, in, h, w] to OHWI [out, h, w, in]
-	weight := mlx.Transpose(c.Weight, 0, 2, 3, 1)
-
-	out := mlx.Conv2d(x, weight, stride, padding)
-	if c.Bias != nil {
-		// Bias: [C_out] -> [1, 1, 1, C_out]
-		bias := mlx.Reshape(c.Bias, 1, 1, 1, -1)
-		out = mlx.Add(out, bias)
-	}
-	return out
-}
-
-// Forward for GroupNorm
-func (gn *GroupNorm) Forward(x *mlx.Array) *mlx.Array {
-	// x: [B, H, W, C] (NHWC)
-	shape := x.Shape()
-	B := shape[0]
-	H := shape[1]
-	W := shape[2]
-	C := shape[3]
-
-	numGroups := gn.NumGroups
-	if numGroups == 0 {
-		numGroups = 32
-	}
-	groupSize := C / numGroups
-
-	// Reshape to [B, H, W, groups, groupSize]
-	x = mlx.Reshape(x, B, H, W, numGroups, groupSize)
-
-	// Compute mean and variance per group
-	mean := mlx.Mean(x, 1, true)
-	mean = mlx.Mean(mean, 2, true)
-	mean = mlx.Mean(mean, 4, true)
-
-	xCentered := mlx.Sub(x, mean)
-	variance := mlx.Mean(mlx.Square(xCentered), 1, true)
-	variance = mlx.Mean(variance, 2, true)
-	variance = mlx.Mean(variance, 4, true)
-
-	// Normalize
-	xNorm := mlx.Div(xCentered, mlx.Sqrt(mlx.AddScalar(variance, gn.Eps)))
-
-	// Reshape back
-	xNorm = mlx.Reshape(xNorm, B, H, W, C)
-
-	// Scale and shift
-	if gn.Weight != nil {
-		weight := mlx.Reshape(gn.Weight, 1, 1, 1, C)
-		xNorm = mlx.Mul(xNorm, weight)
-	}
-	if gn.Bias != nil {
-		bias := mlx.Reshape(gn.Bias, 1, 1, 1, C)
-		xNorm = mlx.Add(xNorm, bias)
-	}
-
-	return xNorm
-}
-
-// Forward for VAEMidBlock
-func (mb *VAEMidBlock) Forward(x *mlx.Array) *mlx.Array {
-	for _, resnet := range mb.Resnets {
-		x = resnet.Forward(x)
-	}
-	return x
-}
-
-// Forward for VAEUpBlock
-func (ub *VAEUpBlock) Forward(x *mlx.Array) *mlx.Array {
-	// Apply resnets
-	for _, resnet := range ub.Resnets {
-		if resnet != nil {
-			x = resnet.Forward(x)
-		}
-	}
-
-	// Apply upsamplers
-	for _, upsampler := range ub.Upsamplers {
-		if upsampler != nil {
-			x = upsampler.Forward(x)
-		}
-	}
-
-	return x
-}
-
-// Forward for VAEResnetBlock
-func (rb *VAEResnetBlock) Forward(x *mlx.Array) *mlx.Array {
-	residual := x
-
-	// First norm + activation + conv
-	h := rb.Norm1.Forward(x)
-	h = mlx.SiLU(h)
-	h = rb.Conv1.Forward(h)
-
-	// Second norm + activation + conv
-	h = rb.Norm2.Forward(h)
-	h = mlx.SiLU(h)
-	h = rb.Conv2.Forward(h)
-
-	// Shortcut for channel mismatch
-	if rb.ConvShortcut != nil {
-		residual = rb.ConvShortcut.Forward(residual)
-	}
-
-	return mlx.Add(h, residual)
-}
-
-// Forward for VAEUpsampler (2x nearest neighbor upsample + conv)
-func (us *VAEUpsampler) Forward(x *mlx.Array) *mlx.Array {
-	// x: [B, H, W, C]
-	// 2x nearest neighbor upsample
-	x = upsample2x(x)
-
-	// Conv
-	if us.Conv != nil {
-		x = us.Conv.Forward(x)
-	}
-
-	return x
-}
-
-// upsample2x performs 2x nearest neighbor upsampling.
-// Input and output are in NHWC format: [B, H, W, C] -> [B, H*2, W*2, C]
-func upsample2x(x *mlx.Array) *mlx.Array {
-	shape := x.Shape()
-	B := shape[0]
-	H := shape[1]
-	W := shape[2]
-	C := shape[3]
-
-	// Create indices [0, 0, 1, 1, 2, 2, ...] for nearest neighbor
-	hIndices := make([]int32, H*2)
-	for i := int32(0); i < H; i++ {
-		hIndices[i*2] = i
-		hIndices[i*2+1] = i
-	}
-	wIndices := make([]int32, W*2)
-	for i := int32(0); i < W; i++ {
-		wIndices[i*2] = i
-		wIndices[i*2+1] = i
-	}
-
-	hIdx := mlx.NewArrayInt32(hIndices, []int32{H * 2})
-	wIdx := mlx.NewArrayInt32(wIndices, []int32{W * 2})
-
-	// Take along height axis
-	x = mlx.Reshape(x, B*H, W, C)
-	x = mlx.Take(x, wIdx, 1) // [B*H, W*2, C]
-	x = mlx.Reshape(x, B, H, W*2, C)
-
-	// Take along width axis - transpose to [B, W*2, H, C], take, transpose back
-	x = mlx.Transpose(x, 0, 2, 1, 3) // [B, W*2, H, C]
-	x = mlx.Reshape(x, B*(W*2), H, C)
-	x = mlx.Take(x, hIdx, 1) // [B*(W*2), H*2, C]
-	x = mlx.Reshape(x, B, W*2, H*2, C)
-	x = mlx.Transpose(x, 0, 2, 1, 3) // [B, H*2, W*2, C]
-
-	return x
-}

x/imagegen/models/glm_image/vision_language_encoder.go

@@ -1,982 +0,0 @@
-//go:build mlx
-
-package glm_image
-
-import (
-	"encoding/json"
-	"fmt"
-	"math"
-	"os"
-	"path/filepath"
-
-	"github.com/ollama/ollama/x/imagegen"
-	"github.com/ollama/ollama/x/imagegen/cache"
-	"github.com/ollama/ollama/x/imagegen/mlx"
-	"github.com/ollama/ollama/x/imagegen/nn"
-	"github.com/ollama/ollama/x/imagegen/safetensors"
-)
-
-// VisionLanguageConfig holds GLM-Image AR generator configuration
-type VisionLanguageConfig struct {
-	// Text model config
-	HiddenSize        int32   `json:"hidden_size"`         // 4096
-	NumHiddenLayers   int32   `json:"num_hidden_layers"`   // 40
-	IntermediateSize  int32   `json:"intermediate_size"`   // 13696
-	NumAttentionHeads int32   `json:"num_attention_heads"` // 32
-	NumKeyValueHeads  int32   `json:"num_key_value_heads"` // 2
-	VocabSize         int32   `json:"vocab_size"`          // 168064
-	RMSNormEps        float32 `json:"rms_norm_eps"`        // 1e-5
-
-	// RoPE config
-	RopeTheta           float32 `json:"rope_theta"`            // 10000
-	PartialRotaryFactor float32 `json:"partial_rotary_factor"` // 0.5
-	MRoPESection        []int32 `json:"mrope_section"`         // [8, 12, 12]
-
-	// Visual token config
-	VisionVocabSize   int32 `json:"vision_vocab_size"`    // 16512
-	ImageStartTokenID int32 `json:"image_start_token_id"` // 16384
-	ImageEndTokenID   int32 `json:"image_end_token_id"`   // 16385
-	ImageTokenID      int32 `json:"image_token_id"`       // 167855
-
-	// Computed
-	HeadDim int32
-}
-
-// VisionLanguageEncoder is the 9B AR generator
-type VisionLanguageEncoder struct {
-	Config *VisionLanguageConfig
-
-	// Embedding
-	EmbedTokens *nn.Embedding `weight:"model.language_model.embed_tokens"`
-
-	// Transformer layers
-	Layers []*GLMBlock `weight:"model.language_model.layers"`
-
-	// Final norm
-	FinalNorm *nn.RMSNorm `weight:"model.language_model.norm"`
-
-	// LM Head
-	LMHead *mlx.Array `weight:"lm_head.weight"`
-}
-
-// GLMBlock is a single transformer block in GLM-4 style
-type GLMBlock struct {
-	// Pre-attention norm (GLM uses post-LN variant)
-	InputLayerNorm    *nn.RMSNorm `weight:"input_layernorm"`
-	PostSelfAttnNorm  *nn.RMSNorm `weight:"post_self_attn_layernorm"`
-	PostAttnLayerNorm *nn.RMSNorm `weight:"post_attention_layernorm"`
-	PostMLPLayerNorm  *nn.RMSNorm `weight:"post_mlp_layernorm"`
-
-	// Attention
-	SelfAttn *GLMAttention `weight:"self_attn"`
-
-	// MLP (fused gate_up)
-	MLP *GLMMLP `weight:"mlp"`
-}
-
-// GLMAttention implements GQA with partial rotary and MRoPE
-type GLMAttention struct {
-	QProj *mlx.Array `weight:"q_proj.weight"`
-	KProj *mlx.Array `weight:"k_proj.weight"`
-	VProj *mlx.Array `weight:"v_proj.weight"`
-	OProj *mlx.Array `weight:"o_proj.weight"`
-
-	// QKV have biases in GLM
-	QBias *mlx.Array `weight:"q_proj.bias"`
-	KBias *mlx.Array `weight:"k_proj.bias"`
-	VBias *mlx.Array `weight:"v_proj.bias"`
-
-	// Computed
-	NHeads           int32
-	NKVHeads         int32
-	HeadDim          int32
-	Scale            float32
-	PartialRotary    float32   // Only rotate this fraction of head_dim
-	RopeTheta        float32
-	MRoPESection     []int32   // [8, 12, 12] - frequency pairs per dimension (temporal, height, width)
-}
-
-// ARCache holds KV caches for all layers using the shared cache implementation
-type ARCache struct {
-	Layers []cache.Cache
-}
-
-// NewARCache creates a new cache for the given number of layers
-func NewARCache(numLayers int32) *ARCache {
-	layers := make([]cache.Cache, numLayers)
-	for i := range layers {
-		layers[i] = cache.NewKVCache()
-	}
-	return &ARCache{Layers: layers}
-}
-
-// Free releases all cached tensors
-func (c *ARCache) Free() {
-	for _, layer := range c.Layers {
-		for _, arr := range layer.State() {
-			if arr != nil {
-				arr.Free()
-			}
-		}
-	}
-}
-
-// GLMMLP implements fused gate_up SwiGLU MLP
-type GLMMLP struct {
-	// GLM uses fused gate_up_proj: [hidden, 2*intermediate]
-	GateUpProj *mlx.Array `weight:"gate_up_proj.weight"`
-	DownProj   *mlx.Array `weight:"down_proj.weight"`
-}
-
-// Load loads the vision-language encoder from manifest
-func (m *VisionLanguageEncoder) Load(manifest *imagegen.ModelManifest) error {
-	fmt.Print("  Loading vision-language encoder... ")
-
-	// Load config
-	var rawCfg struct {
-		TextConfig struct {
-			HiddenSize        int32   `json:"hidden_size"`
-			NumHiddenLayers   int32   `json:"num_hidden_layers"`
-			IntermediateSize  int32   `json:"intermediate_size"`
-			NumAttentionHeads int32   `json:"num_attention_heads"`
-			NumKeyValueHeads  int32   `json:"num_key_value_heads"`
-			VocabSize         int32   `json:"vocab_size"`
-			RMSNormEps        float32 `json:"rms_norm_eps"`
-			VisionVocabSize   int32   `json:"vision_vocab_size"`
-			RopeParameters    struct {
-				RopeTheta           float32 `json:"rope_theta"`
-				PartialRotaryFactor float32 `json:"partial_rotary_factor"`
-				MRoPESection        []int32 `json:"mrope_section"`
-			} `json:"rope_parameters"`
-		} `json:"text_config"`
-		ImageStartTokenID int32 `json:"image_start_token_id"`
-		ImageEndTokenID   int32 `json:"image_end_token_id"`
-		ImageTokenID      int32 `json:"image_token_id"`
-	}
-
-	if err := manifest.ReadConfigJSON("vision_language_encoder/config.json", &rawCfg); err != nil {
-		return fmt.Errorf("config: %w", err)
-	}
-
-	cfg := &VisionLanguageConfig{
-		HiddenSize:          rawCfg.TextConfig.HiddenSize,
-		NumHiddenLayers:     rawCfg.TextConfig.NumHiddenLayers,
-		IntermediateSize:    rawCfg.TextConfig.IntermediateSize,
-		NumAttentionHeads:   rawCfg.TextConfig.NumAttentionHeads,
-		NumKeyValueHeads:    rawCfg.TextConfig.NumKeyValueHeads,
-		VocabSize:           rawCfg.TextConfig.VocabSize,
-		RMSNormEps:          rawCfg.TextConfig.RMSNormEps,
-		VisionVocabSize:     rawCfg.TextConfig.VisionVocabSize,
-		RopeTheta:           rawCfg.TextConfig.RopeParameters.RopeTheta,
-		PartialRotaryFactor: rawCfg.TextConfig.RopeParameters.PartialRotaryFactor,
-		MRoPESection:        rawCfg.TextConfig.RopeParameters.MRoPESection,
-		ImageStartTokenID:   rawCfg.ImageStartTokenID,
-		ImageEndTokenID:     rawCfg.ImageEndTokenID,
-		ImageTokenID:        rawCfg.ImageTokenID,
-	}
-
-	cfg.HeadDim = cfg.HiddenSize / cfg.NumAttentionHeads
-	m.Config = cfg
-
-	// Pre-allocate layers
-	m.Layers = make([]*GLMBlock, cfg.NumHiddenLayers)
-
-	// Load weights
-	weights, err := imagegen.LoadWeightsFromManifest(manifest, "vision_language_encoder")
-	if err != nil {
-		return fmt.Errorf("weights: %w", err)
-	}
-	if err := weights.Load(mlx.DtypeBFloat16); err != nil {
-		return fmt.Errorf("load weights: %w", err)
-	}
-	defer weights.ReleaseAll()
-
-	if err := safetensors.LoadModule(m, weights, ""); err != nil {
-		return fmt.Errorf("load module: %w", err)
-	}
-
-	m.initComputedFields()
-	fmt.Printf("✓ [%d layers]\n", cfg.NumHiddenLayers)
-	return nil
-}
-
-// LoadFromPath loads the vision-language encoder from a directory path
-func (m *VisionLanguageEncoder) LoadFromPath(path string) error {
-	fmt.Print("  Loading vision-language encoder... ")
-
-	// Load config
-	var rawCfg struct {
-		TextConfig struct {
-			HiddenSize        int32   `json:"hidden_size"`
-			NumHiddenLayers   int32   `json:"num_hidden_layers"`
-			IntermediateSize  int32   `json:"intermediate_size"`
-			NumAttentionHeads int32   `json:"num_attention_heads"`
-			NumKeyValueHeads  int32   `json:"num_key_value_heads"`
-			VocabSize         int32   `json:"vocab_size"`
-			RMSNormEps        float32 `json:"rms_norm_eps"`
-			VisionVocabSize   int32   `json:"vision_vocab_size"`
-			RopeParameters    struct {
-				RopeTheta           float32 `json:"rope_theta"`
-				PartialRotaryFactor float32 `json:"partial_rotary_factor"`
-				MRoPESection        []int32 `json:"mrope_section"`
-			} `json:"rope_parameters"`
-		} `json:"text_config"`
-		ImageStartTokenID int32 `json:"image_start_token_id"`
-		ImageEndTokenID   int32 `json:"image_end_token_id"`
-		ImageTokenID      int32 `json:"image_token_id"`
-	}
-
-	configPath := filepath.Join(path, "config.json")
-	data, err := os.ReadFile(configPath)
-	if err != nil {
-		return fmt.Errorf("read config: %w", err)
-	}
-	if err := json.Unmarshal(data, &rawCfg); err != nil {
-		return fmt.Errorf("parse config: %w", err)
-	}
-
-	cfg := &VisionLanguageConfig{
-		HiddenSize:          rawCfg.TextConfig.HiddenSize,
-		NumHiddenLayers:     rawCfg.TextConfig.NumHiddenLayers,
-		IntermediateSize:    rawCfg.TextConfig.IntermediateSize,
-		NumAttentionHeads:   rawCfg.TextConfig.NumAttentionHeads,
-		NumKeyValueHeads:    rawCfg.TextConfig.NumKeyValueHeads,
-		VocabSize:           rawCfg.TextConfig.VocabSize,
-		RMSNormEps:          rawCfg.TextConfig.RMSNormEps,
-		VisionVocabSize:     rawCfg.TextConfig.VisionVocabSize,
-		RopeTheta:           rawCfg.TextConfig.RopeParameters.RopeTheta,
-		PartialRotaryFactor: rawCfg.TextConfig.RopeParameters.PartialRotaryFactor,
-		MRoPESection:        rawCfg.TextConfig.RopeParameters.MRoPESection,
-		ImageStartTokenID:   rawCfg.ImageStartTokenID,
-		ImageEndTokenID:     rawCfg.ImageEndTokenID,
-		ImageTokenID:        rawCfg.ImageTokenID,
-	}
-
-	cfg.HeadDim = cfg.HiddenSize / cfg.NumAttentionHeads
-	m.Config = cfg
-
-	// Pre-allocate layers
-	m.Layers = make([]*GLMBlock, cfg.NumHiddenLayers)
-
-	// Load weights
-	weights, err := safetensors.LoadModelWeights(path)
-	if err != nil {
-		return fmt.Errorf("weights: %w", err)
-	}
-	if err := weights.Load(mlx.DtypeBFloat16); err != nil {
-		return fmt.Errorf("load weights: %w", err)
-	}
-	defer weights.ReleaseAll()
-
-	if err := safetensors.LoadModule(m, weights, ""); err != nil {
-		return fmt.Errorf("load module: %w", err)
-	}
-
-	m.initComputedFields()
-	fmt.Printf("✓ [%d layers]\n", cfg.NumHiddenLayers)
-	return nil
-}
-
-func (m *VisionLanguageEncoder) initComputedFields() {
-	cfg := m.Config
-	for _, block := range m.Layers {
-		block.SelfAttn.NHeads = cfg.NumAttentionHeads
-		block.SelfAttn.NKVHeads = cfg.NumKeyValueHeads
-		block.SelfAttn.HeadDim = cfg.HeadDim
-		block.SelfAttn.Scale = float32(1.0 / math.Sqrt(float64(cfg.HeadDim)))
-		block.SelfAttn.PartialRotary = cfg.PartialRotaryFactor
-		block.SelfAttn.RopeTheta = cfg.RopeTheta
-		block.SelfAttn.MRoPESection = cfg.MRoPESection
-
-		// Set norm eps
-		block.InputLayerNorm.Eps = cfg.RMSNormEps
-		block.PostSelfAttnNorm.Eps = cfg.RMSNormEps
-		block.PostAttnLayerNorm.Eps = cfg.RMSNormEps
-		block.PostMLPLayerNorm.Eps = cfg.RMSNormEps
-	}
-	m.FinalNorm.Eps = cfg.RMSNormEps
-}
-
-// Generate autoregressively generates visual tokens with KV caching
-func (m *VisionLanguageEncoder) Generate(
-	prompt string,
-	tok *GLMTokenizer,
-	maxTokens int32,
-	temperature float32,
-	topP float32,
-	seed int64,
-	targetHeight, targetWidth int32,
-	progressFn func(int),
-) *mlx.Array {
-	cfg := m.Config
-
-	// Encode prompt with grid tokens using GLM tokenizer
-	// Format: {prompt}<sop>{h} {w}<eop><sop>{prev_h} {prev_w}<eop><|dit_token_16384|>
-	tokens := tok.EncodeForGeneration(prompt, targetHeight, targetWidth)
-
-	// Calculate grid dimensions for MRoPE position IDs
-	factor := int32(32)
-	tokenH := targetHeight / factor
-	tokenW := targetWidth / factor
-	ratio := float64(tokenH) / float64(tokenW)
-	prevTokenH := int32(math.Sqrt(ratio) * 16)
-	prevTokenW := int32(math.Sqrt(1.0/ratio) * 16)
-	prevGridSize := prevTokenH * prevTokenW
-
-	// Create KV cache for all layers
-	cache := NewARCache(cfg.NumHiddenLayers)
-	defer cache.Free()
-
-	// ===== PREFILL PHASE =====
-	// Process entire prompt at once, populate cache
-	promptLen := int32(len(tokens))
-	tokenArr := mlx.NewArrayInt32(tokens, []int32{1, promptLen})
-	h := m.EmbedTokens.Forward(tokenArr)
-	tokenArr.Free()
-
-	mlx.Eval(h)
-
-	// Compute position IDs for prefill (text tokens use same position for all dims)
-	prefillPositions := make([][]int32, 3)
-	for dim := 0; dim < 3; dim++ {
-		prefillPositions[dim] = make([]int32, promptLen)
-		for i := int32(0); i < promptLen; i++ {
-			prefillPositions[dim][i] = i
-		}
-	}
-
-	// Forward through layers (prefill)
-	for i, layer := range m.Layers {
-		oldH := h
-		h = layer.ForwardWithCache(h, promptLen, 0, cfg.RMSNormEps, cache.Layers[i], prefillPositions)
-		if i > 0 {
-			oldH.Free()
-		}
-	}
-	// Eval h and cache arrays together so cache is materialized
-	evalArgs := []*mlx.Array{h}
-	for _, lc := range cache.Layers {
-		evalArgs = append(evalArgs, lc.State()...)
-	}
-	mlx.Eval(evalArgs...)
-
-	// Final norm and get logits for last position
-	preNormH := h
-	h = m.FinalNorm.Forward(h, cfg.RMSNormEps)
-	preNormH.Free()
-
-	lastH := mlx.Slice(h, []int32{0, promptLen - 1, 0}, []int32{1, promptLen, cfg.HiddenSize})
-	h.Free()
-	lastH = mlx.Reshape(lastH, 1, cfg.HiddenSize)
-	logits := mlx.Matmul(lastH, mlx.Transpose(m.LMHead, 1, 0))
-	lastH.Free()
-
-	// Sample first token
-	var sampleCounter int64 = 0
-	nextToken := sampleVisualToken(logits, temperature, topP, cfg, seed, &sampleCounter)
-	logits.Free()
-
-	// AR generation loop with caching
-	// Visual tokens are stored as VQ codebook indices [0, 16383]
-	// The LM head outputs indices [0, 16511] where:
-	// - [0, 16383] are VQ codes
-	// - 16384 is BOS
-	// - 16385 is EOS
-	visualTokens := make([]int32, 0, maxTokens)
-	posOffset := promptLen
-	visualTokenIdx := int32(0) // Index within visual token sequence for grid position calculation
-
-	// Preallocate slice for old cache state to reuse
-	oldCacheState := make([]*mlx.Array, 0, len(m.Layers)*2)
-
-	for i := int32(0); i < maxTokens; i++ {
-		if progressFn != nil {
-			progressFn(int(i))
-		}
-
-		// Check for end token (EOS = 16385)
-		if nextToken == cfg.ImageEndTokenID {
-			break
-		}
-
-		// Skip BOS token (16384), only store actual VQ codes [0, 16383]
-		if nextToken == cfg.ImageStartTokenID {
-			// BOS token - skip storing but continue generation
-		} else if nextToken < cfg.ImageStartTokenID {
-			// This is an actual VQ code [0, 16383] - store it
-			visualTokens = append(visualTokens, nextToken)
-		}
-		// Tokens >= 16386 are other special tokens, skip them
-
-		// ===== DECODE PHASE =====
-		// Save old cache state before forward (to free after eval)
-		oldCacheState = oldCacheState[:0]
-		for _, lc := range cache.Layers {
-			oldCacheState = append(oldCacheState, lc.State()...)
-		}
-
-		// Only process the new token, use cached K,V
-		tokenArr := mlx.NewArrayInt32([]int32{nextToken}, []int32{1, 1})
-		h := m.EmbedTokens.Forward(tokenArr)
-		tokenArr.Free()
-
-		// Compute MRoPE position IDs for this visual token
-		// Visual tokens are arranged in two grids: prev grid then target grid
-		// Position dimensions: [temporal, height, width]
-		decodePositions := computeVisualTokenPositions(
-			visualTokenIdx, posOffset, promptLen,
-			prevTokenH, prevTokenW, prevGridSize,
-			tokenH, tokenW,
-		)
-
-		// Forward through layers (decode with cache)
-		for j, layer := range m.Layers {
-			oldH := h
-			h = layer.ForwardWithCache(h, 1, posOffset, cfg.RMSNormEps, cache.Layers[j], decodePositions)
-			if j > 0 { // Don't free the embedding on first layer
-				oldH.Free()
-			}
-		}
-
-		// Eval h and new cache state
-		newCacheState := make([]*mlx.Array, 0, len(m.Layers)*2)
-		for _, lc := range cache.Layers {
-			newCacheState = append(newCacheState, lc.State()...)
-		}
-		mlx.Eval(append([]*mlx.Array{h}, newCacheState...)...)
-
-		// Free old cache state (now that new state is evaluated)
-		for _, arr := range oldCacheState {
-			if arr != nil {
-				arr.Free()
-			}
-		}
-
-		// Final norm
-		preNormH := h
-		h = m.FinalNorm.Forward(h, cfg.RMSNormEps)
-		preNormH.Free()
-
-		// Get logits (h is already [1, 1, hidden_size])
-		h = mlx.Reshape(h, 1, cfg.HiddenSize)
-		logits := mlx.Matmul(h, mlx.Transpose(m.LMHead, 1, 0))
-		h.Free()
-
-		// Sample next token
-		nextToken = sampleVisualToken(logits, temperature, topP, cfg, seed, &sampleCounter)
-		logits.Free()
-
-		posOffset++
-		visualTokenIdx++
-
-		// Periodically clear cache to release intermediate memory
-		if i%256 == 0 {
-			mlx.ClearCache()
-		}
-	}
-
-	if len(visualTokens) == 0 {
-		// Return at least one token to avoid empty tensor issues
-		visualTokens = append(visualTokens, 0)
-	}
-
-	return mlx.NewArrayInt32(visualTokens, []int32{1, int32(len(visualTokens))})
-}
-
-// computeVisualTokenPositions computes MRoPE position IDs for a visual token
-// Returns [3][1] position IDs for temporal, height, and width dimensions
-//
-// MRoPE position encoding for GLM-Image visual tokens:
-// - temporal: CONSTANT within each grid (= decode_pos at grid start)
-// - height: decode_pos + row index within grid
-// - width: decode_pos + column index within grid
-//
-// Between grids, decode_pos advances by max(grid_h, grid_w) to ensure
-// sufficient positional separation.
-func computeVisualTokenPositions(
-	visualIdx int32, absPos int32, promptLen int32,
-	prevH, prevW, prevSize int32,
-	targetH, targetW int32,
-) [][]int32 {
-	positions := make([][]int32, 3)
-	for dim := 0; dim < 3; dim++ {
-		positions[dim] = make([]int32, 1)
-	}
-
-	// First grid (prev grid) starts at decode_pos = promptLen
-	prevGridDecodePos := promptLen
-
-	// Second grid (target grid) starts after first grid
-	// next_pos = prev_decode_pos + max(prevH, prevW)
-	maxPrev := prevH
-	if prevW > maxPrev {
-		maxPrev = prevW
-	}
-	targetGridDecodePos := prevGridDecodePos + maxPrev
-
-	// Compute position IDs based on which grid the token is in
-	if visualIdx < prevSize {
-		// Token is in the prev grid (prev_token_h × prev_token_w)
-		row := visualIdx / prevW
-		col := visualIdx % prevW
-
-		// temporal is CONSTANT for all tokens in this grid
-		positions[0][0] = prevGridDecodePos
-		// height and width are relative to grid's decode_pos
-		positions[1][0] = prevGridDecodePos + row
-		positions[2][0] = prevGridDecodePos + col
-	} else {
-		// Token is in the target grid (token_h × token_w)
-		targetIdx := visualIdx - prevSize
-		row := targetIdx / targetW
-		col := targetIdx % targetW
-
-		// temporal is CONSTANT for all tokens in this grid
-		positions[0][0] = targetGridDecodePos
-		// height and width are relative to grid's decode_pos
-		positions[1][0] = targetGridDecodePos + row
-		positions[2][0] = targetGridDecodePos + col
-	}
-
-	_ = targetH // Used for documentation clarity
-	_ = absPos  // No longer used - kept for API compatibility
-	return positions
-}
-
-// sampleVisualToken samples from the visual vocabulary using top-p (nucleus) sampling
-// Note: For GLM-Image, greedy decoding is not allowed as it may cause repetitive outputs
-// Returns a visual token ID in range [0, 16511] which directly indexes into the embedding table
-// sampleCounter is incremented for each call to ensure different random values
-func sampleVisualToken(logits *mlx.Array, temperature float32, topP float32, cfg *VisionLanguageConfig, seed int64, sampleCounter *int64) int32 {
-	// The LMHead outputs logits for visual tokens only (shape [1, 16512])
-	// Output index directly corresponds to vocab ID [0, 16511]
-	// No offset needed - the visual tokens are at vocab IDs [0, 16511]
-	visualLogits := logits
-
-	// Apply temperature
-	if temperature != 1.0 && temperature > 0 {
-		visualLogits = mlx.DivScalar(visualLogits, temperature)
-	}
-
-	// Apply softmax to get probabilities
-	probs := mlx.Softmax(visualLogits, -1)
-	mlx.Eval(probs)
-
-	// Get the sampled index using top-p sampling
-	// This directly gives us the vocab ID in [0, 16511]
-	// Special tokens: 16384 = BOS, 16385 = EOS
-	// Use seed + counter for reproducible but different random values
-	effectiveSeed := seed + *sampleCounter
-	*sampleCounter++
-	return sampleTopP(probs, topP, effectiveSeed)
-}
-
-// sampleTopP implements nucleus (top-p) sampling
-// probs: [1, vocab_size] probability distribution
-// topP: cumulative probability threshold (e.g., 0.75)
-// seed: random seed for reproducible sampling
-func sampleTopP(probs *mlx.Array, topP float32, seed int64) int32 {
-	// Negate probs for descending sort (Argsort only does ascending)
-	negProbs := mlx.MulScalar(probs, -1)
-	sortedIndices := mlx.Argsort(negProbs, -1)
-	sortedProbs := mlx.TakeAlongAxis(probs, sortedIndices, -1)
-	cumProbs := mlx.Cumsum(sortedProbs, -1)
-	mlx.Eval(sortedIndices, sortedProbs, cumProbs)
-
-	// Find cutoff index where cumulative probability exceeds topP
-	probsData := sortedProbs.Data()
-	cumProbsData := cumProbs.Data()
-	indicesData := sortedIndices.DataInt32()
-
-	// Calculate cutoff and renormalize
-	var cutoffIdx int
-	var totalProb float32
-	for i, cp := range cumProbsData {
-		totalProb += probsData[i]
-		if cp >= topP {
-			cutoffIdx = i + 1 // Include this token
-			break
-		}
-	}
-	if cutoffIdx == 0 {
-		cutoffIdx = len(probsData) // Use all tokens if topP is very high
-	}
-
-	// Sample from the truncated distribution
-	// Renormalize the truncated probabilities
-	truncatedProbs := make([]float32, cutoffIdx)
-	for i := 0; i < cutoffIdx; i++ {
-		truncatedProbs[i] = probsData[i] / totalProb
-	}
-
-	// Sample using random number with provided seed for reproducibility
-	r := mlx.RandomUniform([]int32{1}, uint64(seed))
-	mlx.Eval(r)
-	randVal := r.Data()[0]
-
-	// Find the sampled token
-	var cumulative float32
-	for i := 0; i < cutoffIdx; i++ {
-		cumulative += truncatedProbs[i]
-		if randVal < cumulative {
-			return indicesData[i]
-		}
-	}
-
-	// Fallback to the last token in truncated set
-	return indicesData[cutoffIdx-1]
-}
-
-// Forward for GLMBlock
-func (b *GLMBlock) Forward(x *mlx.Array, seqLen int32, eps float32) *mlx.Array {
-	return b.ForwardWithCache(x, seqLen, 0, eps, nil, nil)
-}
-
-// ForwardWithCache performs block forward with optional KV caching and MRoPE
-// positionIDs: [3][L] - position indices for MRoPE (nil = use sequential positions)
-func (b *GLMBlock) ForwardWithCache(x *mlx.Array, seqLen int32, posOffset int32, eps float32, kvcache cache.Cache, positionIDs [][]int32) *mlx.Array {
-	// Pre-attention norm
-	normed := b.InputLayerNorm.Forward(x, eps)
-
-	// Self-attention with RoPE/MRoPE and cache
-	attnOut := b.SelfAttn.ForwardWithCache(normed, seqLen, posOffset, kvcache, positionIDs)
-
-	// Post-attention norm (GLM-4 style)
-	attnOut = b.PostSelfAttnNorm.Forward(attnOut, eps)
-
-	// Residual connection
-	x = mlx.Add(x, attnOut)
-
-	// Post-attention layer norm
-	normed = b.PostAttnLayerNorm.Forward(x, eps)
-
-	// MLP
-	mlpOut := b.MLP.Forward(normed)
-
-	// Post-MLP norm
-	mlpOut = b.PostMLPLayerNorm.Forward(mlpOut, eps)
-
-	// Residual connection
-	x = mlx.Add(x, mlpOut)
-
-	return x
-}
-
-// Forward for GLMAttention (without cache - used for prefill)
-func (attn *GLMAttention) Forward(x *mlx.Array, seqLen int32) *mlx.Array {
-	return attn.ForwardWithCache(x, seqLen, 0, nil, nil)
-}
-
-// ForwardWithCache performs attention with optional KV caching and MRoPE
-// posOffset is the position offset for RoPE (0 for prefill, cached_len for decode)
-// positionIDs: [3][L] - if nil, uses sequential positions for all dims (text mode)
-// kvcache is updated in-place if provided
-func (attn *GLMAttention) ForwardWithCache(x *mlx.Array, seqLen int32, posOffset int32, kvcache cache.Cache, positionIDs [][]int32) *mlx.Array {
-	shape := x.Shape()
-	B := shape[0]
-	L := shape[1]
-
-	// Q, K, V projections
-	q := mlx.Matmul(x, mlx.Transpose(attn.QProj, 1, 0))
-	k := mlx.Matmul(x, mlx.Transpose(attn.KProj, 1, 0))
-	v := mlx.Matmul(x, mlx.Transpose(attn.VProj, 1, 0))
-
-	// Add biases
-	if attn.QBias != nil {
-		q = mlx.Add(q, attn.QBias)
-	}
-	if attn.KBias != nil {
-		k = mlx.Add(k, attn.KBias)
-	}
-	if attn.VBias != nil {
-		v = mlx.Add(v, attn.VBias)
-	}
-
-	// Reshape to [B, L, nheads, head_dim]
-	q = mlx.Reshape(q, B, L, attn.NHeads, attn.HeadDim)
-	k = mlx.Reshape(k, B, L, attn.NKVHeads, attn.HeadDim)
-	v = mlx.Reshape(v, B, L, attn.NKVHeads, attn.HeadDim)
-
-	// Apply partial RoPE or MRoPE
-	rotaryDim := int32(float32(attn.HeadDim) * attn.PartialRotary)
-	if len(attn.MRoPESection) == 3 && positionIDs != nil {
-		// Use MRoPE with explicit position IDs
-		q = applyPartialMRoPE(q, positionIDs, rotaryDim, attn.RopeTheta, attn.MRoPESection)
-		k = applyPartialMRoPE(k, positionIDs, rotaryDim, attn.RopeTheta, attn.MRoPESection)
-	} else if len(attn.MRoPESection) == 3 {
-		// Use MRoPE with sequential positions (same for all dims - text mode)
-		seqPositions := make([][]int32, 3)
-		for dim := 0; dim < 3; dim++ {
-			seqPositions[dim] = make([]int32, L)
-			for i := int32(0); i < L; i++ {
-				seqPositions[dim][i] = i + posOffset
-			}
-		}
-		q = applyPartialMRoPE(q, seqPositions, rotaryDim, attn.RopeTheta, attn.MRoPESection)
-		k = applyPartialMRoPE(k, seqPositions, rotaryDim, attn.RopeTheta, attn.MRoPESection)
-	} else {
-		// Fallback to standard RoPE
-		q = applyPartialRoPEWithOffset(q, L, posOffset, rotaryDim, attn.RopeTheta)
-		k = applyPartialRoPEWithOffset(k, L, posOffset, rotaryDim, attn.RopeTheta)
-	}
-
-	// Transpose to [B, nheads, L, head_dim]
-	q = mlx.Transpose(q, 0, 2, 1, 3)
-	k = mlx.Transpose(k, 0, 2, 1, 3)
-	v = mlx.Transpose(v, 0, 2, 1, 3)
-
-	// Update cache and get full K, V for attention
-	if kvcache != nil {
-		k, v = kvcache.Update(k, v, int(L))
-	}
-
-	// Repeat KV for GQA
-	kExpanded := k
-	vExpanded := v
-	if attn.NKVHeads < attn.NHeads {
-		repeats := attn.NHeads / attn.NKVHeads
-		kExpanded = repeatKV(k, repeats)
-		vExpanded = repeatKV(v, repeats)
-	}
-
-	// Scaled dot-product attention with causal mask
-	out := mlx.ScaledDotProductAttention(q, kExpanded, vExpanded, attn.Scale, true)
-
-	// Transpose back [B, nheads, L, head_dim] -> [B, L, nheads, head_dim]
-	out = mlx.Transpose(out, 0, 2, 1, 3)
-	// Reshape to [B, L, hidden_size]
-	out = mlx.Reshape(out, B, L, attn.NHeads*attn.HeadDim)
-
-	// Output projection
-	out = mlx.Matmul(out, mlx.Transpose(attn.OProj, 1, 0))
-
-	return out
-}
-
-// applyPartialRoPE applies RoPE to only the first rotaryDim dimensions
-func applyPartialRoPE(x *mlx.Array, seqLen int32, rotaryDim int32, theta float32) *mlx.Array {
-	return applyPartialRoPEWithOffset(x, seqLen, 0, rotaryDim, theta)
-}
-
-// applyPartialRoPEWithOffset applies RoPE with a position offset
-func applyPartialRoPEWithOffset(x *mlx.Array, seqLen int32, posOffset int32, rotaryDim int32, theta float32) *mlx.Array {
-	shape := x.Shape()
-	B := shape[0]
-	L := shape[1]
-	H := shape[2]
-	D := shape[3]
-
-	if rotaryDim <= 0 || rotaryDim > D {
-		rotaryDim = D
-	}
-
-	// Split into rotary and pass-through parts
-	xRot := mlx.Slice(x, []int32{0, 0, 0, 0}, []int32{B, L, H, rotaryDim})
-	xPass := mlx.Slice(x, []int32{0, 0, 0, rotaryDim}, []int32{B, L, H, D})
-
-	// Apply RoPE to rotary part with position offset
-	xRot = applyRoPEWithOffset(xRot, L, posOffset, theta)
-
-	// Concatenate back
-	return mlx.Concatenate([]*mlx.Array{xRot, xPass}, 3)
-}
-
-// applyPartialMRoPE applies Multi-dimensional RoPE (MRoPE) to the first rotaryDim dimensions
-// positionIDs: [3, L] - position indices for each dimension (temporal, height, width)
-// mrope_section: [8, 12, 12] - frequency pairs per dimension
-// For text tokens: all 3 dimensions have the same sequential position
-// For image tokens: temporal=seq_idx, height=row, width=col
-func applyPartialMRoPE(x *mlx.Array, positionIDs [][]int32, rotaryDim int32, theta float32, mropeSection []int32) *mlx.Array {
-	shape := x.Shape()
-	B := shape[0]
-	L := shape[1]
-	H := shape[2]
-	D := shape[3]
-
-	if rotaryDim <= 0 || rotaryDim > D {
-		rotaryDim = D
-	}
-
-	// Split into rotary and pass-through parts
-	xRot := mlx.Slice(x, []int32{0, 0, 0, 0}, []int32{B, L, H, rotaryDim})
-	xPass := mlx.Slice(x, []int32{0, 0, 0, rotaryDim}, []int32{B, L, H, D})
-
-	// Apply MRoPE to rotary part
-	xRot = applyMRoPE(xRot, positionIDs, theta, mropeSection)
-
-	// Concatenate back
-	return mlx.Concatenate([]*mlx.Array{xRot, xPass}, 3)
-}
-
-// applyMRoPE applies multi-dimensional rotary position embedding
-// x: [B, L, H, D] where D is the rotary dimension
-// positionIDs: [3][L] - positions for temporal, height, width dimensions
-// mropeSection: [8, 12, 12] - frequency pairs per dimension
-func applyMRoPE(x *mlx.Array, positionIDs [][]int32, theta float32, mropeSection []int32) *mlx.Array {
-	shape := x.Shape()
-	B := shape[0]
-	L := shape[1]
-	H := shape[2]
-	D := shape[3]
-	half := D / 2
-
-	// Validate mrope_section sums to half (number of frequency pairs)
-	var totalPairs int32
-	for _, s := range mropeSection {
-		totalPairs += s
-	}
-	if totalPairs != half {
-		// Fallback to standard RoPE if section doesn't match
-		return applyRoPEWithOffset(x, L, 0, theta)
-	}
-
-	// Build angles for each position dimension (matching Python's MRoPE approach)
-	// Python: compute freqs for all dims, then apply_mrope selects freq ranges, then duplicate
-	// Order: [temporal_8, height_12, width_12] -> duplicate -> [t8, h12, w12, t8, h12, w12]
-	angleVals := make([]*mlx.Array, 3)
-
-	freqOffset := int32(0)
-	for dim := 0; dim < 3; dim++ {
-		numPairs := mropeSection[dim]
-		if numPairs == 0 {
-			continue
-		}
-
-		// Compute inverse frequencies for this section
-		// Each dimension uses DIFFERENT frequency ranges:
-		// - Temporal: frequencies 0 to section[0]-1
-		// - Height: frequencies section[0] to section[0]+section[1]-1
-		// - Width: frequencies section[0]+section[1] to sum(section)-1
-		freqsArr := make([]float32, numPairs)
-		for i := int32(0); i < numPairs; i++ {
-			globalIdx := freqOffset + i
-			freqsArr[i] = float32(1.0 / math.Pow(float64(theta), float64(2*globalIdx)/float64(D)))
-		}
-		freqs := mlx.NewArray(freqsArr, []int32{numPairs})
-
-		// Position indices for this dimension
-		posArr := make([]float32, L)
-		for i := int32(0); i < L; i++ {
-			posArr[i] = float32(positionIDs[dim][i])
-		}
-		pos := mlx.NewArray(posArr, []int32{L})
-
-		// Compute angles: [L, numPairs] = outer(pos, freqs)
-		posExpanded := mlx.Reshape(pos, L, 1)
-		freqsExpanded := mlx.Reshape(freqs, 1, numPairs)
-		angleVals[dim] = mlx.Mul(posExpanded, freqsExpanded)
-
-		freqOffset += numPairs
-	}
-
-	// Concatenate all sections: [L, half] = [L, 32]
-	allAngles := mlx.Concatenate(angleVals, 1)
-
-	// Duplicate AFTER concatenation: [L, D] = [L, 64]
-	// This gives: [temporal_8, height_12, width_12, temporal_8, height_12, width_12]
-	allAngles = mlx.Concatenate([]*mlx.Array{allAngles, allAngles}, 1)
-
-	// Compute cos/sin
-	allCos := mlx.Cos(allAngles)
-	allSin := mlx.Sin(allAngles)
-
-	// Reshape for broadcasting: [1, L, 1, D] to match x [B, L, H, D]
-	allCos = mlx.Reshape(allCos, 1, L, 1, D)
-	allSin = mlx.Reshape(allSin, 1, L, 1, D)
-
-	// x_rotated = cat([-x_imag, x_real], dim=-1)
-	x1 := mlx.Slice(x, []int32{0, 0, 0, 0}, []int32{B, L, H, half})  // x_real
-	x2 := mlx.Slice(x, []int32{0, 0, 0, half}, []int32{B, L, H, D})  // x_imag
-	x2Neg := mlx.MulScalar(x2, -1)                                   // -x_imag
-	xRotated := mlx.Concatenate([]*mlx.Array{x2Neg, x1}, 3)          // [-x_imag, x_real]
-
-	// out = x * cos + x_rotated * sin
-	return mlx.Add(mlx.Mul(x, allCos), mlx.Mul(xRotated, allSin))
-}
-
-// applyRoPE applies rotary position embedding
-func applyRoPE(x *mlx.Array, seqLen int32, theta float32) *mlx.Array {
-	return applyRoPEWithOffset(x, seqLen, 0, theta)
-}
-
-// applyRoPEWithOffset applies rotary position embedding with position offset
-// Uses the split-half approach (matches diffusers GLM-Image with use_real_unbind_dim=-2)
-func applyRoPEWithOffset(x *mlx.Array, seqLen int32, posOffset int32, theta float32) *mlx.Array {
-	shape := x.Shape()
-	B := shape[0]
-	L := shape[1]
-	H := shape[2]
-	D := shape[3]
-	half := D / 2
-
-	// Compute inverse frequencies: 1 / (theta^(2i/d))
-	freqsArr := make([]float32, half)
-	for i := int32(0); i < half; i++ {
-		freqsArr[i] = float32(1.0 / math.Pow(float64(theta), float64(2*i)/float64(D)))
-	}
-	freqs := mlx.NewArray(freqsArr, []int32{half})
-
-	// Position indices with offset
-	posArr := make([]float32, L)
-	for i := int32(0); i < L; i++ {
-		posArr[i] = float32(i + posOffset)
-	}
-	pos := mlx.NewArray(posArr, []int32{L})
-
-	// Compute angles: [L, half] = outer(pos, freqs)
-	posExpanded := mlx.Reshape(pos, L, 1)
-	freqsExpanded := mlx.Reshape(freqs, 1, half)
-	angles := mlx.Mul(posExpanded, freqsExpanded)
-
-	// Duplicate angles to match diffusers: cat([angles, angles], dim=-1) -> [L, D]
-	anglesDup := mlx.Concatenate([]*mlx.Array{angles, angles}, 1)
-
-	// Cos and sin: [L, 1, D] for broadcasting to [B, L, H, D]
-	cosVals := mlx.Cos(anglesDup)
-	sinVals := mlx.Sin(anglesDup)
-	cosVals = mlx.Reshape(cosVals, L, 1, D)
-	sinVals = mlx.Reshape(sinVals, L, 1, D)
-
-	// x_rotated = cat([-x_imag, x_real], dim=-1) where x_real=x[..., :half], x_imag=x[..., half:]
-	x1 := mlx.Slice(x, []int32{0, 0, 0, 0}, []int32{B, L, H, half})      // x_real
-	x2 := mlx.Slice(x, []int32{0, 0, 0, half}, []int32{B, L, H, D})      // x_imag
-	x2Neg := mlx.MulScalar(x2, -1)                                       // -x_imag
-	xRotated := mlx.Concatenate([]*mlx.Array{x2Neg, x1}, 3)              // [-x_imag, x_real]
-
-	// out = x * cos + x_rotated * sin
-	return mlx.Add(mlx.Mul(x, cosVals), mlx.Mul(xRotated, sinVals))
-}
-
-// repeatKV repeats key/value heads for GQA
-func repeatKV(x *mlx.Array, repeats int32) *mlx.Array {
-	if repeats == 1 {
-		return x
-	}
-	shape := x.Shape()
-	// x: [B, nkvheads, L, head_dim]
-	x = mlx.ExpandDims(x, 2)
-	// x: [B, nkvheads, 1, L, head_dim]
-	x = mlx.Tile(x, []int32{1, 1, repeats, 1, 1})
-	// x: [B, nkvheads, repeats, L, head_dim]
-	return mlx.Reshape(x, shape[0], shape[1]*repeats, shape[2], shape[3])
-}
-
-// Forward for GLMMLP (fused gate_up SwiGLU)
-func (m *GLMMLP) Forward(x *mlx.Array) *mlx.Array {
-	// gate_up_proj outputs [gate, up] concatenated
-	gateUp := mlx.Matmul(x, mlx.Transpose(m.GateUpProj, 1, 0))
-
-	shape := gateUp.Shape()
-	halfDim := shape[len(shape)-1] / 2
-
-	// Split into gate and up
-	gate := mlx.Slice(gateUp, []int32{0, 0, 0}, []int32{shape[0], shape[1], halfDim})
-	up := mlx.Slice(gateUp, []int32{0, 0, halfDim}, []int32{shape[0], shape[1], shape[2]})
-
-	// SwiGLU: silu(gate) * up
-	gate = mlx.SiLU(gate)
-	h := mlx.Mul(gate, up)
-
-	// Down projection
-	return mlx.Matmul(h, mlx.Transpose(m.DownProj, 1, 0))
-}

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

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/zimage.go

@@ -194,7 +194,7 @@ 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

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/quantize.go

@@ -1,22 +0,0 @@
-package imagegen
-
-import (
-	"io"
-	"strings"
-)
-
-// QuantizingTensorLayerCreator creates tensor layers with optional quantization.
-// When quantize is true, returns multiple layers (weight + scales + biases).
-type QuantizingTensorLayerCreator func(r io.Reader, name, dtype string, shape []int32, quantize bool) ([]LayerInfo, error)
-
-// ShouldQuantize returns true if a tensor should be quantized.
-// Quantizes linear weights only, skipping VAE, embeddings, norms, and biases.
-func ShouldQuantize(name, component string) bool {
-	if component == "vae" {
-		return false
-	}
-	if strings.Contains(name, "embed") || strings.Contains(name, "norm") {
-		return false
-	}
-	return strings.HasSuffix(name, ".weight")
-}

x/imagegen/runner/runner.go

@@ -19,15 +19,9 @@ import (
 
 	"github.com/ollama/ollama/x/imagegen"
 	"github.com/ollama/ollama/x/imagegen/mlx"
-	"github.com/ollama/ollama/x/imagegen/models/glm_image"
 	"github.com/ollama/ollama/x/imagegen/models/zimage"
 )
 
-// ImageModel is the interface for image generation models
-type ImageModel interface {
-	GenerateImage(ctx context.Context, prompt string, width, height int32, steps int, seed int64) (*mlx.Array, error)
-}
-
 // Request is the image generation request format
 type Request struct {
 	Prompt string `json:"prompt"`
@@ -42,14 +36,15 @@ type Response struct {
 	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
 type Server struct {
 	mu        sync.Mutex
-	model     ImageModel
+	model     *zimage.Model
 	modelName string
-	modelType string // "zimage" or "glm_image"
 }
 
 // Execute is the entry point for the image runner subprocess
@@ -69,6 +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
@@ -79,35 +80,15 @@ func Execute(args []string) error {
 			requiredMemory/(1024*1024*1024), availableMemory/(1024*1024*1024))
 	}
 
-	// Detect model type and load appropriate model
-	modelType, err := detectModelType(*modelName)
-	if err != nil {
-		return fmt.Errorf("failed to detect model type: %w", err)
-	}
-
-	var model ImageModel
-	switch modelType {
-	case "GlmImagePipeline":
-		slog.Info("loading GLM-Image model")
-		m := &glm_image.Model{}
-		if err := m.Load(*modelName); err != nil {
-			return fmt.Errorf("failed to load GLM-Image model: %w", err)
-		}
-		model = m
-	default:
-		// Default to zimage for ZImagePipeline, FluxPipeline, and unknown types
-		slog.Info("loading Z-Image model")
-		m := &zimage.Model{}
-		if err := m.Load(*modelName); err != nil {
-			return fmt.Errorf("failed to load Z-Image model: %w", err)
-		}
-		model = m
+	// Load model
+	model := &zimage.Model{}
+	if err := model.Load(*modelName); err != nil {
+		return fmt.Errorf("failed to load model: %w", err)
 	}
 
 	server := &Server{
 		model:     model,
 		modelName: *modelName,
-		modelType: modelType,
 	}
 
 	// Set up HTTP handlers
@@ -163,22 +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 {
-		// Default steps depend on model type
-		switch s.modelType {
-		case "GlmImagePipeline":
-			req.Steps = 50 // GLM-Image default
-		default:
-			req.Steps = 9 // Z-Image turbo default
-		}
-	}
+	// 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()
 	}
@@ -192,9 +159,26 @@ func (s *Server) completionHandler(w http.ResponseWriter, r *http.Request) {
 		return
 	}
 
-	// Generate image using interface method
+	// Generate image
 	ctx := r.Context()
-	img, err := s.model.GenerateImage(ctx, req.Prompt, req.Width, req.Height, req.Steps, req.Seed)
+	img, err := s.model.GenerateFromConfig(ctx, &zimage.GenerateConfig{
+		Prompt: req.Prompt,
+		Width:  req.Width,
+		Height: req.Height,
+		Steps:  req.Steps,
+		Seed:   req.Seed,
+		Progress: func(step, total int) {
+			resp := Response{
+				Step:  step,
+				Total: total,
+				Done:  false,
+			}
+			data, _ := json.Marshal(resp)
+			w.Write(data)
+			w.Write([]byte("\n"))
+			flusher.Flush()
+		},
+	})
 
 	if err != nil {
 		// Don't send error for cancellation
@@ -233,35 +217,3 @@ func (s *Server) completionHandler(w http.ResponseWriter, r *http.Request) {
 	w.Write([]byte("\n"))
 	flusher.Flush()
 }
-
-// detectModelType reads the model manifest and returns the pipeline class name
-func detectModelType(modelName string) (string, error) {
-	manifest, err := imagegen.LoadManifest(modelName)
-	if err != nil {
-		return "", err
-	}
-
-	data, err := manifest.ReadConfig("model_index.json")
-	if err != nil {
-		return "ZImagePipeline", nil // Default to Z-Image
-	}
-
-	// Try both _class_name (diffusers format) and architecture (ollama format)
-	var index struct {
-		ClassName    string `json:"_class_name"`
-		Architecture string `json:"architecture"`
-	}
-	if err := json.Unmarshal(data, &index); err != nil {
-		return "ZImagePipeline", nil
-	}
-
-	// Prefer _class_name, fall back to architecture
-	className := index.ClassName
-	if className == "" {
-		className = index.Architecture
-	}
-	if className == "" {
-		return "ZImagePipeline", nil
-	}
-	return className, nil
-}

x/imagegen/server.go

@@ -25,6 +25,11 @@ import (
 )
 
 // Server wraps an image generation subprocess to implement llm.LlamaServer.
+//
+// This implementation is compatible with Ollama's scheduler and can be loaded/unloaded
+// like any other model. The plan is to eventually bring this into the llm/ package
+// and evolve llm/ to support MLX and multimodal models. For now, keeping the code
+// separate allows for independent iteration on image generation support.
 type Server struct {
 	mu          sync.Mutex
 	cmd         *exec.Cmd
@@ -37,22 +42,6 @@ type Server struct {
 	lastErrLock sync.Mutex
 }
 
-// completionRequest is sent to the subprocess
-type completionRequest struct {
-	Prompt string `json:"prompt"`
-	Width  int32  `json:"width,omitempty"`
-	Height int32  `json:"height,omitempty"`
-	Steps  int    `json:"steps,omitempty"`
-	Seed   int64  `json:"seed,omitempty"`
-}
-
-// completionResponse is received from the subprocess
-type completionResponse struct {
-	Content string `json:"content,omitempty"`
-	Image   string `json:"image,omitempty"`
-	Done    bool   `json:"done"`
-}
-
 // NewServer spawns a new image generation subprocess and waits until it's ready.
 func NewServer(modelName string) (*Server, error) {
 	// Validate platform support before attempting to start
@@ -72,7 +61,7 @@ func NewServer(modelName string) (*Server, error) {
 		port = rand.Intn(65535-49152) + 49152
 	}
 
-	// Get the ollama-mlx executable path (in same directory as current executable)
+	// Get the current executable path (we use the same binary with runner subcommand)
 	exe, err := os.Executable()
 	if err != nil {
 		return nil, fmt.Errorf("unable to lookup executable path: %w", err)
@@ -80,10 +69,9 @@ func NewServer(modelName string) (*Server, error) {
 	if eval, err := filepath.EvalSymlinks(exe); err == nil {
 		exe = eval
 	}
-	mlxExe := filepath.Join(filepath.Dir(exe), "ollama-mlx")
 
-	// Spawn subprocess: ollama-mlx runner --image-engine --model <path> --port <port>
-	cmd := exec.Command(mlxExe, "runner", "--image-engine", "--model", modelName, "--port", strconv.Itoa(port))
+	// Spawn subprocess: ollama runner --image-engine --model <path> --port <port>
+	cmd := exec.Command(exe, "runner", "--image-engine", "--model", modelName, "--port", strconv.Itoa(port))
 	cmd.Env = os.Environ()
 
 	// On Linux, set LD_LIBRARY_PATH to include MLX library directories
@@ -139,14 +127,13 @@ func NewServer(modelName string) (*Server, error) {
 		for scanner.Scan() {
 			line := scanner.Text()
 			slog.Warn("image-runner", "msg", line)
-			// Capture last error line for better error reporting
 			s.lastErrLock.Lock()
 			s.lastErr = line
 			s.lastErrLock.Unlock()
 		}
 	}()
 
-	slog.Info("starting ollama-mlx image runner subprocess", "exe", mlxExe, "model", modelName, "port", port)
+	slog.Info("starting image runner subprocess", "exe", exe, "model", modelName, "port", port)
 	if err := cmd.Start(); err != nil {
 		return nil, fmt.Errorf("failed to start image runner: %w", err)
 	}
@@ -171,7 +158,6 @@ func (s *Server) ModelPath() string {
 	return s.modelName
 }
 
-// Load is called by the scheduler after the server is created.
 func (s *Server) Load(ctx context.Context, systemInfo ml.SystemInfo, gpus []ml.DeviceInfo, requireFull bool) ([]ml.DeviceID, error) {
 	return nil, nil
 }
@@ -204,20 +190,16 @@ func (s *Server) waitUntilRunning() error {
 	for {
 		select {
 		case err := <-s.done:
-			// Include last stderr line for better error context
-			s.lastErrLock.Lock()
-			lastErr := s.lastErr
-			s.lastErrLock.Unlock()
-			if lastErr != "" {
-				return fmt.Errorf("image runner failed: %s (exit: %v)", lastErr, err)
+			// Include recent stderr lines for better error context
+			errMsg := s.getLastErr()
+			if errMsg != "" {
+				return fmt.Errorf("image runner failed: %s (exit: %v)", errMsg, err)
 			}
 			return fmt.Errorf("image runner exited unexpectedly: %w", err)
 		case <-timeout:
-			s.lastErrLock.Lock()
-			lastErr := s.lastErr
-			s.lastErrLock.Unlock()
-			if lastErr != "" {
-				return fmt.Errorf("timeout waiting for image runner: %s", lastErr)
+			errMsg := s.getLastErr()
+			if errMsg != "" {
+				return fmt.Errorf("timeout waiting for image runner: %s", errMsg)
 			}
 			return errors.New("timeout waiting for image runner to start")
 		case <-ticker.C:
@@ -229,44 +211,41 @@ func (s *Server) waitUntilRunning() error {
 	}
 }
 
-// WaitUntilRunning implements the LlamaServer interface (no-op since NewServer waits).
-func (s *Server) WaitUntilRunning(ctx context.Context) error {
-	return nil
+// getLastErr returns the last stderr line.
+func (s *Server) getLastErr() string {
+	s.lastErrLock.Lock()
+	defer s.lastErrLock.Unlock()
+	return s.lastErr
 }
 
-// Completion generates an image from the prompt via the subprocess.
+func (s *Server) WaitUntilRunning(ctx context.Context) error { return nil }
+
 func (s *Server) Completion(ctx context.Context, req llm.CompletionRequest, fn func(llm.CompletionResponse)) error {
-	// Build request
-	creq := completionRequest{
+	seed := req.Seed
+	if seed == 0 {
+		seed = time.Now().UnixNano()
+	}
+
+	// Build request for subprocess
+	creq := struct {
+		Prompt string `json:"prompt"`
+		Width  int32  `json:"width,omitempty"`
+		Height int32  `json:"height,omitempty"`
+		Steps  int32  `json:"steps,omitempty"`
+		Seed   int64  `json:"seed,omitempty"`
+	}{
 		Prompt: req.Prompt,
-		Width:  1024,
-		Height: 1024,
-		Steps:  9,
-		Seed:   time.Now().UnixNano(),
+		Width:  req.Width,
+		Height: req.Height,
+		Steps:  req.Steps,
+		Seed:   seed,
 	}
 
-	if req.Options != nil {
-		if req.Options.NumCtx > 0 && req.Options.NumCtx <= 4096 {
-			creq.Width = int32(req.Options.NumCtx)
-		}
-		if req.Options.NumGPU > 0 && req.Options.NumGPU <= 4096 {
-			creq.Height = int32(req.Options.NumGPU)
-		}
-		if req.Options.NumPredict > 0 && req.Options.NumPredict <= 100 {
-			creq.Steps = req.Options.NumPredict
-		}
-		if req.Options.Seed > 0 {
-			creq.Seed = int64(req.Options.Seed)
-		}
-	}
-
-	// Encode request body
 	body, err := json.Marshal(creq)
 	if err != nil {
 		return err
 	}
 
-	// Send request to subprocess
 	url := fmt.Sprintf("http://127.0.0.1:%d/completion", s.port)
 	httpReq, err := http.NewRequestWithContext(ctx, "POST", url, bytes.NewReader(body))
 	if err != nil {
@@ -281,30 +260,36 @@ func (s *Server) Completion(ctx context.Context, req llm.CompletionRequest, fn f
 	defer resp.Body.Close()
 
 	if resp.StatusCode != http.StatusOK {
-		return fmt.Errorf("completion request failed: %d", resp.StatusCode)
+		return fmt.Errorf("request failed: %d", resp.StatusCode)
 	}
 
-	// Stream responses - use large buffer for base64 image data
 	scanner := bufio.NewScanner(resp.Body)
 	scanner.Buffer(make([]byte, 1024*1024), 16*1024*1024) // 16MB max
 	for scanner.Scan() {
-		var cresp completionResponse
-		if err := json.Unmarshal(scanner.Bytes(), &cresp); err != nil {
+		// Parse subprocess response (has singular "image" field)
+		var raw struct {
+			Image   string `json:"image,omitempty"`
+			Content string `json:"content,omitempty"`
+			Done    bool   `json:"done"`
+			Step    int    `json:"step,omitempty"`
+			Total   int    `json:"total,omitempty"`
+		}
+		if err := json.Unmarshal(scanner.Bytes(), &raw); err != nil {
 			continue
 		}
 
-		content := cresp.Content
-		// If this is the final response with an image, encode it in the content
-		if cresp.Done && cresp.Image != "" {
-			content = "IMAGE_BASE64:" + cresp.Image
+		// Convert to llm.CompletionResponse
+		cresp := llm.CompletionResponse{
+			Content:    raw.Content,
+			Done:       raw.Done,
+			Step:       raw.Step,
+			TotalSteps: raw.Total,
+			Image:      raw.Image,
 		}
 
-		fn(llm.CompletionResponse{
-			Content: content,
-			Done:    cresp.Done,
-		})
+		fn(cresp)
 		if cresp.Done {
-			break
+			return nil
 		}
 	}
 
@@ -346,22 +331,18 @@ func (s *Server) VRAMByGPU(id ml.DeviceID) uint64 {
 	return s.vramSize
 }
 
-// Embedding is not supported for image generation models.
 func (s *Server) Embedding(ctx context.Context, input string) ([]float32, int, error) {
-	return nil, 0, errors.New("embedding not supported for image generation models")
+	return nil, 0, errors.New("not supported")
 }
 
-// Tokenize is not supported for image generation models.
 func (s *Server) Tokenize(ctx context.Context, content string) ([]int, error) {
-	return nil, errors.New("tokenize not supported for image generation models")
+	return nil, errors.New("not supported")
 }
 
-// Detokenize is not supported for image generation models.
 func (s *Server) Detokenize(ctx context.Context, tokens []int) (string, error) {
-	return "", errors.New("detokenize not supported for image generation models")
+	return "", errors.New("not supported")
 }
 
-// Pid returns the subprocess PID.
 func (s *Server) Pid() int {
 	s.mu.Lock()
 	defer s.mu.Unlock()
@@ -371,17 +352,9 @@ func (s *Server) Pid() int {
 	return -1
 }
 
-// GetPort returns the subprocess port.
-func (s *Server) GetPort() int {
-	return s.port
-}
+func (s *Server) GetPort() int                                       { return s.port }
+func (s *Server) GetDeviceInfos(ctx context.Context) []ml.DeviceInfo { return nil }
 
-// GetDeviceInfos returns nil since we don't track GPU info.
-func (s *Server) GetDeviceInfos(ctx context.Context) []ml.DeviceInfo {
-	return nil
-}
-
-// HasExited returns true if the subprocess has exited.
 func (s *Server) HasExited() bool {
 	select {
 	case <-s.done:

x/ml/backend/mlx/CMakeLists.txt

@@ -1,5 +1,9 @@
 include(FetchContent)
 
+# Read MLX version from top-level file (shared with Dockerfile)
+file(READ "${CMAKE_SOURCE_DIR}/MLX_VERSION" MLX_C_GIT_TAG)
+string(STRIP "${MLX_C_GIT_TAG}" MLX_C_GIT_TAG)
+
 set(MLX_C_BUILD_EXAMPLES OFF)
 
 set(MLX_BUILD_GGUF OFF)
@@ -50,7 +54,7 @@ endif()
 FetchContent_Declare(
   mlx-c
   GIT_REPOSITORY "https://github.com/ml-explore/mlx-c.git"
-  GIT_TAG v0.4.1)
+  GIT_TAG ${MLX_C_GIT_TAG})
 FetchContent_MakeAvailable(mlx-c)
 
 set_target_output_directory(mlx)

x/ml/backend/mlx/mlx_dynamic.c

@@ -0,0 +1,92 @@
+// 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"
+static const char* LIB_NAMES[] = {"libmlxc.dll", NULL};
+#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__
+static const char* LIB_NAMES[] = {
+    "libmlxc.dylib",
+    "@loader_path/../build/lib/ollama/libmlxc.dylib",
+    "@executable_path/../build/lib/ollama/libmlxc.dylib",
+    "build/lib/ollama/libmlxc.dylib",
+    "../build/lib/ollama/libmlxc.dylib",
+    NULL
+};
+#else
+static const char* LIB_NAMES[] = {
+    "libmlxc.so",
+    "$ORIGIN/../build/lib/ollama/libmlxc.so",
+    "build/lib/ollama/libmlxc.so",
+    "../build/lib/ollama/libmlxc.so",
+    NULL
+};
+#endif
+#endif
+
+static lib_handle_t mlx_handle = NULL;
+static int mlx_initialized = 0;
+static char mlx_error_buffer[512] = {0};
+
+// 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
+    }
+
+    // Try each possible library path
+    for (int i = 0; LIB_NAMES[i] != NULL; i++) {
+        mlx_handle = LOAD_LIB(LIB_NAMES[i]);
+        if (mlx_handle != NULL) {
+            mlx_initialized = 1;
+            snprintf(mlx_error_buffer, sizeof(mlx_error_buffer),
+                     "MLX: Successfully loaded %s", LIB_NAMES[i]);
+            return 0;
+        }
+    }
+
+    // Failed to load library
+    const char* err = LIB_ERROR();
+    snprintf(mlx_error_buffer, sizeof(mlx_error_buffer),
+             "MLX: Failed to load libmlxc library. %s",
+             err ? err : "Unknown error");
+    return -1;
+}
+
+// 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;
+}
+
+// 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/ml/backend/mlx/mlx_dynamic.h

@@ -0,0 +1,26 @@
+// 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);
+
+// Cleanup resources (optional, for clean shutdown)
+void mlx_dynamic_cleanup(void);
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif // MLX_DYNAMIC_H

x/server/show.go

@@ -0,0 +1,284 @@
+package server
+
+import (
+	"encoding/binary"
+	"encoding/json"
+	"fmt"
+	"io"
+	"os"
+	"strings"
+
+	"github.com/ollama/ollama/api"
+	"github.com/ollama/ollama/x/imagegen"
+)
+
+// modelConfig represents the HuggingFace config.json structure
+type modelConfig struct {
+	Architectures         []string `json:"architectures"`
+	ModelType             string   `json:"model_type"`
+	HiddenSize            int      `json:"hidden_size"`
+	NumHiddenLayers       int      `json:"num_hidden_layers"`
+	MaxPositionEmbeddings int      `json:"max_position_embeddings"`
+	IntermediateSize      int      `json:"intermediate_size"`
+	NumAttentionHeads     int      `json:"num_attention_heads"`
+	NumKeyValueHeads      int      `json:"num_key_value_heads"`
+	VocabSize             int      `json:"vocab_size"`
+	RMSNormEps            float64  `json:"rms_norm_eps"`
+	RopeTheta             float64  `json:"rope_theta"`
+	TorchDtype            string   `json:"torch_dtype"`
+	TextConfig            *struct {
+		HiddenSize            int `json:"hidden_size"`
+		MaxPositionEmbeddings int `json:"max_position_embeddings"`
+		NumHiddenLayers       int `json:"num_hidden_layers"`
+	} `json:"text_config"`
+}
+
+// GetSafetensorsLLMInfo extracts model information from safetensors LLM models.
+// It reads the config.json layer and returns a map compatible with GGML's KV format.
+func GetSafetensorsLLMInfo(modelName string) (map[string]any, error) {
+	manifest, err := imagegen.LoadManifest(modelName)
+	if err != nil {
+		return nil, fmt.Errorf("failed to load manifest: %w", err)
+	}
+
+	var config modelConfig
+	if err := manifest.ReadConfigJSON("config.json", &config); err != nil {
+		return nil, fmt.Errorf("failed to read config.json: %w", err)
+	}
+
+	// Calculate total tensor bytes from manifest layers
+	var totalBytes int64
+	var tensorCount int64
+	for _, layer := range manifest.Manifest.Layers {
+		if layer.MediaType == "application/vnd.ollama.image.tensor" {
+			totalBytes += layer.Size
+			tensorCount++
+		}
+	}
+
+	return buildModelInfo(config, totalBytes, tensorCount), nil
+}
+
+// buildModelInfo constructs the model info map from config and tensor stats.
+// This is separated for testability.
+func buildModelInfo(config modelConfig, totalTensorBytes, tensorCount int64) map[string]any {
+	// Determine architecture
+	arch := config.ModelType
+	if arch == "" && len(config.Architectures) > 0 {
+		// Convert HuggingFace architecture name to Ollama format
+		// e.g., "Gemma3ForCausalLM" -> "gemma3"
+		hfArch := config.Architectures[0]
+		arch = strings.ToLower(hfArch)
+		arch = strings.TrimSuffix(arch, "forcausallm")
+		arch = strings.TrimSuffix(arch, "forconditionalgeneration")
+	}
+
+	// Use text_config values if they exist (for multimodal models)
+	hiddenSize := config.HiddenSize
+	maxPosEmbed := config.MaxPositionEmbeddings
+	numLayers := config.NumHiddenLayers
+
+	if config.TextConfig != nil {
+		if config.TextConfig.HiddenSize > 0 {
+			hiddenSize = config.TextConfig.HiddenSize
+		}
+		if config.TextConfig.MaxPositionEmbeddings > 0 {
+			maxPosEmbed = config.TextConfig.MaxPositionEmbeddings
+		}
+		if config.TextConfig.NumHiddenLayers > 0 {
+			numLayers = config.TextConfig.NumHiddenLayers
+		}
+	}
+
+	// Get dtype to determine bytes per parameter for count calculation
+	dtype := config.TorchDtype
+
+	// Determine bytes per parameter based on dtype
+	var bytesPerParam int64 = 2 // default to float16/bfloat16
+	switch strings.ToLower(dtype) {
+	case "float32":
+		bytesPerParam = 4
+	case "float16", "bfloat16":
+		bytesPerParam = 2
+	case "int8", "uint8":
+		bytesPerParam = 1
+	}
+
+	// Subtract safetensors header overhead (88 bytes per tensor file)
+	// Each tensor is stored as a minimal safetensors file
+	totalBytes := totalTensorBytes - tensorCount*88
+
+	paramCount := totalBytes / bytesPerParam
+
+	info := map[string]any{
+		"general.architecture": arch,
+	}
+
+	if maxPosEmbed > 0 {
+		info[fmt.Sprintf("%s.context_length", arch)] = maxPosEmbed
+	}
+
+	if hiddenSize > 0 {
+		info[fmt.Sprintf("%s.embedding_length", arch)] = hiddenSize
+	}
+
+	if numLayers > 0 {
+		info[fmt.Sprintf("%s.block_count", arch)] = numLayers
+	}
+
+	if config.NumAttentionHeads > 0 {
+		info[fmt.Sprintf("%s.attention.head_count", arch)] = config.NumAttentionHeads
+	}
+
+	if config.NumKeyValueHeads > 0 {
+		info[fmt.Sprintf("%s.attention.head_count_kv", arch)] = config.NumKeyValueHeads
+	}
+
+	if config.IntermediateSize > 0 {
+		info[fmt.Sprintf("%s.feed_forward_length", arch)] = config.IntermediateSize
+	}
+
+	if config.VocabSize > 0 {
+		info[fmt.Sprintf("%s.vocab_size", arch)] = config.VocabSize
+	}
+
+	if paramCount > 0 {
+		info["general.parameter_count"] = paramCount
+	}
+
+	return info
+}
+
+// GetSafetensorsTensorInfo extracts tensor information from safetensors model layers.
+// Each tensor is stored as a minimal safetensors file with an 88-byte header containing metadata.
+func GetSafetensorsTensorInfo(modelName string) ([]api.Tensor, error) {
+	manifest, err := imagegen.LoadManifest(modelName)
+	if err != nil {
+		return nil, fmt.Errorf("failed to load manifest: %w", err)
+	}
+
+	return getTensorInfoFromManifest(manifest)
+}
+
+// getTensorInfoFromManifest extracts tensor info from a manifest.
+// This is separated for testability.
+func getTensorInfoFromManifest(manifest *imagegen.ModelManifest) ([]api.Tensor, error) {
+	var tensors []api.Tensor
+
+	for _, layer := range manifest.Manifest.Layers {
+		if layer.MediaType != "application/vnd.ollama.image.tensor" {
+			continue
+		}
+
+		// Read the safetensors header from the blob
+		blobPath := manifest.BlobPath(layer.Digest)
+		info, err := readSafetensorsHeader(blobPath)
+		if err != nil {
+			// Skip tensors we can't read
+			continue
+		}
+
+		// Convert shape from int to uint64
+		shape := make([]uint64, len(info.Shape))
+		for i, s := range info.Shape {
+			shape[i] = uint64(s)
+		}
+
+		tensors = append(tensors, api.Tensor{
+			Name:  layer.Name,
+			Type:  info.Dtype,
+			Shape: shape,
+		})
+	}
+
+	return tensors, nil
+}
+
+// GetSafetensorsDtype returns the quantization type for a safetensors model.
+// If the model is quantized (has _scale tensors), returns the quantization type (e.g., "FP8").
+// Otherwise returns the torch_dtype from config.json.
+func GetSafetensorsDtype(modelName string) (string, error) {
+	manifest, err := imagegen.LoadManifest(modelName)
+	if err != nil {
+		return "", fmt.Errorf("failed to load manifest: %w", err)
+	}
+
+	// Check if model is quantized by looking for _scale tensors
+	for _, layer := range manifest.Manifest.Layers {
+		if layer.MediaType == "application/vnd.ollama.image.tensor" {
+			if strings.HasSuffix(layer.Name, "_scale") {
+				// Model is quantized - return FP8 (affine quantization)
+				return "FP8", nil
+			}
+		}
+	}
+
+	// Not quantized - return torch_dtype from config.json
+	var cfg struct {
+		TorchDtype string `json:"torch_dtype"`
+	}
+	if err := manifest.ReadConfigJSON("config.json", &cfg); err != nil {
+		return "", fmt.Errorf("failed to read config.json: %w", err)
+	}
+
+	return cfg.TorchDtype, nil
+}
+
+// safetensorsTensorInfo holds metadata about a tensor from a safetensors header
+type safetensorsTensorInfo struct {
+	Dtype string  `json:"dtype"`
+	Shape []int64 `json:"shape"`
+}
+
+// readSafetensorsHeader reads the JSON header from a safetensors file to get tensor metadata.
+// Safetensors format: 8-byte header size (little endian) + JSON header + tensor data
+func readSafetensorsHeader(path string) (*safetensorsTensorInfo, error) {
+	f, err := os.Open(path)
+	if err != nil {
+		return nil, err
+	}
+	defer f.Close()
+
+	return parseSafetensorsHeader(f)
+}
+
+// parseSafetensorsHeader parses a safetensors header from a reader.
+// This is separated for testability.
+func parseSafetensorsHeader(r io.Reader) (*safetensorsTensorInfo, error) {
+	// Read header size (8 bytes, little endian)
+	var headerSize uint64
+	if err := binary.Read(r, binary.LittleEndian, &headerSize); err != nil {
+		return nil, fmt.Errorf("failed to read header size: %w", err)
+	}
+
+	// Sanity check - header shouldn't be too large
+	if headerSize > 1024*1024 {
+		return nil, fmt.Errorf("header size too large: %d", headerSize)
+	}
+
+	// Read header JSON
+	headerBytes := make([]byte, headerSize)
+	if _, err := io.ReadFull(r, headerBytes); err != nil {
+		return nil, fmt.Errorf("failed to read header: %w", err)
+	}
+
+	// Parse as map of tensor name -> info
+	var header map[string]json.RawMessage
+	if err := json.Unmarshal(headerBytes, &header); err != nil {
+		return nil, fmt.Errorf("failed to parse header: %w", err)
+	}
+
+	// Find the first (and should be only) tensor entry
+	for name, raw := range header {
+		if name == "__metadata__" {
+			continue
+		}
+		var info safetensorsTensorInfo
+		if err := json.Unmarshal(raw, &info); err != nil {
+			return nil, fmt.Errorf("failed to parse tensor info: %w", err)
+		}
+		return &info, nil
+	}
+
+	return nil, fmt.Errorf("no tensor found in header")
+}

x/server/show_test.go

@@ -0,0 +1,597 @@
+package server
+
+import (
+	"bytes"
+	"encoding/binary"
+	"encoding/json"
+	"os"
+	"path/filepath"
+	"testing"
+
+	"github.com/ollama/ollama/x/imagegen"
+)
+
+func TestBuildModelInfo(t *testing.T) {
+	tests := []struct {
+		name             string
+		config           modelConfig
+		totalTensorBytes int64
+		tensorCount      int64
+		wantArch         string
+		wantContextLen   int
+		wantEmbedLen     int
+		wantBlockCount   int
+		wantParamCount   int64
+	}{
+		{
+			name: "gemma3 model with model_type",
+			config: modelConfig{
+				ModelType:             "gemma3",
+				HiddenSize:            2560,
+				NumHiddenLayers:       34,
+				MaxPositionEmbeddings: 131072,
+				IntermediateSize:      10240,
+				NumAttentionHeads:     8,
+				NumKeyValueHeads:      4,
+				VocabSize:             262144,
+				TorchDtype:            "bfloat16",
+			},
+			totalTensorBytes: 8_600_000_088, // ~4.3B params * 2 bytes + 88 bytes header
+			tensorCount:      1,
+			wantArch:         "gemma3",
+			wantContextLen:   131072,
+			wantEmbedLen:     2560,
+			wantBlockCount:   34,
+			wantParamCount:   4_300_000_000,
+		},
+		{
+			name: "llama model with architectures array",
+			config: modelConfig{
+				Architectures:         []string{"LlamaForCausalLM"},
+				HiddenSize:            4096,
+				NumHiddenLayers:       32,
+				MaxPositionEmbeddings: 4096,
+				IntermediateSize:      11008,
+				NumAttentionHeads:     32,
+				NumKeyValueHeads:      32,
+				VocabSize:             32000,
+				TorchDtype:            "float16",
+			},
+			totalTensorBytes: 14_000_000_088, // ~7B params * 2 bytes + 88 bytes header
+			tensorCount:      1,
+			wantArch:         "llama",
+			wantContextLen:   4096,
+			wantEmbedLen:     4096,
+			wantBlockCount:   32,
+			wantParamCount:   7_000_000_000,
+		},
+		{
+			name: "multimodal model with text_config",
+			config: modelConfig{
+				Architectures: []string{"Gemma3ForConditionalGeneration"},
+				HiddenSize:    1152, // vision hidden size
+				TextConfig: &struct {
+					HiddenSize            int `json:"hidden_size"`
+					MaxPositionEmbeddings int `json:"max_position_embeddings"`
+					NumHiddenLayers       int `json:"num_hidden_layers"`
+				}{
+					HiddenSize:            2560,
+					MaxPositionEmbeddings: 131072,
+					NumHiddenLayers:       34,
+				},
+				NumAttentionHeads: 8,
+				NumKeyValueHeads:  4,
+				VocabSize:         262144,
+				TorchDtype:        "bfloat16",
+			},
+			totalTensorBytes: 8_600_000_088,
+			tensorCount:      1,
+			wantArch:         "gemma3",
+			wantContextLen:   131072,
+			wantEmbedLen:     2560,
+			wantBlockCount:   34,
+			wantParamCount:   4_300_000_000,
+		},
+		{
+			name: "float32 model",
+			config: modelConfig{
+				ModelType:             "test",
+				HiddenSize:            512,
+				NumHiddenLayers:       6,
+				MaxPositionEmbeddings: 2048,
+				TorchDtype:            "float32",
+			},
+			totalTensorBytes: 400_000_088, // 100M params * 4 bytes + 88 bytes header
+			tensorCount:      1,
+			wantArch:         "test",
+			wantContextLen:   2048,
+			wantEmbedLen:     512,
+			wantBlockCount:   6,
+			wantParamCount:   100_000_000,
+		},
+		{
+			name: "multiple tensors with header overhead",
+			config: modelConfig{
+				ModelType:             "test",
+				HiddenSize:            256,
+				NumHiddenLayers:       4,
+				MaxPositionEmbeddings: 1024,
+				TorchDtype:            "bfloat16",
+			},
+			totalTensorBytes: 2_000_880, // 1M params * 2 bytes + 10 tensors * 88 bytes
+			tensorCount:      10,
+			wantArch:         "test",
+			wantContextLen:   1024,
+			wantEmbedLen:     256,
+			wantBlockCount:   4,
+			wantParamCount:   1_000_000,
+		},
+	}
+
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			info := buildModelInfo(tt.config, tt.totalTensorBytes, tt.tensorCount)
+
+			// Check architecture
+			if arch, ok := info["general.architecture"].(string); !ok || arch != tt.wantArch {
+				t.Errorf("architecture = %v, want %v", info["general.architecture"], tt.wantArch)
+			}
+
+			// Check context length
+			contextKey := tt.wantArch + ".context_length"
+			if contextLen, ok := info[contextKey].(int); !ok || contextLen != tt.wantContextLen {
+				t.Errorf("context_length = %v, want %v", info[contextKey], tt.wantContextLen)
+			}
+
+			// Check embedding length
+			embedKey := tt.wantArch + ".embedding_length"
+			if embedLen, ok := info[embedKey].(int); !ok || embedLen != tt.wantEmbedLen {
+				t.Errorf("embedding_length = %v, want %v", info[embedKey], tt.wantEmbedLen)
+			}
+
+			// Check block count
+			blockKey := tt.wantArch + ".block_count"
+			if blockCount, ok := info[blockKey].(int); !ok || blockCount != tt.wantBlockCount {
+				t.Errorf("block_count = %v, want %v", info[blockKey], tt.wantBlockCount)
+			}
+
+			// Check parameter count
+			if paramCount, ok := info["general.parameter_count"].(int64); !ok || paramCount != tt.wantParamCount {
+				t.Errorf("parameter_count = %v, want %v", info["general.parameter_count"], tt.wantParamCount)
+			}
+		})
+	}
+}
+
+func TestBuildModelInfo_ArchitectureConversion(t *testing.T) {
+	tests := []struct {
+		name          string
+		architectures []string
+		modelType     string
+		wantArch      string
+	}{
+		{
+			name:          "LlamaForCausalLM",
+			architectures: []string{"LlamaForCausalLM"},
+			wantArch:      "llama",
+		},
+		{
+			name:          "Gemma3ForCausalLM",
+			architectures: []string{"Gemma3ForCausalLM"},
+			wantArch:      "gemma3",
+		},
+		{
+			name:          "Gemma3ForConditionalGeneration",
+			architectures: []string{"Gemma3ForConditionalGeneration"},
+			wantArch:      "gemma3",
+		},
+		{
+			name:          "Qwen2ForCausalLM",
+			architectures: []string{"Qwen2ForCausalLM"},
+			wantArch:      "qwen2",
+		},
+		{
+			name:          "model_type takes precedence",
+			architectures: []string{"LlamaForCausalLM"},
+			modelType:     "custom",
+			wantArch:      "custom",
+		},
+		{
+			name:          "empty architectures with model_type",
+			architectures: nil,
+			modelType:     "mymodel",
+			wantArch:      "mymodel",
+		},
+	}
+
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			config := modelConfig{
+				Architectures: tt.architectures,
+				ModelType:     tt.modelType,
+			}
+			info := buildModelInfo(config, 0, 0)
+
+			if arch, ok := info["general.architecture"].(string); !ok || arch != tt.wantArch {
+				t.Errorf("architecture = %v, want %v", info["general.architecture"], tt.wantArch)
+			}
+		})
+	}
+}
+
+func TestBuildModelInfo_BytesPerParam(t *testing.T) {
+	tests := []struct {
+		name           string
+		dtype          string
+		totalBytes     int64
+		tensorCount    int64
+		wantParamCount int64
+	}{
+		{
+			name:           "bfloat16",
+			dtype:          "bfloat16",
+			totalBytes:     2_000_088, // 1M * 2 + 88
+			tensorCount:    1,
+			wantParamCount: 1_000_000,
+		},
+		{
+			name:           "float16",
+			dtype:          "float16",
+			totalBytes:     2_000_088,
+			tensorCount:    1,
+			wantParamCount: 1_000_000,
+		},
+		{
+			name:           "float32",
+			dtype:          "float32",
+			totalBytes:     4_000_088, // 1M * 4 + 88
+			tensorCount:    1,
+			wantParamCount: 1_000_000,
+		},
+		{
+			name:           "int8",
+			dtype:          "int8",
+			totalBytes:     1_000_088, // 1M * 1 + 88
+			tensorCount:    1,
+			wantParamCount: 1_000_000,
+		},
+		{
+			name:           "unknown dtype defaults to 2 bytes",
+			dtype:          "unknown",
+			totalBytes:     2_000_088,
+			tensorCount:    1,
+			wantParamCount: 1_000_000,
+		},
+		{
+			name:           "empty dtype defaults to 2 bytes",
+			dtype:          "",
+			totalBytes:     2_000_088,
+			tensorCount:    1,
+			wantParamCount: 1_000_000,
+		},
+	}
+
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			config := modelConfig{
+				ModelType:  "test",
+				TorchDtype: tt.dtype,
+			}
+			info := buildModelInfo(config, tt.totalBytes, tt.tensorCount)
+
+			if paramCount, ok := info["general.parameter_count"].(int64); !ok || paramCount != tt.wantParamCount {
+				t.Errorf("parameter_count = %v, want %v", info["general.parameter_count"], tt.wantParamCount)
+			}
+		})
+	}
+}
+
+func TestParseSafetensorsHeader(t *testing.T) {
+	tests := []struct {
+		name      string
+		header    map[string]any
+		wantDtype string
+		wantShape []int64
+		wantErr   bool
+	}{
+		{
+			name: "simple tensor",
+			header: map[string]any{
+				"weight": map[string]any{
+					"dtype":        "BF16",
+					"shape":        []int64{2560, 262144},
+					"data_offsets": []int64{0, 1342177280},
+				},
+			},
+			wantDtype: "BF16",
+			wantShape: []int64{2560, 262144},
+		},
+		{
+			name: "with metadata",
+			header: map[string]any{
+				"__metadata__": map[string]any{
+					"format": "pt",
+				},
+				"bias": map[string]any{
+					"dtype":        "F32",
+					"shape":        []int64{1024},
+					"data_offsets": []int64{0, 4096},
+				},
+			},
+			wantDtype: "F32",
+			wantShape: []int64{1024},
+		},
+		{
+			name: "float16 tensor",
+			header: map[string]any{
+				"layer.weight": map[string]any{
+					"dtype":        "F16",
+					"shape":        []int64{512, 512, 3, 3},
+					"data_offsets": []int64{0, 4718592},
+				},
+			},
+			wantDtype: "F16",
+			wantShape: []int64{512, 512, 3, 3},
+		},
+	}
+
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			// Create safetensors format: 8-byte size + JSON header
+			headerJSON, err := json.Marshal(tt.header)
+			if err != nil {
+				t.Fatalf("failed to marshal header: %v", err)
+			}
+
+			var buf bytes.Buffer
+			if err := binary.Write(&buf, binary.LittleEndian, uint64(len(headerJSON))); err != nil {
+				t.Fatalf("failed to write header size: %v", err)
+			}
+			buf.Write(headerJSON)
+
+			info, err := parseSafetensorsHeader(&buf)
+			if (err != nil) != tt.wantErr {
+				t.Errorf("parseSafetensorsHeader() error = %v, wantErr %v", err, tt.wantErr)
+				return
+			}
+			if tt.wantErr {
+				return
+			}
+
+			if info.Dtype != tt.wantDtype {
+				t.Errorf("Dtype = %v, want %v", info.Dtype, tt.wantDtype)
+			}
+
+			if len(info.Shape) != len(tt.wantShape) {
+				t.Errorf("Shape length = %v, want %v", len(info.Shape), len(tt.wantShape))
+			} else {
+				for i, s := range info.Shape {
+					if s != tt.wantShape[i] {
+						t.Errorf("Shape[%d] = %v, want %v", i, s, tt.wantShape[i])
+					}
+				}
+			}
+		})
+	}
+}
+
+func TestParseSafetensorsHeader_Errors(t *testing.T) {
+	tests := []struct {
+		name    string
+		data    []byte
+		wantErr string
+	}{
+		{
+			name:    "empty data",
+			data:    []byte{},
+			wantErr: "failed to read header size",
+		},
+		{
+			name:    "truncated header size",
+			data:    []byte{0x01, 0x02, 0x03},
+			wantErr: "failed to read header size",
+		},
+		{
+			name: "header size too large",
+			data: func() []byte {
+				var buf bytes.Buffer
+				binary.Write(&buf, binary.LittleEndian, uint64(2*1024*1024)) // 2MB
+				return buf.Bytes()
+			}(),
+			wantErr: "header size too large",
+		},
+		{
+			name: "truncated header",
+			data: func() []byte {
+				var buf bytes.Buffer
+				binary.Write(&buf, binary.LittleEndian, uint64(100))
+				buf.Write([]byte("short"))
+				return buf.Bytes()
+			}(),
+			wantErr: "failed to read header",
+		},
+		{
+			name: "invalid JSON",
+			data: func() []byte {
+				var buf bytes.Buffer
+				binary.Write(&buf, binary.LittleEndian, uint64(10))
+				buf.Write([]byte("not json!!"))
+				return buf.Bytes()
+			}(),
+			wantErr: "failed to parse header",
+		},
+		{
+			name: "no tensors in header",
+			data: func() []byte {
+				header := map[string]any{
+					"__metadata__": map[string]any{"format": "pt"},
+				}
+				headerJSON, _ := json.Marshal(header)
+				var buf bytes.Buffer
+				binary.Write(&buf, binary.LittleEndian, uint64(len(headerJSON)))
+				buf.Write(headerJSON)
+				return buf.Bytes()
+			}(),
+			wantErr: "no tensor found in header",
+		},
+	}
+
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			_, err := parseSafetensorsHeader(bytes.NewReader(tt.data))
+			if err == nil {
+				t.Error("expected error, got nil")
+				return
+			}
+			if !bytes.Contains([]byte(err.Error()), []byte(tt.wantErr)) {
+				t.Errorf("error = %v, want error containing %v", err, tt.wantErr)
+			}
+		})
+	}
+}
+
+func TestGetTensorInfoFromManifest(t *testing.T) {
+	// Create a temp directory for blobs
+	tempDir := t.TempDir()
+
+	// Create test tensor blobs
+	tensors := []struct {
+		name   string
+		digest string
+		dtype  string
+		shape  []int64
+	}{
+		{
+			name:   "model.embed_tokens.weight",
+			digest: "sha256:abc123",
+			dtype:  "BF16",
+			shape:  []int64{262144, 2560},
+		},
+		{
+			name:   "model.layers.0.self_attn.q_proj.weight",
+			digest: "sha256:def456",
+			dtype:  "BF16",
+			shape:  []int64{2560, 2560},
+		},
+		{
+			name:   "model.norm.weight",
+			digest: "sha256:ghi789",
+			dtype:  "F32",
+			shape:  []int64{2560},
+		},
+	}
+
+	// Create blob files
+	var layers []imagegen.ManifestLayer
+	for _, tensor := range tensors {
+		// Create safetensors blob
+		header := map[string]any{
+			tensor.name: map[string]any{
+				"dtype":        tensor.dtype,
+				"shape":        tensor.shape,
+				"data_offsets": []int64{0, 1000},
+			},
+		}
+		headerJSON, _ := json.Marshal(header)
+
+		var buf bytes.Buffer
+		binary.Write(&buf, binary.LittleEndian, uint64(len(headerJSON)))
+		buf.Write(headerJSON)
+
+		// Write blob file
+		blobName := "sha256-" + tensor.digest[7:]
+		blobPath := filepath.Join(tempDir, blobName)
+		if err := os.WriteFile(blobPath, buf.Bytes(), 0o644); err != nil {
+			t.Fatalf("failed to write blob: %v", err)
+		}
+
+		layers = append(layers, imagegen.ManifestLayer{
+			MediaType: "application/vnd.ollama.image.tensor",
+			Digest:    tensor.digest,
+			Size:      int64(buf.Len() + 1000), // header + fake data
+			Name:      tensor.name,
+		})
+	}
+
+	// Add a non-tensor layer (should be skipped)
+	layers = append(layers, imagegen.ManifestLayer{
+		MediaType: "application/vnd.ollama.image.json",
+		Digest:    "sha256:config",
+		Size:      100,
+		Name:      "config.json",
+	})
+
+	manifest := &imagegen.ModelManifest{
+		Manifest: &imagegen.Manifest{
+			Layers: layers,
+		},
+		BlobDir: tempDir,
+	}
+
+	result, err := getTensorInfoFromManifest(manifest)
+	if err != nil {
+		t.Fatalf("getTensorInfoFromManifest() error = %v", err)
+	}
+
+	if len(result) != 3 {
+		t.Errorf("got %d tensors, want 3", len(result))
+	}
+
+	// Verify each tensor
+	for i, tensor := range tensors {
+		if i >= len(result) {
+			break
+		}
+		if result[i].Name != tensor.name {
+			t.Errorf("tensor[%d].Name = %v, want %v", i, result[i].Name, tensor.name)
+		}
+		if result[i].Type != tensor.dtype {
+			t.Errorf("tensor[%d].Type = %v, want %v", i, result[i].Type, tensor.dtype)
+		}
+		if len(result[i].Shape) != len(tensor.shape) {
+			t.Errorf("tensor[%d].Shape length = %v, want %v", i, len(result[i].Shape), len(tensor.shape))
+		}
+	}
+}
+
+func TestReadSafetensorsHeader(t *testing.T) {
+	// Create a temp file with a valid safetensors header
+	tempDir := t.TempDir()
+
+	header := map[string]any{
+		"test_tensor": map[string]any{
+			"dtype":        "BF16",
+			"shape":        []int64{1024, 768},
+			"data_offsets": []int64{0, 1572864},
+		},
+	}
+	headerJSON, _ := json.Marshal(header)
+
+	var buf bytes.Buffer
+	binary.Write(&buf, binary.LittleEndian, uint64(len(headerJSON)))
+	buf.Write(headerJSON)
+
+	filePath := filepath.Join(tempDir, "test.safetensors")
+	if err := os.WriteFile(filePath, buf.Bytes(), 0o644); err != nil {
+		t.Fatalf("failed to write test file: %v", err)
+	}
+
+	info, err := readSafetensorsHeader(filePath)
+	if err != nil {
+		t.Fatalf("readSafetensorsHeader() error = %v", err)
+	}
+
+	if info.Dtype != "BF16" {
+		t.Errorf("Dtype = %v, want BF16", info.Dtype)
+	}
+	if len(info.Shape) != 2 || info.Shape[0] != 1024 || info.Shape[1] != 768 {
+		t.Errorf("Shape = %v, want [1024, 768]", info.Shape)
+	}
+}
+
+func TestReadSafetensorsHeader_FileNotFound(t *testing.T) {
+	_, err := readSafetensorsHeader("/nonexistent/path/file.safetensors")
+	if err == nil {
+		t.Error("expected error for nonexistent file")
+	}
+}