wip

Replace custom normalizePath function with stdlib path.Clean.
Use path.IsAbs and path.Dir for cleaner, more robust code.
Add sibling escape detection to prevent traversal attacks like
"tools/a/b/../../../etc" which normalizes to "etc" (a sibling).

.github/workflows/release.yaml

@@ -68,7 +68,6 @@ jobs:
           name: bundles-darwin
           path: |
             dist/*.tgz
-            dist/*.tar.zst
             dist/*.zip
             dist/*.dmg
 
@@ -393,13 +392,13 @@ jobs:
           done
       - run: |
           for ARCHIVE in dist/${{ matrix.os }}-${{ matrix.arch }}/*.tar.in; do
-            tar c -C dist/${{ matrix.os }}-${{ matrix.arch }} -T $ARCHIVE --owner 0 --group 0 | zstd --ultra -22 -T0 >$(basename ${ARCHIVE//.*/}.tar.zst);
+            tar c -C dist/${{ matrix.os }}-${{ matrix.arch }} -T $ARCHIVE --owner 0 --group 0 | pigz -9vc >$(basename ${ARCHIVE//.*/}.tgz);
           done
       - uses: actions/upload-artifact@v4
         with:
           name: bundles-${{ matrix.os }}-${{ matrix.arch }}-${{ matrix.target }}
           path: |
-            *.tar.zst
+            *.tgz
 
   # Build each Docker variant (OS, arch, and flavor) separately. Using QEMU is unreliable and slower.
   docker-build-push:
@@ -532,7 +531,7 @@ jobs:
       - name: Upload release artifacts
         run: |
           pids=()
-          for payload in dist/*.txt dist/*.zip dist/*.tgz dist/*.tar.zst dist/*.exe dist/*.dmg ; do
+          for payload in dist/*.txt dist/*.zip dist/*.tgz dist/*.exe dist/*.dmg ; do
             echo "Uploading $payload"
             gh release upload ${GITHUB_REF_NAME} $payload --clobber &
             pids[$!]=$!

CMakeLists.txt

@@ -2,22 +2,6 @@ cmake_minimum_required(VERSION 3.21)
 
 project(Ollama C CXX)
 
-# Handle cross-compilation on macOS: when CMAKE_OSX_ARCHITECTURES is set to a
-# single architecture different from the host, override CMAKE_SYSTEM_PROCESSOR
-# to match. This is necessary because CMAKE_SYSTEM_PROCESSOR defaults to the
-# host architecture, but downstream projects (like MLX) use it to detect the
-# target architecture.
-if(CMAKE_OSX_ARCHITECTURES AND NOT CMAKE_OSX_ARCHITECTURES MATCHES ";")
-    # Single architecture specified
-    if(CMAKE_OSX_ARCHITECTURES STREQUAL "x86_64" AND NOT CMAKE_SYSTEM_PROCESSOR STREQUAL "x86_64")
-        message(STATUS "Cross-compiling for x86_64: overriding CMAKE_SYSTEM_PROCESSOR from ${CMAKE_SYSTEM_PROCESSOR} to x86_64")
-        set(CMAKE_SYSTEM_PROCESSOR "x86_64")
-    elseif(CMAKE_OSX_ARCHITECTURES STREQUAL "arm64" AND NOT CMAKE_SYSTEM_PROCESSOR STREQUAL "arm64")
-        message(STATUS "Cross-compiling for arm64: overriding CMAKE_SYSTEM_PROCESSOR from ${CMAKE_SYSTEM_PROCESSOR} to arm64")
-        set(CMAKE_SYSTEM_PROCESSOR "arm64")
-    endif()
-endif()
-
 include(CheckLanguage)
 include(GNUInstallDirs)
 
@@ -28,7 +12,7 @@ set(BUILD_SHARED_LIBS ON)
 
 set(CMAKE_CXX_STANDARD 17)
 set(CMAKE_CXX_STANDARD_REQUIRED ON)
-set(CMAKE_CXX_EXTENSIONS ON) # Recent versions of MLX Requires gnu++17 extensions to compile properly
+set(CMAKE_CXX_EXTENSIONS OFF)
 
 set(GGML_BUILD ON)
 set(GGML_SHARED ON)
@@ -163,48 +147,14 @@ if(CMAKE_HIP_COMPILER)
     endif()
 endif()
 
-if(NOT APPLE)
-    find_package(Vulkan)
-    if(Vulkan_FOUND)
-        add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/ml/backend/ggml/ggml/src/ggml-vulkan)
-        install(TARGETS ggml-vulkan
-            RUNTIME_DEPENDENCIES
-                PRE_INCLUDE_REGEXES vulkan
-                PRE_EXCLUDE_REGEXES ".*"
-            RUNTIME DESTINATION ${OLLAMA_INSTALL_DIR} COMPONENT Vulkan
-            LIBRARY DESTINATION ${OLLAMA_INSTALL_DIR} COMPONENT Vulkan
-        )
-    endif()
-endif()
-
-option(MLX_ENGINE "Enable MLX backend" OFF)
-
-if(MLX_ENGINE)
-    message(STATUS "Setting up MLX (this takes a while...)")
-    add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/x/ml/backend/mlx)
-
-    # Find CUDA toolkit if MLX is built with CUDA support
-    find_package(CUDAToolkit)
-
-    install(TARGETS mlx mlxc
+find_package(Vulkan)
+if(Vulkan_FOUND)
+    add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/ml/backend/ggml/ggml/src/ggml-vulkan)
+    install(TARGETS ggml-vulkan
         RUNTIME_DEPENDENCIES
-            DIRECTORIES ${CUDAToolkit_BIN_DIR} ${CUDAToolkit_BIN_DIR}/x64 ${CUDAToolkit_LIBRARY_DIR}
-            PRE_INCLUDE_REGEXES cublas cublasLt cudart nvrtc cudnn nccl
+            PRE_INCLUDE_REGEXES vulkan
             PRE_EXCLUDE_REGEXES ".*"
-        RUNTIME DESTINATION ${OLLAMA_INSTALL_DIR} COMPONENT MLX
-        LIBRARY DESTINATION ${OLLAMA_INSTALL_DIR} COMPONENT MLX
-        FRAMEWORK DESTINATION ${OLLAMA_INSTALL_DIR} COMPONENT MLX
+        RUNTIME DESTINATION ${OLLAMA_INSTALL_DIR} COMPONENT Vulkan
+        LIBRARY DESTINATION ${OLLAMA_INSTALL_DIR} COMPONENT Vulkan
     )
-
-    # Manually install cudart and cublas since they might not be picked up as direct dependencies
-    if(CUDAToolkit_FOUND)
-        file(GLOB CUDART_LIBS
-            "${CUDAToolkit_LIBRARY_DIR}/libcudart.so*"
-            "${CUDAToolkit_LIBRARY_DIR}/libcublas.so*")
-        if(CUDART_LIBS)
-            install(FILES ${CUDART_LIBS}
-                DESTINATION ${OLLAMA_INSTALL_DIR}
-                COMPONENT MLX)
-        endif()
-    endif()
-endif()
+endif()

CMakePresets.json

@@ -41,7 +41,7 @@
       "inherits": [ "CUDA" ],
       "cacheVariables": {
         "CMAKE_CUDA_ARCHITECTURES": "75-virtual;80-virtual;86-virtual;87-virtual;89-virtual;90-virtual;90a-virtual;100-virtual;103-virtual;110-virtual;120-virtual;121-virtual",
-        "CMAKE_CUDA_FLAGS": "-t 4",
+        "CMAKE_CUDA_FLAGS": "-t 2",
         "OLLAMA_RUNNER_DIR": "cuda_v13"
       }
     },
@@ -83,28 +83,6 @@
       "cacheVariables": {
         "OLLAMA_RUNNER_DIR": "vulkan"
       }
-    },
-    {
-      "name": "MLX",
-      "inherits": [ "Default" ],
-      "cacheVariables": {
-        "MLX_ENGINE": "ON",
-        "OLLAMA_RUNNER_DIR": "mlx"
-      }
-    },
-    {
-      "name": "MLX CUDA 12",
-      "inherits": [ "MLX", "CUDA 12" ],
-      "cacheVariables": {
-        "OLLAMA_RUNNER_DIR": "mlx_cuda_v12"
-      }
-    },
-    {
-      "name": "MLX CUDA 13",
-      "inherits": [ "MLX", "CUDA 13" ],
-      "cacheVariables": {
-        "OLLAMA_RUNNER_DIR": "mlx_cuda_v13"
-      }
     }
   ],
   "buildPresets": [
@@ -162,21 +140,6 @@
       "name": "Vulkan",
       "targets": [ "ggml-vulkan" ],
       "configurePreset": "Vulkan"
-    },
-    {
-      "name": "MLX",
-      "targets": [ "mlx", "mlxc" ],
-      "configurePreset": "MLX"
-    },
-    {
-      "name": "MLX CUDA 12",
-      "targets": [ "mlx", "mlxc" ],
-      "configurePreset": "MLX CUDA 12"
-    },
-    {
-      "name": "MLX CUDA 13",
-      "targets": [ "mlx", "mlxc" ],
-      "configurePreset": "MLX CUDA 13"
     }
   ]
 }

Dockerfile

@@ -131,36 +131,8 @@ COPY ml/backend/ggml/ggml ml/backend/ggml/ggml
 RUN --mount=type=cache,target=/root/.ccache \
     cmake --preset 'Vulkan' \
         && cmake --build --parallel --preset 'Vulkan' \
-        && cmake --install build --component Vulkan --strip --parallel 8
+        && cmake --install build --component Vulkan --strip --parallel 8 
 
-FROM base AS mlx
-ARG CUDA13VERSION=13.0
-RUN dnf install -y cuda-toolkit-${CUDA13VERSION//./-} \
-    && dnf install -y openblas-devel lapack-devel \
-    && dnf install -y libcudnn9-cuda-13 libcudnn9-devel-cuda-13 \
-    && dnf install -y libnccl libnccl-devel
-ENV PATH=/usr/local/cuda-13/bin:$PATH
-ENV BLAS_INCLUDE_DIRS=/usr/include/openblas
-ENV LAPACK_INCLUDE_DIRS=/usr/include/openblas
-ENV CGO_LDFLAGS="-L/usr/local/cuda-13/lib64 -L/usr/local/cuda-13/targets/x86_64-linux/lib/stubs"
-ARG PARALLEL
-WORKDIR /go/src/github.com/ollama/ollama
-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 .
-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
-RUN --mount=type=cache,target=/root/.ccache \
-    cmake --preset 'MLX CUDA 13' -DBLAS_INCLUDE_DIRS=/usr/include/openblas -DLAPACK_INCLUDE_DIRS=/usr/include/openblas \
-        && cmake --build --parallel ${PARALLEL} --preset 'MLX CUDA 13' \
-        && cmake --install build --component MLX --strip --parallel ${PARALLEL}
-COPY . .
-ARG GOFLAGS="'-ldflags=-w -s'"
-ENV CGO_ENABLED=1
-ARG CGO_CFLAGS
-ARG CGO_CXXFLAGS
 
 FROM base AS build
 WORKDIR /go/src/github.com/ollama/ollama
@@ -181,7 +153,6 @@ FROM --platform=linux/amd64 scratch AS amd64
 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/
 
 FROM --platform=linux/arm64 scratch AS arm64
 # COPY --from=cuda-11 dist/lib/ollama/ /lib/ollama/
@@ -200,7 +171,7 @@ COPY --from=build /bin/ollama /bin/ollama
 
 FROM ubuntu:24.04
 RUN apt-get update \
-    && apt-get install -y ca-certificates libvulkan1 libopenblas0 \
+    && apt-get install -y ca-certificates libvulkan1 \
     && apt-get clean \
     && rm -rf /var/lib/apt/lists/*
 COPY --from=archive /bin /usr/bin

anthropic/anthropic.go

@@ -1,778 +0,0 @@
-package anthropic
-
-import (
-	"crypto/rand"
-	"encoding/base64"
-	"encoding/json"
-	"errors"
-	"fmt"
-	"log/slog"
-	"net/http"
-	"strings"
-	"time"
-
-	"github.com/ollama/ollama/api"
-)
-
-// Error types matching Anthropic API
-type Error struct {
-	Type    string `json:"type"`
-	Message string `json:"message"`
-}
-
-type ErrorResponse struct {
-	Type      string `json:"type"` // always "error"
-	Error     Error  `json:"error"`
-	RequestID string `json:"request_id,omitempty"`
-}
-
-// NewError creates a new ErrorResponse with the appropriate error type based on HTTP status code
-func NewError(code int, message string) ErrorResponse {
-	var etype string
-	switch code {
-	case http.StatusBadRequest:
-		etype = "invalid_request_error"
-	case http.StatusUnauthorized:
-		etype = "authentication_error"
-	case http.StatusForbidden:
-		etype = "permission_error"
-	case http.StatusNotFound:
-		etype = "not_found_error"
-	case http.StatusTooManyRequests:
-		etype = "rate_limit_error"
-	case http.StatusServiceUnavailable, 529:
-		etype = "overloaded_error"
-	default:
-		etype = "api_error"
-	}
-
-	return ErrorResponse{
-		Type:      "error",
-		Error:     Error{Type: etype, Message: message},
-		RequestID: generateID("req"),
-	}
-}
-
-// Request types
-
-// MessagesRequest represents an Anthropic Messages API request
-type MessagesRequest struct {
-	Model         string          `json:"model"`
-	MaxTokens     int             `json:"max_tokens"`
-	Messages      []MessageParam  `json:"messages"`
-	System        any             `json:"system,omitempty"` // string or []ContentBlock
-	Stream        bool            `json:"stream,omitempty"`
-	Temperature   *float64        `json:"temperature,omitempty"`
-	TopP          *float64        `json:"top_p,omitempty"`
-	TopK          *int            `json:"top_k,omitempty"`
-	StopSequences []string        `json:"stop_sequences,omitempty"`
-	Tools         []Tool          `json:"tools,omitempty"`
-	ToolChoice    *ToolChoice     `json:"tool_choice,omitempty"`
-	Thinking      *ThinkingConfig `json:"thinking,omitempty"`
-	Metadata      *Metadata       `json:"metadata,omitempty"`
-}
-
-// MessageParam represents a message in the request
-type MessageParam struct {
-	Role    string `json:"role"`    // "user" or "assistant"
-	Content any    `json:"content"` // string or []ContentBlock
-}
-
-// ContentBlock represents a content block in a message.
-// Text and Thinking use pointers so they serialize as the field being present (even if empty)
-// only when set, which is required for SDK streaming accumulation.
-type ContentBlock struct {
-	Type string `json:"type"` // text, image, tool_use, tool_result, thinking
-
-	// For text blocks - pointer so field only appears when set (SDK requires it for accumulation)
-	Text *string `json:"text,omitempty"`
-
-	// For image blocks
-	Source *ImageSource `json:"source,omitempty"`
-
-	// For tool_use blocks
-	ID    string `json:"id,omitempty"`
-	Name  string `json:"name,omitempty"`
-	Input any    `json:"input,omitempty"`
-
-	// For tool_result blocks
-	ToolUseID string `json:"tool_use_id,omitempty"`
-	Content   any    `json:"content,omitempty"` // string or []ContentBlock
-	IsError   bool   `json:"is_error,omitempty"`
-
-	// For thinking blocks - pointer so field only appears when set (SDK requires it for accumulation)
-	Thinking  *string `json:"thinking,omitempty"`
-	Signature string  `json:"signature,omitempty"`
-}
-
-// ImageSource represents the source of an image
-type ImageSource struct {
-	Type      string `json:"type"` // "base64" or "url"
-	MediaType string `json:"media_type,omitempty"`
-	Data      string `json:"data,omitempty"`
-	URL       string `json:"url,omitempty"`
-}
-
-// Tool represents a tool definition
-type Tool struct {
-	Type        string          `json:"type,omitempty"` // "custom" for user-defined tools
-	Name        string          `json:"name"`
-	Description string          `json:"description,omitempty"`
-	InputSchema json.RawMessage `json:"input_schema,omitempty"`
-}
-
-// ToolChoice controls how the model uses tools
-type ToolChoice struct {
-	Type                   string `json:"type"` // "auto", "any", "tool", "none"
-	Name                   string `json:"name,omitempty"`
-	DisableParallelToolUse bool   `json:"disable_parallel_tool_use,omitempty"`
-}
-
-// ThinkingConfig controls extended thinking
-type ThinkingConfig struct {
-	Type         string `json:"type"` // "enabled" or "disabled"
-	BudgetTokens int    `json:"budget_tokens,omitempty"`
-}
-
-// Metadata for the request
-type Metadata struct {
-	UserID string `json:"user_id,omitempty"`
-}
-
-// Response types
-
-// MessagesResponse represents an Anthropic Messages API response
-type MessagesResponse struct {
-	ID           string         `json:"id"`
-	Type         string         `json:"type"` // "message"
-	Role         string         `json:"role"` // "assistant"
-	Model        string         `json:"model"`
-	Content      []ContentBlock `json:"content"`
-	StopReason   string         `json:"stop_reason,omitempty"`
-	StopSequence string         `json:"stop_sequence,omitempty"`
-	Usage        Usage          `json:"usage"`
-}
-
-// Usage contains token usage information
-type Usage struct {
-	InputTokens  int `json:"input_tokens"`
-	OutputTokens int `json:"output_tokens"`
-}
-
-// Streaming event types
-
-// MessageStartEvent is sent at the start of streaming
-type MessageStartEvent struct {
-	Type    string           `json:"type"` // "message_start"
-	Message MessagesResponse `json:"message"`
-}
-
-// ContentBlockStartEvent signals the start of a content block
-type ContentBlockStartEvent struct {
-	Type         string       `json:"type"` // "content_block_start"
-	Index        int          `json:"index"`
-	ContentBlock ContentBlock `json:"content_block"`
-}
-
-// ContentBlockDeltaEvent contains incremental content updates
-type ContentBlockDeltaEvent struct {
-	Type  string `json:"type"` // "content_block_delta"
-	Index int    `json:"index"`
-	Delta Delta  `json:"delta"`
-}
-
-// Delta represents an incremental update
-type Delta struct {
-	Type        string `json:"type"` // "text_delta", "input_json_delta", "thinking_delta", "signature_delta"
-	Text        string `json:"text,omitempty"`
-	PartialJSON string `json:"partial_json,omitempty"`
-	Thinking    string `json:"thinking,omitempty"`
-	Signature   string `json:"signature,omitempty"`
-}
-
-// ContentBlockStopEvent signals the end of a content block
-type ContentBlockStopEvent struct {
-	Type  string `json:"type"` // "content_block_stop"
-	Index int    `json:"index"`
-}
-
-// MessageDeltaEvent contains updates to the message
-type MessageDeltaEvent struct {
-	Type  string       `json:"type"` // "message_delta"
-	Delta MessageDelta `json:"delta"`
-	Usage DeltaUsage   `json:"usage"`
-}
-
-// MessageDelta contains stop information
-type MessageDelta struct {
-	StopReason   string `json:"stop_reason,omitempty"`
-	StopSequence string `json:"stop_sequence,omitempty"`
-}
-
-// DeltaUsage contains cumulative token usage
-type DeltaUsage struct {
-	OutputTokens int `json:"output_tokens"`
-}
-
-// MessageStopEvent signals the end of the message
-type MessageStopEvent struct {
-	Type string `json:"type"` // "message_stop"
-}
-
-// PingEvent is a keepalive event
-type PingEvent struct {
-	Type string `json:"type"` // "ping"
-}
-
-// StreamErrorEvent is an error during streaming
-type StreamErrorEvent struct {
-	Type  string `json:"type"` // "error"
-	Error Error  `json:"error"`
-}
-
-// FromMessagesRequest converts an Anthropic MessagesRequest to an Ollama api.ChatRequest
-func FromMessagesRequest(r MessagesRequest) (*api.ChatRequest, error) {
-	var messages []api.Message
-
-	if r.System != nil {
-		switch sys := r.System.(type) {
-		case string:
-			if sys != "" {
-				messages = append(messages, api.Message{Role: "system", Content: sys})
-			}
-		case []any:
-			// System can be an array of content blocks
-			var content strings.Builder
-			for _, block := range sys {
-				if blockMap, ok := block.(map[string]any); ok {
-					if blockMap["type"] == "text" {
-						if text, ok := blockMap["text"].(string); ok {
-							content.WriteString(text)
-						}
-					}
-				}
-			}
-			if content.Len() > 0 {
-				messages = append(messages, api.Message{Role: "system", Content: content.String()})
-			}
-		}
-	}
-
-	for _, msg := range r.Messages {
-		converted, err := convertMessage(msg)
-		if err != nil {
-			return nil, err
-		}
-		messages = append(messages, converted...)
-	}
-
-	options := make(map[string]any)
-
-	options["num_predict"] = r.MaxTokens
-
-	if r.Temperature != nil {
-		options["temperature"] = *r.Temperature
-	}
-
-	if r.TopP != nil {
-		options["top_p"] = *r.TopP
-	}
-
-	if r.TopK != nil {
-		options["top_k"] = *r.TopK
-	}
-
-	if len(r.StopSequences) > 0 {
-		options["stop"] = r.StopSequences
-	}
-
-	var tools api.Tools
-	for _, t := range r.Tools {
-		tool, err := convertTool(t)
-		if err != nil {
-			return nil, err
-		}
-		tools = append(tools, tool)
-	}
-
-	var think *api.ThinkValue
-	if r.Thinking != nil && r.Thinking.Type == "enabled" {
-		think = &api.ThinkValue{Value: true}
-	}
-
-	stream := r.Stream
-
-	return &api.ChatRequest{
-		Model:    r.Model,
-		Messages: messages,
-		Options:  options,
-		Stream:   &stream,
-		Tools:    tools,
-		Think:    think,
-	}, nil
-}
-
-// convertMessage converts an Anthropic MessageParam to Ollama api.Message(s)
-func convertMessage(msg MessageParam) ([]api.Message, error) {
-	var messages []api.Message
-	role := strings.ToLower(msg.Role)
-
-	switch content := msg.Content.(type) {
-	case string:
-		messages = append(messages, api.Message{Role: role, Content: content})
-
-	case []any:
-		var textContent strings.Builder
-		var images []api.ImageData
-		var toolCalls []api.ToolCall
-		var thinking string
-		var toolResults []api.Message
-
-		for _, block := range content {
-			blockMap, ok := block.(map[string]any)
-			if !ok {
-				return nil, errors.New("invalid content block format")
-			}
-
-			blockType, _ := blockMap["type"].(string)
-
-			switch blockType {
-			case "text":
-				if text, ok := blockMap["text"].(string); ok {
-					textContent.WriteString(text)
-				}
-
-			case "image":
-				source, ok := blockMap["source"].(map[string]any)
-				if !ok {
-					return nil, errors.New("invalid image source")
-				}
-
-				sourceType, _ := source["type"].(string)
-				if sourceType == "base64" {
-					data, _ := source["data"].(string)
-					decoded, err := base64.StdEncoding.DecodeString(data)
-					if err != nil {
-						return nil, fmt.Errorf("invalid base64 image data: %w", err)
-					}
-					images = append(images, decoded)
-				} else {
-					return nil, fmt.Errorf("invalid image source type: %s. Only base64 images are supported.", sourceType)
-				}
-				// URL images would need to be fetched - skip for now
-
-			case "tool_use":
-				id, ok := blockMap["id"].(string)
-				if !ok {
-					return nil, errors.New("tool_use block missing required 'id' field")
-				}
-				name, ok := blockMap["name"].(string)
-				if !ok {
-					return nil, errors.New("tool_use block missing required 'name' field")
-				}
-				tc := api.ToolCall{
-					ID: id,
-					Function: api.ToolCallFunction{
-						Name: name,
-					},
-				}
-				if input, ok := blockMap["input"].(map[string]any); ok {
-					tc.Function.Arguments = mapToArgs(input)
-				}
-				toolCalls = append(toolCalls, tc)
-
-			case "tool_result":
-				toolUseID, _ := blockMap["tool_use_id"].(string)
-				var resultContent string
-
-				switch c := blockMap["content"].(type) {
-				case string:
-					resultContent = c
-				case []any:
-					for _, cb := range c {
-						if cbMap, ok := cb.(map[string]any); ok {
-							if cbMap["type"] == "text" {
-								if text, ok := cbMap["text"].(string); ok {
-									resultContent += text
-								}
-							}
-						}
-					}
-				}
-
-				toolResults = append(toolResults, api.Message{
-					Role:       "tool",
-					Content:    resultContent,
-					ToolCallID: toolUseID,
-				})
-
-			case "thinking":
-				if t, ok := blockMap["thinking"].(string); ok {
-					thinking = t
-				}
-			}
-		}
-
-		if textContent.Len() > 0 || len(images) > 0 || len(toolCalls) > 0 || thinking != "" {
-			m := api.Message{
-				Role:      role,
-				Content:   textContent.String(),
-				Images:    images,
-				ToolCalls: toolCalls,
-				Thinking:  thinking,
-			}
-			messages = append(messages, m)
-		}
-
-		// Add tool results as separate messages
-		messages = append(messages, toolResults...)
-
-	default:
-		return nil, fmt.Errorf("invalid message content type: %T", content)
-	}
-
-	return messages, nil
-}
-
-// convertTool converts an Anthropic Tool to an Ollama api.Tool
-func convertTool(t Tool) (api.Tool, error) {
-	var params api.ToolFunctionParameters
-	if len(t.InputSchema) > 0 {
-		if err := json.Unmarshal(t.InputSchema, &params); err != nil {
-			return api.Tool{}, fmt.Errorf("invalid input_schema for tool %q: %w", t.Name, err)
-		}
-	}
-
-	return api.Tool{
-		Type: "function",
-		Function: api.ToolFunction{
-			Name:        t.Name,
-			Description: t.Description,
-			Parameters:  params,
-		},
-	}, nil
-}
-
-// ToMessagesResponse converts an Ollama api.ChatResponse to an Anthropic MessagesResponse
-func ToMessagesResponse(id string, r api.ChatResponse) MessagesResponse {
-	var content []ContentBlock
-
-	if r.Message.Thinking != "" {
-		content = append(content, ContentBlock{
-			Type:     "thinking",
-			Thinking: ptr(r.Message.Thinking),
-		})
-	}
-
-	if r.Message.Content != "" {
-		content = append(content, ContentBlock{
-			Type: "text",
-			Text: ptr(r.Message.Content),
-		})
-	}
-
-	for _, tc := range r.Message.ToolCalls {
-		content = append(content, ContentBlock{
-			Type:  "tool_use",
-			ID:    tc.ID,
-			Name:  tc.Function.Name,
-			Input: tc.Function.Arguments,
-		})
-	}
-
-	stopReason := mapStopReason(r.DoneReason, len(r.Message.ToolCalls) > 0)
-
-	return MessagesResponse{
-		ID:         id,
-		Type:       "message",
-		Role:       "assistant",
-		Model:      r.Model,
-		Content:    content,
-		StopReason: stopReason,
-		Usage: Usage{
-			InputTokens:  r.Metrics.PromptEvalCount,
-			OutputTokens: r.Metrics.EvalCount,
-		},
-	}
-}
-
-// mapStopReason converts Ollama done_reason to Anthropic stop_reason
-func mapStopReason(reason string, hasToolCalls bool) string {
-	if hasToolCalls {
-		return "tool_use"
-	}
-
-	switch reason {
-	case "stop":
-		return "end_turn"
-	case "length":
-		return "max_tokens"
-	default:
-		if reason != "" {
-			return "stop_sequence"
-		}
-		return ""
-	}
-}
-
-// StreamConverter manages state for converting Ollama streaming responses to Anthropic format
-type StreamConverter struct {
-	ID              string
-	Model           string
-	firstWrite      bool
-	contentIndex    int
-	inputTokens     int
-	outputTokens    int
-	thinkingStarted bool
-	thinkingDone    bool
-	textStarted     bool
-	toolCallsSent   map[string]bool
-}
-
-func NewStreamConverter(id, model string) *StreamConverter {
-	return &StreamConverter{
-		ID:            id,
-		Model:         model,
-		firstWrite:    true,
-		toolCallsSent: make(map[string]bool),
-	}
-}
-
-// StreamEvent represents a streaming event to be sent to the client
-type StreamEvent struct {
-	Event string
-	Data  any
-}
-
-// Process converts an Ollama ChatResponse to Anthropic streaming events
-func (c *StreamConverter) Process(r api.ChatResponse) []StreamEvent {
-	var events []StreamEvent
-
-	if c.firstWrite {
-		c.firstWrite = false
-		c.inputTokens = r.Metrics.PromptEvalCount
-
-		events = append(events, StreamEvent{
-			Event: "message_start",
-			Data: MessageStartEvent{
-				Type: "message_start",
-				Message: MessagesResponse{
-					ID:      c.ID,
-					Type:    "message",
-					Role:    "assistant",
-					Model:   c.Model,
-					Content: []ContentBlock{},
-					Usage: Usage{
-						InputTokens:  c.inputTokens,
-						OutputTokens: 0,
-					},
-				},
-			},
-		})
-	}
-
-	if r.Message.Thinking != "" && !c.thinkingDone {
-		if !c.thinkingStarted {
-			c.thinkingStarted = true
-			events = append(events, StreamEvent{
-				Event: "content_block_start",
-				Data: ContentBlockStartEvent{
-					Type:  "content_block_start",
-					Index: c.contentIndex,
-					ContentBlock: ContentBlock{
-						Type:     "thinking",
-						Thinking: ptr(""),
-					},
-				},
-			})
-		}
-
-		events = append(events, StreamEvent{
-			Event: "content_block_delta",
-			Data: ContentBlockDeltaEvent{
-				Type:  "content_block_delta",
-				Index: c.contentIndex,
-				Delta: Delta{
-					Type:     "thinking_delta",
-					Thinking: r.Message.Thinking,
-				},
-			},
-		})
-	}
-
-	if r.Message.Content != "" {
-		if c.thinkingStarted && !c.thinkingDone {
-			c.thinkingDone = true
-			events = append(events, StreamEvent{
-				Event: "content_block_stop",
-				Data: ContentBlockStopEvent{
-					Type:  "content_block_stop",
-					Index: c.contentIndex,
-				},
-			})
-			c.contentIndex++
-		}
-
-		if !c.textStarted {
-			c.textStarted = true
-			events = append(events, StreamEvent{
-				Event: "content_block_start",
-				Data: ContentBlockStartEvent{
-					Type:  "content_block_start",
-					Index: c.contentIndex,
-					ContentBlock: ContentBlock{
-						Type: "text",
-						Text: ptr(""),
-					},
-				},
-			})
-		}
-
-		events = append(events, StreamEvent{
-			Event: "content_block_delta",
-			Data: ContentBlockDeltaEvent{
-				Type:  "content_block_delta",
-				Index: c.contentIndex,
-				Delta: Delta{
-					Type: "text_delta",
-					Text: r.Message.Content,
-				},
-			},
-		})
-	}
-
-	for _, tc := range r.Message.ToolCalls {
-		if c.toolCallsSent[tc.ID] {
-			continue
-		}
-
-		if c.textStarted {
-			events = append(events, StreamEvent{
-				Event: "content_block_stop",
-				Data: ContentBlockStopEvent{
-					Type:  "content_block_stop",
-					Index: c.contentIndex,
-				},
-			})
-			c.contentIndex++
-			c.textStarted = false
-		}
-
-		argsJSON, err := json.Marshal(tc.Function.Arguments)
-		if err != nil {
-			slog.Error("failed to marshal tool arguments", "error", err, "tool_id", tc.ID)
-			continue
-		}
-
-		events = append(events, StreamEvent{
-			Event: "content_block_start",
-			Data: ContentBlockStartEvent{
-				Type:  "content_block_start",
-				Index: c.contentIndex,
-				ContentBlock: ContentBlock{
-					Type:  "tool_use",
-					ID:    tc.ID,
-					Name:  tc.Function.Name,
-					Input: map[string]any{},
-				},
-			},
-		})
-
-		events = append(events, StreamEvent{
-			Event: "content_block_delta",
-			Data: ContentBlockDeltaEvent{
-				Type:  "content_block_delta",
-				Index: c.contentIndex,
-				Delta: Delta{
-					Type:        "input_json_delta",
-					PartialJSON: string(argsJSON),
-				},
-			},
-		})
-
-		events = append(events, StreamEvent{
-			Event: "content_block_stop",
-			Data: ContentBlockStopEvent{
-				Type:  "content_block_stop",
-				Index: c.contentIndex,
-			},
-		})
-
-		c.toolCallsSent[tc.ID] = true
-		c.contentIndex++
-	}
-
-	if r.Done {
-		if c.textStarted {
-			events = append(events, StreamEvent{
-				Event: "content_block_stop",
-				Data: ContentBlockStopEvent{
-					Type:  "content_block_stop",
-					Index: c.contentIndex,
-				},
-			})
-		} else if c.thinkingStarted && !c.thinkingDone {
-			events = append(events, StreamEvent{
-				Event: "content_block_stop",
-				Data: ContentBlockStopEvent{
-					Type:  "content_block_stop",
-					Index: c.contentIndex,
-				},
-			})
-		}
-
-		c.outputTokens = r.Metrics.EvalCount
-		stopReason := mapStopReason(r.DoneReason, len(c.toolCallsSent) > 0)
-
-		events = append(events, StreamEvent{
-			Event: "message_delta",
-			Data: MessageDeltaEvent{
-				Type: "message_delta",
-				Delta: MessageDelta{
-					StopReason: stopReason,
-				},
-				Usage: DeltaUsage{
-					OutputTokens: c.outputTokens,
-				},
-			},
-		})
-
-		events = append(events, StreamEvent{
-			Event: "message_stop",
-			Data: MessageStopEvent{
-				Type: "message_stop",
-			},
-		})
-	}
-
-	return events
-}
-
-// generateID generates a unique ID with the given prefix using crypto/rand
-func generateID(prefix string) string {
-	b := make([]byte, 12)
-	if _, err := rand.Read(b); err != nil {
-		// Fallback to time-based ID if crypto/rand fails
-		return fmt.Sprintf("%s_%d", prefix, time.Now().UnixNano())
-	}
-	return fmt.Sprintf("%s_%x", prefix, b)
-}
-
-// GenerateMessageID generates a unique message ID
-func GenerateMessageID() string {
-	return generateID("msg")
-}
-
-// ptr returns a pointer to the given string value
-func ptr(s string) *string {
-	return &s
-}
-
-// mapToArgs converts a map to ToolCallFunctionArguments
-func mapToArgs(m map[string]any) api.ToolCallFunctionArguments {
-	args := api.NewToolCallFunctionArguments()
-	for k, v := range m {
-		args.Set(k, v)
-	}
-	return args
-}

anthropic/anthropic_test.go

@@ -1,953 +0,0 @@
-package anthropic
-
-import (
-	"encoding/base64"
-	"encoding/json"
-	"testing"
-
-	"github.com/google/go-cmp/cmp"
-
-	"github.com/ollama/ollama/api"
-)
-
-const (
-	testImage = `iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVR42mNk+A8AAQUBAScY42YAAAAASUVORK5CYII=`
-)
-
-// testArgs creates ToolCallFunctionArguments from a map (convenience function for tests)
-func testArgs(m map[string]any) api.ToolCallFunctionArguments {
-	args := api.NewToolCallFunctionArguments()
-	for k, v := range m {
-		args.Set(k, v)
-	}
-	return args
-}
-
-func TestFromMessagesRequest_Basic(t *testing.T) {
-	req := MessagesRequest{
-		Model:     "test-model",
-		MaxTokens: 1024,
-		Messages: []MessageParam{
-			{Role: "user", Content: "Hello"},
-		},
-	}
-
-	result, err := FromMessagesRequest(req)
-	if err != nil {
-		t.Fatalf("unexpected error: %v", err)
-	}
-
-	if result.Model != "test-model" {
-		t.Errorf("expected model 'test-model', got %q", result.Model)
-	}
-
-	if len(result.Messages) != 1 {
-		t.Fatalf("expected 1 message, got %d", len(result.Messages))
-	}
-
-	if result.Messages[0].Role != "user" || result.Messages[0].Content != "Hello" {
-		t.Errorf("unexpected message: %+v", result.Messages[0])
-	}
-
-	if numPredict, ok := result.Options["num_predict"].(int); !ok || numPredict != 1024 {
-		t.Errorf("expected num_predict 1024, got %v", result.Options["num_predict"])
-	}
-}
-
-func TestFromMessagesRequest_WithSystemPrompt(t *testing.T) {
-	req := MessagesRequest{
-		Model:     "test-model",
-		MaxTokens: 1024,
-		System:    "You are a helpful assistant.",
-		Messages: []MessageParam{
-			{Role: "user", Content: "Hello"},
-		},
-	}
-
-	result, err := FromMessagesRequest(req)
-	if err != nil {
-		t.Fatalf("unexpected error: %v", err)
-	}
-
-	if len(result.Messages) != 2 {
-		t.Fatalf("expected 2 messages, got %d", len(result.Messages))
-	}
-
-	if result.Messages[0].Role != "system" || result.Messages[0].Content != "You are a helpful assistant." {
-		t.Errorf("unexpected system message: %+v", result.Messages[0])
-	}
-}
-
-func TestFromMessagesRequest_WithSystemPromptArray(t *testing.T) {
-	req := MessagesRequest{
-		Model:     "test-model",
-		MaxTokens: 1024,
-		System: []any{
-			map[string]any{"type": "text", "text": "You are helpful."},
-			map[string]any{"type": "text", "text": " Be concise."},
-		},
-		Messages: []MessageParam{
-			{Role: "user", Content: "Hello"},
-		},
-	}
-
-	result, err := FromMessagesRequest(req)
-	if err != nil {
-		t.Fatalf("unexpected error: %v", err)
-	}
-
-	if len(result.Messages) != 2 {
-		t.Fatalf("expected 2 messages, got %d", len(result.Messages))
-	}
-
-	if result.Messages[0].Content != "You are helpful. Be concise." {
-		t.Errorf("unexpected system message content: %q", result.Messages[0].Content)
-	}
-}
-
-func TestFromMessagesRequest_WithOptions(t *testing.T) {
-	temp := 0.7
-	topP := 0.9
-	topK := 40
-	req := MessagesRequest{
-		Model:         "test-model",
-		MaxTokens:     2048,
-		Messages:      []MessageParam{{Role: "user", Content: "Hello"}},
-		Temperature:   &temp,
-		TopP:          &topP,
-		TopK:          &topK,
-		StopSequences: []string{"\n", "END"},
-	}
-
-	result, err := FromMessagesRequest(req)
-	if err != nil {
-		t.Fatalf("unexpected error: %v", err)
-	}
-
-	if result.Options["temperature"] != 0.7 {
-		t.Errorf("expected temperature 0.7, got %v", result.Options["temperature"])
-	}
-	if result.Options["top_p"] != 0.9 {
-		t.Errorf("expected top_p 0.9, got %v", result.Options["top_p"])
-	}
-	if result.Options["top_k"] != 40 {
-		t.Errorf("expected top_k 40, got %v", result.Options["top_k"])
-	}
-	if diff := cmp.Diff([]string{"\n", "END"}, result.Options["stop"]); diff != "" {
-		t.Errorf("stop sequences mismatch: %s", diff)
-	}
-}
-
-func TestFromMessagesRequest_WithImage(t *testing.T) {
-	imgData, _ := base64.StdEncoding.DecodeString(testImage)
-
-	req := MessagesRequest{
-		Model:     "test-model",
-		MaxTokens: 1024,
-		Messages: []MessageParam{
-			{
-				Role: "user",
-				Content: []any{
-					map[string]any{"type": "text", "text": "What's in this image?"},
-					map[string]any{
-						"type": "image",
-						"source": map[string]any{
-							"type":       "base64",
-							"media_type": "image/png",
-							"data":       testImage,
-						},
-					},
-				},
-			},
-		},
-	}
-
-	result, err := FromMessagesRequest(req)
-	if err != nil {
-		t.Fatalf("unexpected error: %v", err)
-	}
-
-	if len(result.Messages) != 1 {
-		t.Fatalf("expected 1 message, got %d", len(result.Messages))
-	}
-
-	if result.Messages[0].Content != "What's in this image?" {
-		t.Errorf("expected content 'What's in this image?', got %q", result.Messages[0].Content)
-	}
-
-	if len(result.Messages[0].Images) != 1 {
-		t.Fatalf("expected 1 image, got %d", len(result.Messages[0].Images))
-	}
-
-	if string(result.Messages[0].Images[0]) != string(imgData) {
-		t.Error("image data mismatch")
-	}
-}
-
-func TestFromMessagesRequest_WithToolUse(t *testing.T) {
-	req := MessagesRequest{
-		Model:     "test-model",
-		MaxTokens: 1024,
-		Messages: []MessageParam{
-			{Role: "user", Content: "What's the weather in Paris?"},
-			{
-				Role: "assistant",
-				Content: []any{
-					map[string]any{
-						"type":  "tool_use",
-						"id":    "call_123",
-						"name":  "get_weather",
-						"input": map[string]any{"location": "Paris"},
-					},
-				},
-			},
-		},
-	}
-
-	result, err := FromMessagesRequest(req)
-	if err != nil {
-		t.Fatalf("unexpected error: %v", err)
-	}
-
-	if len(result.Messages) != 2 {
-		t.Fatalf("expected 2 messages, got %d", len(result.Messages))
-	}
-
-	if len(result.Messages[1].ToolCalls) != 1 {
-		t.Fatalf("expected 1 tool call, got %d", len(result.Messages[1].ToolCalls))
-	}
-
-	tc := result.Messages[1].ToolCalls[0]
-	if tc.ID != "call_123" {
-		t.Errorf("expected tool call ID 'call_123', got %q", tc.ID)
-	}
-	if tc.Function.Name != "get_weather" {
-		t.Errorf("expected tool name 'get_weather', got %q", tc.Function.Name)
-	}
-}
-
-func TestFromMessagesRequest_WithToolResult(t *testing.T) {
-	req := MessagesRequest{
-		Model:     "test-model",
-		MaxTokens: 1024,
-		Messages: []MessageParam{
-			{
-				Role: "user",
-				Content: []any{
-					map[string]any{
-						"type":        "tool_result",
-						"tool_use_id": "call_123",
-						"content":     "The weather in Paris is sunny, 22°C",
-					},
-				},
-			},
-		},
-	}
-
-	result, err := FromMessagesRequest(req)
-	if err != nil {
-		t.Fatalf("unexpected error: %v", err)
-	}
-
-	if len(result.Messages) != 1 {
-		t.Fatalf("expected 1 message, got %d", len(result.Messages))
-	}
-
-	msg := result.Messages[0]
-	if msg.Role != "tool" {
-		t.Errorf("expected role 'tool', got %q", msg.Role)
-	}
-	if msg.ToolCallID != "call_123" {
-		t.Errorf("expected tool_call_id 'call_123', got %q", msg.ToolCallID)
-	}
-	if msg.Content != "The weather in Paris is sunny, 22°C" {
-		t.Errorf("unexpected content: %q", msg.Content)
-	}
-}
-
-func TestFromMessagesRequest_WithTools(t *testing.T) {
-	req := MessagesRequest{
-		Model:     "test-model",
-		MaxTokens: 1024,
-		Messages:  []MessageParam{{Role: "user", Content: "Hello"}},
-		Tools: []Tool{
-			{
-				Name:        "get_weather",
-				Description: "Get current weather",
-				InputSchema: json.RawMessage(`{"type":"object","properties":{"location":{"type":"string"}},"required":["location"]}`),
-			},
-		},
-	}
-
-	result, err := FromMessagesRequest(req)
-	if err != nil {
-		t.Fatalf("unexpected error: %v", err)
-	}
-
-	if len(result.Tools) != 1 {
-		t.Fatalf("expected 1 tool, got %d", len(result.Tools))
-	}
-
-	tool := result.Tools[0]
-	if tool.Type != "function" {
-		t.Errorf("expected type 'function', got %q", tool.Type)
-	}
-	if tool.Function.Name != "get_weather" {
-		t.Errorf("expected name 'get_weather', got %q", tool.Function.Name)
-	}
-	if tool.Function.Description != "Get current weather" {
-		t.Errorf("expected description 'Get current weather', got %q", tool.Function.Description)
-	}
-}
-
-func TestFromMessagesRequest_WithThinking(t *testing.T) {
-	req := MessagesRequest{
-		Model:     "test-model",
-		MaxTokens: 1024,
-		Messages:  []MessageParam{{Role: "user", Content: "Hello"}},
-		Thinking:  &ThinkingConfig{Type: "enabled", BudgetTokens: 1000},
-	}
-
-	result, err := FromMessagesRequest(req)
-	if err != nil {
-		t.Fatalf("unexpected error: %v", err)
-	}
-
-	if result.Think == nil {
-		t.Fatal("expected Think to be set")
-	}
-	if v, ok := result.Think.Value.(bool); !ok || !v {
-		t.Errorf("expected Think.Value to be true, got %v", result.Think.Value)
-	}
-}
-
-// TestFromMessagesRequest_ThinkingOnlyBlock verifies that messages containing only
-// a thinking block (no text, images, or tool calls) are preserved and not dropped.
-func TestFromMessagesRequest_ThinkingOnlyBlock(t *testing.T) {
-	req := MessagesRequest{
-		Model:     "test-model",
-		MaxTokens: 1024,
-		Messages: []MessageParam{
-			{Role: "user", Content: "Hello"},
-			{
-				Role: "assistant",
-				Content: []any{
-					map[string]any{
-						"type":     "thinking",
-						"thinking": "Let me think about this...",
-					},
-				},
-			},
-		},
-	}
-
-	result, err := FromMessagesRequest(req)
-	if err != nil {
-		t.Fatalf("unexpected error: %v", err)
-	}
-
-	if len(result.Messages) != 2 {
-		t.Fatalf("expected 2 messages, got %d", len(result.Messages))
-	}
-
-	assistantMsg := result.Messages[1]
-	if assistantMsg.Thinking != "Let me think about this..." {
-		t.Errorf("expected thinking content, got %q", assistantMsg.Thinking)
-	}
-}
-
-func TestFromMessagesRequest_ToolUseMissingID(t *testing.T) {
-	req := MessagesRequest{
-		Model:     "test-model",
-		MaxTokens: 1024,
-		Messages: []MessageParam{
-			{
-				Role: "assistant",
-				Content: []any{
-					map[string]any{
-						"type": "tool_use",
-						"name": "get_weather",
-					},
-				},
-			},
-		},
-	}
-
-	_, err := FromMessagesRequest(req)
-	if err == nil {
-		t.Fatal("expected error for missing tool_use id")
-	}
-	if err.Error() != "tool_use block missing required 'id' field" {
-		t.Errorf("unexpected error message: %v", err)
-	}
-}
-
-func TestFromMessagesRequest_ToolUseMissingName(t *testing.T) {
-	req := MessagesRequest{
-		Model:     "test-model",
-		MaxTokens: 1024,
-		Messages: []MessageParam{
-			{
-				Role: "assistant",
-				Content: []any{
-					map[string]any{
-						"type": "tool_use",
-						"id":   "call_123",
-					},
-				},
-			},
-		},
-	}
-
-	_, err := FromMessagesRequest(req)
-	if err == nil {
-		t.Fatal("expected error for missing tool_use name")
-	}
-	if err.Error() != "tool_use block missing required 'name' field" {
-		t.Errorf("unexpected error message: %v", err)
-	}
-}
-
-func TestFromMessagesRequest_InvalidToolSchema(t *testing.T) {
-	req := MessagesRequest{
-		Model:     "test-model",
-		MaxTokens: 1024,
-		Messages:  []MessageParam{{Role: "user", Content: "Hello"}},
-		Tools: []Tool{
-			{
-				Name:        "bad_tool",
-				InputSchema: json.RawMessage(`{invalid json`),
-			},
-		},
-	}
-
-	_, err := FromMessagesRequest(req)
-	if err == nil {
-		t.Fatal("expected error for invalid tool schema")
-	}
-}
-
-func TestToMessagesResponse_Basic(t *testing.T) {
-	resp := api.ChatResponse{
-		Model: "test-model",
-		Message: api.Message{
-			Role:    "assistant",
-			Content: "Hello there!",
-		},
-		Done:       true,
-		DoneReason: "stop",
-		Metrics: api.Metrics{
-			PromptEvalCount: 10,
-			EvalCount:       5,
-		},
-	}
-
-	result := ToMessagesResponse("msg_123", resp)
-
-	if result.ID != "msg_123" {
-		t.Errorf("expected ID 'msg_123', got %q", result.ID)
-	}
-	if result.Type != "message" {
-		t.Errorf("expected type 'message', got %q", result.Type)
-	}
-	if result.Role != "assistant" {
-		t.Errorf("expected role 'assistant', got %q", result.Role)
-	}
-	if len(result.Content) != 1 {
-		t.Fatalf("expected 1 content block, got %d", len(result.Content))
-	}
-	if result.Content[0].Type != "text" || result.Content[0].Text == nil || *result.Content[0].Text != "Hello there!" {
-		t.Errorf("unexpected content: %+v", result.Content[0])
-	}
-	if result.StopReason != "end_turn" {
-		t.Errorf("expected stop_reason 'end_turn', got %q", result.StopReason)
-	}
-	if result.Usage.InputTokens != 10 || result.Usage.OutputTokens != 5 {
-		t.Errorf("unexpected usage: %+v", result.Usage)
-	}
-}
-
-func TestToMessagesResponse_WithToolCalls(t *testing.T) {
-	resp := api.ChatResponse{
-		Model: "test-model",
-		Message: api.Message{
-			Role: "assistant",
-			ToolCalls: []api.ToolCall{
-				{
-					ID: "call_123",
-					Function: api.ToolCallFunction{
-						Name:      "get_weather",
-						Arguments: testArgs(map[string]any{"location": "Paris"}),
-					},
-				},
-			},
-		},
-		Done:       true,
-		DoneReason: "stop",
-	}
-
-	result := ToMessagesResponse("msg_123", resp)
-
-	if len(result.Content) != 1 {
-		t.Fatalf("expected 1 content block, got %d", len(result.Content))
-	}
-	if result.Content[0].Type != "tool_use" {
-		t.Errorf("expected type 'tool_use', got %q", result.Content[0].Type)
-	}
-	if result.Content[0].ID != "call_123" {
-		t.Errorf("expected ID 'call_123', got %q", result.Content[0].ID)
-	}
-	if result.Content[0].Name != "get_weather" {
-		t.Errorf("expected name 'get_weather', got %q", result.Content[0].Name)
-	}
-	if result.StopReason != "tool_use" {
-		t.Errorf("expected stop_reason 'tool_use', got %q", result.StopReason)
-	}
-}
-
-func TestToMessagesResponse_WithThinking(t *testing.T) {
-	resp := api.ChatResponse{
-		Model: "test-model",
-		Message: api.Message{
-			Role:     "assistant",
-			Content:  "The answer is 42.",
-			Thinking: "Let me think about this...",
-		},
-		Done:       true,
-		DoneReason: "stop",
-	}
-
-	result := ToMessagesResponse("msg_123", resp)
-
-	if len(result.Content) != 2 {
-		t.Fatalf("expected 2 content blocks, got %d", len(result.Content))
-	}
-	if result.Content[0].Type != "thinking" {
-		t.Errorf("expected first block type 'thinking', got %q", result.Content[0].Type)
-	}
-	if result.Content[0].Thinking == nil || *result.Content[0].Thinking != "Let me think about this..." {
-		t.Errorf("unexpected thinking content: %v", result.Content[0].Thinking)
-	}
-	if result.Content[1].Type != "text" {
-		t.Errorf("expected second block type 'text', got %q", result.Content[1].Type)
-	}
-}
-
-func TestMapStopReason(t *testing.T) {
-	tests := []struct {
-		reason       string
-		hasToolCalls bool
-		want         string
-	}{
-		{"stop", false, "end_turn"},
-		{"length", false, "max_tokens"},
-		{"stop", true, "tool_use"},
-		{"other", false, "stop_sequence"},
-		{"", false, ""},
-	}
-
-	for _, tt := range tests {
-		got := mapStopReason(tt.reason, tt.hasToolCalls)
-		if got != tt.want {
-			t.Errorf("mapStopReason(%q, %v) = %q, want %q", tt.reason, tt.hasToolCalls, got, tt.want)
-		}
-	}
-}
-
-func TestNewError(t *testing.T) {
-	tests := []struct {
-		code int
-		want string
-	}{
-		{400, "invalid_request_error"},
-		{401, "authentication_error"},
-		{403, "permission_error"},
-		{404, "not_found_error"},
-		{429, "rate_limit_error"},
-		{500, "api_error"},
-		{503, "overloaded_error"},
-		{529, "overloaded_error"},
-	}
-
-	for _, tt := range tests {
-		result := NewError(tt.code, "test message")
-		if result.Type != "error" {
-			t.Errorf("NewError(%d) type = %q, want 'error'", tt.code, result.Type)
-		}
-		if result.Error.Type != tt.want {
-			t.Errorf("NewError(%d) error.type = %q, want %q", tt.code, result.Error.Type, tt.want)
-		}
-		if result.Error.Message != "test message" {
-			t.Errorf("NewError(%d) message = %q, want 'test message'", tt.code, result.Error.Message)
-		}
-		if result.RequestID == "" {
-			t.Errorf("NewError(%d) request_id should not be empty", tt.code)
-		}
-	}
-}
-
-func TestGenerateMessageID(t *testing.T) {
-	id1 := GenerateMessageID()
-	id2 := GenerateMessageID()
-
-	if id1 == "" {
-		t.Error("GenerateMessageID returned empty string")
-	}
-	if id1 == id2 {
-		t.Error("GenerateMessageID returned duplicate IDs")
-	}
-	if len(id1) < 10 {
-		t.Errorf("GenerateMessageID returned short ID: %q", id1)
-	}
-	if id1[:4] != "msg_" {
-		t.Errorf("GenerateMessageID should start with 'msg_', got %q", id1[:4])
-	}
-}
-
-func TestStreamConverter_Basic(t *testing.T) {
-	conv := NewStreamConverter("msg_123", "test-model")
-
-	// First chunk
-	resp1 := api.ChatResponse{
-		Model: "test-model",
-		Message: api.Message{
-			Role:    "assistant",
-			Content: "Hello",
-		},
-		Metrics: api.Metrics{PromptEvalCount: 10},
-	}
-
-	events1 := conv.Process(resp1)
-	if len(events1) < 3 {
-		t.Fatalf("expected at least 3 events for first chunk, got %d", len(events1))
-	}
-
-	// Should have message_start, content_block_start, content_block_delta
-	if events1[0].Event != "message_start" {
-		t.Errorf("expected first event 'message_start', got %q", events1[0].Event)
-	}
-	if events1[1].Event != "content_block_start" {
-		t.Errorf("expected second event 'content_block_start', got %q", events1[1].Event)
-	}
-	if events1[2].Event != "content_block_delta" {
-		t.Errorf("expected third event 'content_block_delta', got %q", events1[2].Event)
-	}
-
-	// Final chunk
-	resp2 := api.ChatResponse{
-		Model: "test-model",
-		Message: api.Message{
-			Role:    "assistant",
-			Content: " world!",
-		},
-		Done:       true,
-		DoneReason: "stop",
-		Metrics:    api.Metrics{EvalCount: 5},
-	}
-
-	events2 := conv.Process(resp2)
-
-	// Should have content_block_delta, content_block_stop, message_delta, message_stop
-	hasStop := false
-	for _, e := range events2 {
-		if e.Event == "message_stop" {
-			hasStop = true
-		}
-	}
-	if !hasStop {
-		t.Error("expected message_stop event in final chunk")
-	}
-}
-
-func TestStreamConverter_WithToolCalls(t *testing.T) {
-	conv := NewStreamConverter("msg_123", "test-model")
-
-	resp := api.ChatResponse{
-		Model: "test-model",
-		Message: api.Message{
-			Role: "assistant",
-			ToolCalls: []api.ToolCall{
-				{
-					ID: "call_123",
-					Function: api.ToolCallFunction{
-						Name:      "get_weather",
-						Arguments: testArgs(map[string]any{"location": "Paris"}),
-					},
-				},
-			},
-		},
-		Done:       true,
-		DoneReason: "stop",
-		Metrics:    api.Metrics{PromptEvalCount: 10, EvalCount: 5},
-	}
-
-	events := conv.Process(resp)
-
-	hasToolStart := false
-	hasToolDelta := false
-	for _, e := range events {
-		if e.Event == "content_block_start" {
-			if start, ok := e.Data.(ContentBlockStartEvent); ok {
-				if start.ContentBlock.Type == "tool_use" {
-					hasToolStart = true
-				}
-			}
-		}
-		if e.Event == "content_block_delta" {
-			if delta, ok := e.Data.(ContentBlockDeltaEvent); ok {
-				if delta.Delta.Type == "input_json_delta" {
-					hasToolDelta = true
-				}
-			}
-		}
-	}
-
-	if !hasToolStart {
-		t.Error("expected tool_use content_block_start event")
-	}
-	if !hasToolDelta {
-		t.Error("expected input_json_delta event")
-	}
-}
-
-func TestStreamConverter_ToolCallWithUnmarshalableArgs(t *testing.T) {
-	// Test that unmarshalable arguments (like channels) are handled gracefully
-	// and don't cause a panic or corrupt stream
-	conv := NewStreamConverter("msg_123", "test-model")
-
-	// Create a channel which cannot be JSON marshaled
-	unmarshalable := make(chan int)
-	badArgs := api.NewToolCallFunctionArguments()
-	badArgs.Set("channel", unmarshalable)
-
-	resp := api.ChatResponse{
-		Model: "test-model",
-		Message: api.Message{
-			Role: "assistant",
-			ToolCalls: []api.ToolCall{
-				{
-					ID: "call_bad",
-					Function: api.ToolCallFunction{
-						Name:      "bad_function",
-						Arguments: badArgs,
-					},
-				},
-			},
-		},
-		Done:       true,
-		DoneReason: "stop",
-	}
-
-	// Should not panic and should skip the unmarshalable tool call
-	events := conv.Process(resp)
-
-	// Verify no tool_use block was started (since marshal failed before block start)
-	hasToolStart := false
-	for _, e := range events {
-		if e.Event == "content_block_start" {
-			if start, ok := e.Data.(ContentBlockStartEvent); ok {
-				if start.ContentBlock.Type == "tool_use" {
-					hasToolStart = true
-				}
-			}
-		}
-	}
-
-	if hasToolStart {
-		t.Error("expected no tool_use block when arguments cannot be marshaled")
-	}
-}
-
-func TestStreamConverter_MultipleToolCallsWithMixedValidity(t *testing.T) {
-	// Test that valid tool calls still work when mixed with invalid ones
-	conv := NewStreamConverter("msg_123", "test-model")
-
-	unmarshalable := make(chan int)
-	badArgs := api.NewToolCallFunctionArguments()
-	badArgs.Set("channel", unmarshalable)
-
-	resp := api.ChatResponse{
-		Model: "test-model",
-		Message: api.Message{
-			Role: "assistant",
-			ToolCalls: []api.ToolCall{
-				{
-					ID: "call_good",
-					Function: api.ToolCallFunction{
-						Name:      "good_function",
-						Arguments: testArgs(map[string]any{"location": "Paris"}),
-					},
-				},
-				{
-					ID: "call_bad",
-					Function: api.ToolCallFunction{
-						Name:      "bad_function",
-						Arguments: badArgs,
-					},
-				},
-			},
-		},
-		Done:       true,
-		DoneReason: "stop",
-	}
-
-	events := conv.Process(resp)
-
-	// Count tool_use blocks - should only have 1 (the valid one)
-	toolStartCount := 0
-	toolDeltaCount := 0
-	for _, e := range events {
-		if e.Event == "content_block_start" {
-			if start, ok := e.Data.(ContentBlockStartEvent); ok {
-				if start.ContentBlock.Type == "tool_use" {
-					toolStartCount++
-					if start.ContentBlock.Name != "good_function" {
-						t.Errorf("expected tool name 'good_function', got %q", start.ContentBlock.Name)
-					}
-				}
-			}
-		}
-		if e.Event == "content_block_delta" {
-			if delta, ok := e.Data.(ContentBlockDeltaEvent); ok {
-				if delta.Delta.Type == "input_json_delta" {
-					toolDeltaCount++
-				}
-			}
-		}
-	}
-
-	if toolStartCount != 1 {
-		t.Errorf("expected 1 tool_use block, got %d", toolStartCount)
-	}
-	if toolDeltaCount != 1 {
-		t.Errorf("expected 1 input_json_delta, got %d", toolDeltaCount)
-	}
-}
-
-// TestContentBlockJSON_EmptyFieldsPresent verifies that empty text and thinking fields
-// are serialized in JSON output. The Anthropic SDK requires these fields to be present
-// (even when empty) in content_block_start events to properly accumulate streaming deltas.
-// Without these fields, the SDK throws: "TypeError: unsupported operand type(s) for +=: 'NoneType' and 'str'"
-func TestContentBlockJSON_EmptyFieldsPresent(t *testing.T) {
-	tests := []struct {
-		name     string
-		block    ContentBlock
-		wantKeys []string
-	}{
-		{
-			name: "text block includes empty text field",
-			block: ContentBlock{
-				Type: "text",
-				Text: ptr(""),
-			},
-			wantKeys: []string{"type", "text"},
-		},
-		{
-			name: "thinking block includes empty thinking field",
-			block: ContentBlock{
-				Type:     "thinking",
-				Thinking: ptr(""),
-			},
-			wantKeys: []string{"type", "thinking"},
-		},
-		{
-			name: "text block with content",
-			block: ContentBlock{
-				Type: "text",
-				Text: ptr("hello"),
-			},
-			wantKeys: []string{"type", "text"},
-		},
-	}
-
-	for _, tt := range tests {
-		t.Run(tt.name, func(t *testing.T) {
-			data, err := json.Marshal(tt.block)
-			if err != nil {
-				t.Fatalf("failed to marshal: %v", err)
-			}
-
-			var result map[string]any
-			if err := json.Unmarshal(data, &result); err != nil {
-				t.Fatalf("failed to unmarshal: %v", err)
-			}
-
-			for _, key := range tt.wantKeys {
-				if _, ok := result[key]; !ok {
-					t.Errorf("expected key %q to be present in JSON output, got: %s", key, string(data))
-				}
-			}
-		})
-	}
-}
-
-// TestStreamConverter_ContentBlockStartIncludesEmptyFields verifies that content_block_start
-// events include the required empty fields for SDK compatibility.
-func TestStreamConverter_ContentBlockStartIncludesEmptyFields(t *testing.T) {
-	t.Run("text block start includes empty text", func(t *testing.T) {
-		conv := NewStreamConverter("msg_123", "test-model")
-
-		resp := api.ChatResponse{
-			Model:   "test-model",
-			Message: api.Message{Role: "assistant", Content: "hello"},
-		}
-
-		events := conv.Process(resp)
-
-		var foundTextStart bool
-		for _, e := range events {
-			if e.Event == "content_block_start" {
-				if start, ok := e.Data.(ContentBlockStartEvent); ok {
-					if start.ContentBlock.Type == "text" {
-						foundTextStart = true
-						// Marshal and verify the text field is present
-						data, _ := json.Marshal(start)
-						var result map[string]any
-						json.Unmarshal(data, &result)
-						cb := result["content_block"].(map[string]any)
-						if _, ok := cb["text"]; !ok {
-							t.Error("content_block_start for text should include 'text' field")
-						}
-					}
-				}
-			}
-		}
-
-		if !foundTextStart {
-			t.Error("expected text content_block_start event")
-		}
-	})
-
-	t.Run("thinking block start includes empty thinking", func(t *testing.T) {
-		conv := NewStreamConverter("msg_123", "test-model")
-
-		resp := api.ChatResponse{
-			Model:   "test-model",
-			Message: api.Message{Role: "assistant", Thinking: "let me think..."},
-		}
-
-		events := conv.Process(resp)
-
-		var foundThinkingStart bool
-		for _, e := range events {
-			if e.Event == "content_block_start" {
-				if start, ok := e.Data.(ContentBlockStartEvent); ok {
-					if start.ContentBlock.Type == "thinking" {
-						foundThinkingStart = true
-						data, _ := json.Marshal(start)
-						var result map[string]any
-						json.Unmarshal(data, &result)
-						cb := result["content_block"].(map[string]any)
-						if _, ok := cb["thinking"]; !ok {
-							t.Error("content_block_start for thinking should include 'thinking' field")
-						}
-					}
-				}
-			}
-		}
-
-		if !foundThinkingStart {
-			t.Error("expected thinking content_block_start event")
-		}
-	})
-}

cmd/cmd.go

@@ -46,8 +46,6 @@ import (
 	"github.com/ollama/ollama/types/syncmap"
 	"github.com/ollama/ollama/version"
 	xcmd "github.com/ollama/ollama/x/cmd"
-	"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"
@@ -98,10 +96,6 @@ func CreateHandler(cmd *cobra.Command, args []string) error {
 	filename, err := getModelfileName(cmd)
 	if os.IsNotExist(err) {
 		if filename == "" {
-			// No Modelfile found - check if current directory is an image gen model
-			if imagegen.IsTensorModelDir(".") {
-				return imagegenclient.CreateModel(args[0], ".", p)
-			}
 			reader = strings.NewReader("FROM .\n")
 		} else {
 			return errModelfileNotFound
@@ -463,15 +457,6 @@ func RunHandler(cmd *cobra.Command, args []string) error {
 	}
 
 	name := args[0]
-
-	// Check if this is a known image generation model (skip Show/Pull)
-	if imagegen.HasTensorLayers(name) {
-		if opts.Prompt == "" && !interactive {
-			return errors.New("image generation models require a prompt. Usage: ollama run " + name + " \"your prompt here\"")
-		}
-		return imagegen.RunCLI(cmd, name, opts.Prompt, interactive, opts.KeepAlive)
-	}
-
 	info, err := func() (*api.ShowResponse, error) {
 		showReq := &api.ShowRequest{Name: name}
 		info, err := client.Show(cmd.Context(), showReq)
@@ -535,7 +520,7 @@ func RunHandler(cmd *cobra.Command, args []string) error {
 
 	// Check for experimental flag
 	isExperimental, _ := cmd.Flags().GetBool("experimental")
-	yoloMode, _ := cmd.Flags().GetBool("experimental-yolo")
+	yoloMode, _ := cmd.Flags().GetBool("yolo")
 
 	if interactive {
 		if err := loadOrUnloadModel(cmd, &opts); err != nil {
@@ -837,11 +822,6 @@ func DeleteHandler(cmd *cobra.Command, args []string) error {
 }
 
 func ShowHandler(cmd *cobra.Command, args []string) error {
-	// Check if this is an image generation model
-	if imagegen.HasTensorLayers(args[0]) {
-		return imagegen.Show(args[0], os.Stdout)
-	}
-
 	client, err := api.ClientFromEnvironment()
 	if err != nil {
 		return err
@@ -1785,10 +1765,7 @@ func NewCLI() *cobra.Command {
 	runCmd.Flags().Bool("truncate", false, "For embedding models: truncate inputs exceeding context length (default: true). Set --truncate=false to error instead")
 	runCmd.Flags().Int("dimensions", 0, "Truncate output embeddings to specified dimension (embedding models only)")
 	runCmd.Flags().Bool("experimental", false, "Enable experimental agent loop with tools")
-	runCmd.Flags().Bool("experimental-yolo", false, "Skip all tool approval prompts (use with caution)")
-
-	// Image generation flags (width, height, steps, seed, etc.)
-	imagegen.RegisterFlags(runCmd)
+	runCmd.Flags().BoolP("yolo", "y", false, "Skip all tool approval prompts (use with caution)")
 
 	stopCmd := &cobra.Command{
 		Use:     "stop MODEL",

convert/convert.go

@@ -6,14 +6,11 @@ import (
 	"errors"
 	"fmt"
 	"io/fs"
-	"iter"
 	"log/slog"
-	"maps"
 	"os"
 	"slices"
 	"strings"
 
-	ofs "github.com/ollama/ollama/fs"
 	"github.com/ollama/ollama/fs/ggml"
 )
 
@@ -21,13 +18,8 @@ type ModelParameters struct {
 	Architectures []string `json:"architectures"`
 	VocabSize     uint32   `json:"vocab_size"`
 
-	// TODO is this needed?
-	ModelType string `json:"model_type"`
-
 	TextModel struct {
-		VocabSize  uint32 `json:"vocab_size"`
-		HiddenSize uint32 `json:"hidden_size"`
-		ModelType  string `json:"model_type"`
+		VocabSize uint32 `json:"vocab_size"`
 	} `json:"text_config"`
 }
 
@@ -41,94 +33,8 @@ type AdapterParameters struct {
 	} `json:"lora_parameters"`
 }
 
-type KV map[string]any
-
-func (kv KV) Architecture() string {
-	return kv.String("general.architecture", "unknown")
-}
-
-type valueTypes interface {
-	uint8 | int8 | uint16 | int16 |
-		uint32 | int32 | uint64 | int64 |
-		string | float32 | float64 | bool
-}
-
-type arrayValueTypes interface {
-	[]uint8 | []int8 | []uint16 | []int16 |
-		[]uint32 | []int32 | []uint64 | []int64 |
-		[]string | []float32 | []float64 | []bool
-}
-
-func keyValue[T valueTypes | arrayValueTypes](kv KV, key string, defaultValue ...T) (T, bool) {
-	if !strings.HasPrefix(key, "tokenizer.") && !strings.HasPrefix(key, "general.") {
-		key = kv.Architecture() + "." + key
-	}
-
-	if val, ok := kv[key].(T); ok {
-		return val, true
-	}
-	return defaultValue[0], false
-}
-
-func (kv KV) String(key string, defaultValue ...string) string {
-	val, _ := keyValue(kv, key, append(defaultValue, "")...)
-	return val
-}
-
-func (kv KV) Uint(key string, defaultValue ...uint32) uint32 {
-	val, _ := keyValue(kv, key, append(defaultValue, 0)...)
-	return val
-}
-
-func (kv KV) Float(key string, defaultValue ...float32) float32 {
-	val, _ := keyValue(kv, key, append(defaultValue, 0)...)
-	return val
-}
-
-func (kv KV) Bool(key string, defaultValue ...bool) bool {
-	val, _ := keyValue(kv, key, append(defaultValue, false)...)
-	return val
-}
-
-func (kv KV) Strings(key string, defaultValue ...[]string) []string {
-	val, _ := keyValue(kv, key, append(defaultValue, []string{""})...)
-	return val
-}
-
-func (kv KV) Ints(key string, defaultValue ...[]int32) []int32 {
-	val, _ := keyValue(kv, key, append(defaultValue, []int32{0})...)
-	return val
-}
-
-func (kv KV) Uints(key string, defaultValue ...[]uint32) []uint32 {
-	val, _ := keyValue(kv, key, append(defaultValue, []uint32{0})...)
-	return val
-}
-
-func (kv KV) Floats(key string, defaultValue ...[]float32) []float32 {
-	val, _ := keyValue(kv, key, append(defaultValue, []float32{0})...)
-	return val
-}
-
-func (kv KV) Bools(key string, defaultValue ...[]bool) []bool {
-	val, _ := keyValue(kv, key, append(defaultValue, []bool{false})...)
-	return val
-}
-
-func (kv KV) Len() int {
-	return len(kv)
-}
-
-func (kv KV) Keys() iter.Seq[string] {
-	return maps.Keys(kv)
-}
-
-func (kv KV) Value(key string) any {
-	return kv[key]
-}
-
-func (ModelParameters) KV(t *Tokenizer) KV {
-	kv := KV{
+func (ModelParameters) KV(t *Tokenizer) ggml.KV {
+	kv := ggml.KV{
 		"general.file_type":            uint32(1),
 		"general.quantization_version": uint32(2),
 		"tokenizer.ggml.pre":           t.Pre,
@@ -157,7 +63,7 @@ func (ModelParameters) KV(t *Tokenizer) KV {
 	return kv
 }
 
-func (p AdapterParameters) KV() KV {
+func (p AdapterParameters) KV() ggml.KV {
 	var alpha float32
 	if p.LoraParameters.Alpha == 0 {
 		alpha = float32(p.Alpha)
@@ -165,7 +71,7 @@ func (p AdapterParameters) KV() KV {
 		alpha = p.LoraParameters.Alpha
 	}
 
-	kv := KV{
+	kv := ggml.KV{
 		"adapter.lora.alpha": alpha,
 		"adapter.type":       "lora",
 		"general.file_type":  uint32(1),
@@ -182,14 +88,9 @@ func (ModelParameters) specialTokenTypes() []string {
 	}
 }
 
-type ModelKV interface {
-	// KV maps parameters to LLM key-values
-	KV(*Tokenizer) KV
-}
-
 type ModelConverter interface {
-	ModelKV
-
+	// KV maps parameters to LLM key-values
+	KV(*Tokenizer) ggml.KV
 	// Tensors maps input tensors to LLM tensors. Model specific modifications can be done here.
 	Tensors([]Tensor) []*ggml.Tensor
 	// Replacements returns a list of string pairs to replace in tensor names.
@@ -206,7 +107,7 @@ type moreParser interface {
 
 type AdapterConverter interface {
 	// KV maps parameters to LLM key-values
-	KV(ofs.Config) KV
+	KV(ggml.KV) ggml.KV
 	// Tensors maps input tensors to LLM tensors. Adapter specific modifications can be done here.
 	Tensors([]Tensor) []*ggml.Tensor
 	// Replacements returns a list of string pairs to replace in tensor names.
@@ -214,7 +115,7 @@ type AdapterConverter interface {
 	Replacements() []string
 }
 
-func ConvertAdapter(fsys fs.FS, f *os.File, baseKV ofs.Config) error {
+func ConvertAdapter(fsys fs.FS, f *os.File, baseKV ggml.KV) error {
 	bts, err := fs.ReadFile(fsys, "adapter_config.json")
 	if err != nil {
 		return err
@@ -225,8 +126,8 @@ func ConvertAdapter(fsys fs.FS, f *os.File, baseKV ofs.Config) error {
 		return err
 	}
 
-	arch := baseKV.Architecture()
-	if arch == "" {
+	arch, ok := baseKV["general.architecture"]
+	if !ok {
 		return errors.New("architecture not set for the base model")
 	}
 
@@ -252,19 +153,23 @@ func ConvertAdapter(fsys fs.FS, f *os.File, baseKV ofs.Config) error {
 	return writeFile(f, conv.KV(baseKV), conv.Tensors(ts))
 }
 
-func LoadModelMetadata(fsys fs.FS) (ModelKV, *Tokenizer, error) {
+// Convert writes an Ollama compatible model to the provided io.WriteSeeker based on configurations
+// and files it finds in the input path.
+// Supported input model formats include safetensors.
+// Supported input tokenizers files include tokenizer.json (preferred) and tokenizer.model.
+func ConvertModel(fsys fs.FS, f *os.File) error {
 	bts, err := fs.ReadFile(fsys, "config.json")
 	if err != nil {
-		return nil, nil, err
+		return err
 	}
 
 	var p ModelParameters
 	if err := json.Unmarshal(bts, &p); err != nil {
-		return nil, nil, err
+		return err
 	}
 
 	if len(p.Architectures) < 1 {
-		return nil, nil, errors.New("unknown architecture")
+		return errors.New("unknown architecture")
 	}
 
 	var conv ModelConverter
@@ -312,22 +217,22 @@ func LoadModelMetadata(fsys fs.FS) (ModelKV, *Tokenizer, error) {
 	case "DeepseekV3ForCausalLM":
 		conv = &deepseek2Model{}
 	default:
-		return nil, nil, fmt.Errorf("unsupported architecture %q", p.Architectures[0])
+		return fmt.Errorf("unsupported architecture %q", p.Architectures[0])
 	}
 
 	if err := json.Unmarshal(bts, conv); err != nil {
-		return nil, nil, err
+		return err
 	}
 
 	if t, ok := conv.(moreParser); ok {
 		if err := t.parseMore(fsys); err != nil {
-			return nil, nil, err
+			return err
 		}
 	}
 
 	t, err := parseTokenizer(fsys, conv.specialTokenTypes())
 	if err != nil {
-		return nil, nil, err
+		return err
 	}
 
 	vocabSize := int(cmp.Or(p.VocabSize, p.TextModel.VocabSize))
@@ -349,19 +254,6 @@ func LoadModelMetadata(fsys fs.FS) (ModelKV, *Tokenizer, error) {
 	default:
 		slog.Debug("vocabulary", "size", len(t.Vocabulary.Tokens))
 	}
-	return conv, t, nil
-}
-
-// Convert writes an Ollama compatible model to the provided io.WriteSeeker based on configurations
-// and files it finds in the input path.
-// Supported input model formats include safetensors.
-// Supported input tokenizers files include tokenizer.json (preferred) and tokenizer.model.
-func ConvertModel(fsys fs.FS, f *os.File) error {
-	kv, t, err := LoadModelMetadata(fsys)
-	if err != nil {
-		return err
-	}
-	conv := kv.(ModelConverter)
 
 	ts, err := parseTensors(fsys, strings.NewReplacer(conv.Replacements()...))
 	if err != nil {
@@ -371,7 +263,7 @@ func ConvertModel(fsys fs.FS, f *os.File) error {
 	return writeFile(f, conv.KV(t), conv.Tensors(ts))
 }
 
-func writeFile(f *os.File, kv KV, ts []*ggml.Tensor) error {
+func writeFile(f *os.File, kv ggml.KV, ts []*ggml.Tensor) error {
 	for i := range ts {
 		ts[i].Shape = slices.Clone(ts[i].Shape)
 		slices.Reverse(ts[i].Shape)

convert/convert_bert.go

@@ -88,7 +88,7 @@ func (p *bertModel) parseMore(fsys fs.FS) error {
 	return nil
 }
 
-func (p *bertModel) KV(t *Tokenizer) KV {
+func (p *bertModel) KV(t *Tokenizer) ggml.KV {
 	kv := p.ModelParameters.KV(t)
 	kv["general.architecture"] = "bert"
 	kv["bert.attention.causal"] = false

convert/convert_commandr.go

@@ -24,7 +24,7 @@ type commandrModel struct {
 
 var _ ModelConverter = (*commandrModel)(nil)
 
-func (p *commandrModel) KV(t *Tokenizer) KV {
+func (p *commandrModel) KV(t *Tokenizer) ggml.KV {
 	kv := p.ModelParameters.KV(t)
 	kv["general.architecture"] = "command-r"
 	kv["general.name"] = "command-r"

convert/convert_deepseek2.go

@@ -47,7 +47,7 @@ type deepseek2Model struct {
 	Architecture string
 }
 
-func (p *deepseek2Model) KV(t *Tokenizer) KV {
+func (p *deepseek2Model) KV(t *Tokenizer) ggml.KV {
 	kv := p.ModelParameters.KV(t)
 	kv["general.architecture"] = "deepseek2"
 	kv["general.type"] = "model"

convert/convert_deepseekocr.go

@@ -41,7 +41,7 @@ type deepseekocr struct {
 	} `json:"vision_config"`
 }
 
-func (m *deepseekocr) KV(t *Tokenizer) KV {
+func (m *deepseekocr) KV(t *Tokenizer) ggml.KV {
 	kv := m.ModelParameters.KV(t)
 	kv["general.architecture"] = "deepseekocr"
 	kv["block_count"] = m.LanguageConfig.HiddenLayers

convert/convert_gemma.go

@@ -23,7 +23,7 @@ type gemmaModel struct {
 
 var _ ModelConverter = (*gemmaModel)(nil)
 
-func (p *gemmaModel) KV(t *Tokenizer) KV {
+func (p *gemmaModel) KV(t *Tokenizer) ggml.KV {
 	kv := p.ModelParameters.KV(t)
 	kv["general.architecture"] = "gemma"
 	kv["gemma.context_length"] = p.MaxPositionEmbeddings

convert/convert_gemma2.go

@@ -1,5 +1,7 @@
 package convert
 
+import "github.com/ollama/ollama/fs/ggml"
+
 type gemma2Model struct {
 	gemmaModel
 	SlidingWindow         uint32  `json:"sliding_window"`
@@ -7,7 +9,7 @@ type gemma2Model struct {
 	FinalLogitSoftcap     float32 `json:"final_logit_softcapping"`
 }
 
-func (p *gemma2Model) KV(t *Tokenizer) KV {
+func (p *gemma2Model) KV(t *Tokenizer) ggml.KV {
 	kv := p.ModelParameters.KV(t)
 	kv["general.architecture"] = "gemma2"
 	kv["gemma2.context_length"] = p.MaxPositionEmbeddings

convert/convert_gemma2_adapter.go

@@ -6,7 +6,6 @@ import (
 	"github.com/pdevine/tensor"
 	"github.com/pdevine/tensor/native"
 
-	"github.com/ollama/ollama/fs"
 	"github.com/ollama/ollama/fs/ggml"
 )
 
@@ -16,7 +15,7 @@ type gemma2Adapter struct {
 
 var _ AdapterConverter = (*gemma2Adapter)(nil)
 
-func (p *gemma2Adapter) KV(baseKV fs.Config) KV {
+func (p *gemma2Adapter) KV(baseKV ggml.KV) ggml.KV {
 	kv := p.AdapterParameters.KV()
 	kv["general.architecture"] = "gemma2"
 	return kv

convert/convert_gemma3.go

@@ -3,6 +3,8 @@ package convert
 import (
 	"cmp"
 	"slices"
+
+	"github.com/ollama/ollama/fs/ggml"
 )
 
 type gemma3Model struct {
@@ -53,7 +55,7 @@ const (
 	gemma27BLayerCount = 62
 )
 
-func (p *gemma3Model) KV(t *Tokenizer) KV {
+func (p *gemma3Model) KV(t *Tokenizer) ggml.KV {
 	kv := p.ModelParameters.KV(t)
 	kv["general.architecture"] = "gemma3"
 

convert/convert_gemma3n.go

@@ -38,7 +38,7 @@ type gemma3nModel struct {
 	VisionModel struct{} `json:"vision_config"`
 }
 
-func (m *gemma3nModel) KV(t *Tokenizer) KV {
+func (m *gemma3nModel) KV(t *Tokenizer) ggml.KV {
 	kv := m.ModelParameters.KV(t)
 	kv["general.architecture"] = "gemma3n"
 	kv["gemma3n.activation_sparsity_scale"] = slices.Collect(func(yield func(float32) bool) {

convert/convert_gptoss.go

@@ -37,7 +37,7 @@ type gptossModel struct {
 
 var _ ModelConverter = (*gptossModel)(nil)
 
-func (m *gptossModel) KV(t *Tokenizer) KV {
+func (m *gptossModel) KV(t *Tokenizer) ggml.KV {
 	kv := m.ModelParameters.KV(t)
 	kv["general.architecture"] = "gptoss"
 	kv["general.file_type"] = uint32(4)

convert/convert_llama.go

@@ -48,7 +48,7 @@ type llamaModel struct {
 
 var _ ModelConverter = (*llamaModel)(nil)
 
-func (p *llamaModel) KV(t *Tokenizer) KV {
+func (p *llamaModel) KV(t *Tokenizer) ggml.KV {
 	kv := p.ModelParameters.KV(t)
 	kv["general.architecture"] = "llama"
 	kv["llama.vocab_size"] = p.VocabSize

convert/convert_llama4.go

@@ -35,7 +35,7 @@ type llama4Model struct {
 }
 
 // KV implements ModelConverter.
-func (p *llama4Model) KV(t *Tokenizer) KV {
+func (p *llama4Model) KV(t *Tokenizer) ggml.KV {
 	kv := p.ModelParameters.KV(t)
 	kv["general.architecture"] = "llama4"
 

convert/convert_llama_adapter.go

@@ -7,7 +7,6 @@ import (
 	"github.com/pdevine/tensor"
 	"github.com/pdevine/tensor/native"
 
-	"github.com/ollama/ollama/fs"
 	"github.com/ollama/ollama/fs/ggml"
 )
 
@@ -19,13 +18,13 @@ type llamaAdapter struct {
 
 var _ AdapterConverter = (*llamaAdapter)(nil)
 
-func (p *llamaAdapter) KV(baseKV fs.Config) KV {
+func (p *llamaAdapter) KV(baseKV ggml.KV) ggml.KV {
 	kv := p.AdapterParameters.KV()
 	kv["general.architecture"] = "llama"
-	kv["llama.attention.head_count"] = baseKV.Value("llama.attention.head_count")
-	kv["llama.attention.head_count_kv"] = baseKV.Value("llama.attention.head_count_kv")
+	kv["llama.attention.head_count"] = baseKV["llama.attention.head_count"]
+	kv["llama.attention.head_count_kv"] = baseKV["llama.attention.head_count_kv"]
 
-	p.NumAttentionHeads = baseKV.Value("llama.attention.head_count").(uint32)
+	p.NumAttentionHeads = baseKV["llama.attention.head_count"].(uint32)
 
 	return kv
 }

convert/convert_mistral.go

@@ -60,7 +60,7 @@ type mistral3Model struct {
 	ProjectorHiddenAct      string `json:"projector_hidden_act"`
 }
 
-func (p *mistral3Model) KV(t *Tokenizer) KV {
+func (p *mistral3Model) KV(t *Tokenizer) ggml.KV {
 	kv := p.ModelParameters.KV(t)
 	kv["general.architecture"] = "mistral3"
 	kv["mistral3.vocab_size"] = p.TextModel.VocabSize

convert/convert_mistral_causal.go

@@ -39,7 +39,7 @@ type mistral3CausalModel struct {
 	} `json:"rope_parameters"`
 }
 
-func (p *mistral3CausalModel) KV(t *Tokenizer) KV {
+func (p *mistral3CausalModel) KV(t *Tokenizer) ggml.KV {
 	kv := p.ModelParameters.KV(t)
 	kv["general.architecture"] = "mistral3"
 	kv["mistral3.vocab_size"] = p.VocabSize

convert/convert_mixtral.go

@@ -12,7 +12,7 @@ type mixtralModel struct {
 	NumExpertsPerToken uint32 `json:"num_experts_per_tok"`
 }
 
-func (p *mixtralModel) KV(t *Tokenizer) KV {
+func (p *mixtralModel) KV(t *Tokenizer) ggml.KV {
 	kv := p.llamaModel.KV(t)
 
 	if p.NumLocalExperts > 0 {

convert/convert_mllama.go

@@ -34,7 +34,7 @@ type mllamaModel struct {
 	} `json:"vision_config"`
 }
 
-func (m *mllamaModel) KV(t *Tokenizer) KV {
+func (m *mllamaModel) KV(t *Tokenizer) ggml.KV {
 	kv := m.ModelParameters.KV(t)
 	kv["general.architecture"] = "mllama"
 

convert/convert_nomicbert.go

@@ -87,7 +87,7 @@ func (p *nomicbertModel) parseMore(fsys fs.FS) error {
 	return nil
 }
 
-func (p *nomicbertModel) KV(t *Tokenizer) KV {
+func (p *nomicbertModel) KV(t *Tokenizer) ggml.KV {
 	kv := p.ModelParameters.KV(t)
 
 	// Determine architecture based on MoE parameters (following qwen3 pattern)

convert/convert_olmo.go

@@ -34,7 +34,7 @@ type olmoModel struct {
 
 var _ ModelConverter = (*olmoModel)(nil)
 
-func (p *olmoModel) KV(t *Tokenizer) KV {
+func (p *olmoModel) KV(t *Tokenizer) ggml.KV {
 	kv := p.ModelParameters.KV(t)
 	kv["general.architecture"] = "olmo3"
 	kv["olmo3.block_count"] = p.NumHiddenLayers

convert/convert_phi3.go

@@ -37,7 +37,7 @@ type phi3Model struct {
 
 var _ ModelConverter = (*phi3Model)(nil)
 
-func (p *phi3Model) KV(t *Tokenizer) KV {
+func (p *phi3Model) KV(t *Tokenizer) ggml.KV {
 	kv := p.ModelParameters.KV(t)
 	kv["general.architecture"] = "phi3"
 	kv["phi3.context_length"] = p.MaxPositionEmbeddings

convert/convert_qwen2.go

@@ -22,7 +22,7 @@ type qwen2Model struct {
 
 var _ ModelConverter = (*qwen2Model)(nil)
 
-func (q *qwen2Model) KV(t *Tokenizer) KV {
+func (q *qwen2Model) KV(t *Tokenizer) ggml.KV {
 	kv := q.ModelParameters.KV(t)
 	kv["general.architecture"] = "qwen2"
 	kv["qwen2.block_count"] = q.HiddenLayers

convert/convert_qwen25vl.go

@@ -29,7 +29,7 @@ type qwen25VLModel struct {
 
 var _ ModelConverter = (*qwen25VLModel)(nil)
 
-func (q *qwen25VLModel) KV(t *Tokenizer) KV {
+func (q *qwen25VLModel) KV(t *Tokenizer) ggml.KV {
 	kv := q.ModelParameters.KV(t)
 	kv["general.architecture"] = "qwen25vl"
 

convert/convert_qwen3.go

@@ -32,7 +32,7 @@ type qwen3Model struct {
 }
 
 // KV implements ModelConverter.
-func (q *qwen3Model) KV(t *Tokenizer) KV {
+func (q *qwen3Model) KV(t *Tokenizer) ggml.KV {
 	arch := "qwen3"
 	if q.NumExperts > 0 {
 		arch += "moe"

convert/convert_qwen3vl.go

@@ -45,7 +45,7 @@ func (m *qwen3VLModel) parseMore(fsys fs.FS) error {
 	return json.Unmarshal(bts, &m.VisionModel)
 }
 
-func (m *qwen3VLModel) KV(t *Tokenizer) KV {
+func (m *qwen3VLModel) KV(t *Tokenizer) ggml.KV {
 	kv := m.qwen3Model.KV(t)
 
 	arch := "qwen3vl"

convert/convert_test.go

@@ -19,7 +19,6 @@ import (
 	"testing"
 
 	"github.com/google/go-cmp/cmp"
-	fsc "github.com/ollama/ollama/fs"
 	"github.com/ollama/ollama/fs/ggml"
 )
 
@@ -29,7 +28,7 @@ type tensorData struct {
 	Shape   []int  `json:"shape"`
 }
 
-func convertFull(t *testing.T, fsys fs.FS) (*os.File, fsc.Config, ggml.Tensors) {
+func convertFull(t *testing.T, fsys fs.FS) (*os.File, ggml.KV, ggml.Tensors) {
 	t.Helper()
 
 	f, err := os.CreateTemp(t.TempDir(), "f16")
@@ -60,10 +59,9 @@ func convertFull(t *testing.T, fsys fs.FS) (*os.File, fsc.Config, ggml.Tensors)
 	return r, m.KV(), m.Tensors()
 }
 
-func generateResultsJSON(t *testing.T, f *os.File, kv fsc.Config, tensors ggml.Tensors) map[string]string {
+func generateResultsJSON(t *testing.T, f *os.File, kv ggml.KV, tensors ggml.Tensors) map[string]string {
 	actual := make(map[string]string)
-	for k := range kv.Keys() {
-		v := kv.Value(k)
+	for k, v := range kv {
 		if s, ok := v.(json.Marshaler); !ok {
 			actual[k] = fmt.Sprintf("%v", v)
 		} else {
@@ -279,7 +277,7 @@ func generateSafetensorTestData(t *testing.T, tempDir string, tensorData map[str
 func TestConvertAdapter(t *testing.T) {
 	type AdapterCase struct {
 		Name     string
-		BaseKV   KV
+		BaseKV   map[string]any
 		Expected map[string]string
 	}
 

docs/README.md

@@ -14,7 +14,6 @@
 * [API Reference](https://docs.ollama.com/api)
 * [Modelfile Reference](https://docs.ollama.com/modelfile)
 * [OpenAI Compatibility](https://docs.ollama.com/api/openai-compatibility)
-* [Anthropic Compatibility](./api/anthropic-compatibility.mdx)
 
 ### Resources
 

docs/api/anthropic-compatibility.mdx

@@ -1,406 +0,0 @@
----
-title: Anthropic compatibility
----
-
-Ollama provides compatibility with the [Anthropic Messages API](https://docs.anthropic.com/en/api/messages) to help connect existing applications to Ollama, including tools like Claude Code.
-
-## Recommended models
-
-For coding use cases, models like `glm-4.7:cloud`, `minimax-m2.1:cloud`, and `qwen3-coder` are recommended.
-
-Pull a model before use:
-```shell
-ollama pull qwen3-coder
-ollama pull glm-4.7:cloud
-```
-
-## Usage
-
-### Environment variables
-
-To use Ollama with tools that expect the Anthropic API (like Claude Code), set these environment variables:
-
-```shell
-export ANTHROPIC_BASE_URL=http://localhost:11434
-export ANTHROPIC_API_KEY=ollama  # required but ignored
-```
-
-### Simple `/v1/messages` example
-
-<CodeGroup dropdown>
-
-```python basic.py
-import anthropic
-
-client = anthropic.Anthropic(
-    base_url='http://localhost:11434',
-    api_key='ollama',  # required but ignored
-)
-
-message = client.messages.create(
-    model='qwen3-coder',
-    max_tokens=1024,
-    messages=[
-        {'role': 'user', 'content': 'Hello, how are you?'}
-    ]
-)
-print(message.content[0].text)
-```
-
-```javascript basic.js
-import Anthropic from "@anthropic-ai/sdk";
-
-const anthropic = new Anthropic({
-  baseURL: "http://localhost:11434",
-  apiKey: "ollama", // required but ignored
-});
-
-const message = await anthropic.messages.create({
-  model: "qwen3-coder",
-  max_tokens: 1024,
-  messages: [{ role: "user", content: "Hello, how are you?" }],
-});
-
-console.log(message.content[0].text);
-```
-
-```shell basic.sh
-curl -X POST http://localhost:11434/v1/messages \
--H "Content-Type: application/json" \
--H "x-api-key: ollama" \
--H "anthropic-version: 2023-06-01" \
--d '{
-  "model": "qwen3-coder",
-  "max_tokens": 1024,
-  "messages": [{ "role": "user", "content": "Hello, how are you?" }]
-}'
-```
-
-</CodeGroup>
-
-### Streaming example
-
-<CodeGroup dropdown>
-
-```python streaming.py
-import anthropic
-
-client = anthropic.Anthropic(
-    base_url='http://localhost:11434',
-    api_key='ollama',
-)
-
-with client.messages.stream(
-    model='qwen3-coder',
-    max_tokens=1024,
-    messages=[{'role': 'user', 'content': 'Count from 1 to 10'}]
-) as stream:
-    for text in stream.text_stream:
-        print(text, end='', flush=True)
-```
-
-```javascript streaming.js
-import Anthropic from "@anthropic-ai/sdk";
-
-const anthropic = new Anthropic({
-  baseURL: "http://localhost:11434",
-  apiKey: "ollama",
-});
-
-const stream = await anthropic.messages.stream({
-  model: "qwen3-coder",
-  max_tokens: 1024,
-  messages: [{ role: "user", content: "Count from 1 to 10" }],
-});
-
-for await (const event of stream) {
-  if (
-    event.type === "content_block_delta" &&
-    event.delta.type === "text_delta"
-  ) {
-    process.stdout.write(event.delta.text);
-  }
-}
-```
-
-```shell streaming.sh
-curl -X POST http://localhost:11434/v1/messages \
--H "Content-Type: application/json" \
--d '{
-  "model": "qwen3-coder",
-  "max_tokens": 1024,
-  "stream": true,
-  "messages": [{ "role": "user", "content": "Count from 1 to 10" }]
-}'
-```
-
-</CodeGroup>
-
-### Tool calling example
-
-<CodeGroup dropdown>
-
-```python tools.py
-import anthropic
-
-client = anthropic.Anthropic(
-    base_url='http://localhost:11434',
-    api_key='ollama',
-)
-
-message = client.messages.create(
-    model='qwen3-coder',
-    max_tokens=1024,
-    tools=[
-        {
-            'name': 'get_weather',
-            'description': 'Get the current weather in a location',
-            'input_schema': {
-                'type': 'object',
-                'properties': {
-                    'location': {
-                        'type': 'string',
-                        'description': 'The city and state, e.g. San Francisco, CA'
-                    }
-                },
-                'required': ['location']
-            }
-        }
-    ],
-    messages=[{'role': 'user', 'content': "What's the weather in San Francisco?"}]
-)
-
-for block in message.content:
-    if block.type == 'tool_use':
-        print(f'Tool: {block.name}')
-        print(f'Input: {block.input}')
-```
-
-```javascript tools.js
-import Anthropic from "@anthropic-ai/sdk";
-
-const anthropic = new Anthropic({
-  baseURL: "http://localhost:11434",
-  apiKey: "ollama",
-});
-
-const message = await anthropic.messages.create({
-  model: "qwen3-coder",
-  max_tokens: 1024,
-  tools: [
-    {
-      name: "get_weather",
-      description: "Get the current weather in a location",
-      input_schema: {
-        type: "object",
-        properties: {
-          location: {
-            type: "string",
-            description: "The city and state, e.g. San Francisco, CA",
-          },
-        },
-        required: ["location"],
-      },
-    },
-  ],
-  messages: [{ role: "user", content: "What's the weather in San Francisco?" }],
-});
-
-for (const block of message.content) {
-  if (block.type === "tool_use") {
-    console.log("Tool:", block.name);
-    console.log("Input:", block.input);
-  }
-}
-```
-
-```shell tools.sh
-curl -X POST http://localhost:11434/v1/messages \
--H "Content-Type: application/json" \
--d '{
-  "model": "qwen3-coder",
-  "max_tokens": 1024,
-  "tools": [
-    {
-      "name": "get_weather",
-      "description": "Get the current weather in a location",
-      "input_schema": {
-        "type": "object",
-        "properties": {
-          "location": {
-            "type": "string",
-            "description": "The city and state"
-          }
-        },
-        "required": ["location"]
-      }
-    }
-  ],
-  "messages": [{ "role": "user", "content": "What is the weather in San Francisco?" }]
-}'
-```
-
-</CodeGroup>
-
-## Using with Claude Code
-
-[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
-```
-
-Or set the environment variables in your shell profile:
-
-```shell
-export ANTHROPIC_BASE_URL=http://localhost:11434
-export ANTHROPIC_API_KEY=ollama
-```
-
-Then run Claude Code with any Ollama model:
-
-```shell
-# Local models
-claude --model qwen3-coder
-claude --model gpt-oss:20b
-
-# Cloud models
-claude --model glm-4.7:cloud
-claude --model minimax-m2.1:cloud
-```
-
-## Endpoints
-
-### `/v1/messages`
-
-#### Supported features
-
-- [x] Messages
-- [x] Streaming
-- [x] System prompts
-- [x] Multi-turn conversations
-- [x] Vision (images)
-- [x] Tools (function calling)
-- [x] Tool results
-- [x] Thinking/extended thinking
-
-#### Supported request fields
-
-- [x] `model`
-- [x] `max_tokens`
-- [x] `messages`
-  - [x] Text `content`
-  - [x] Image `content` (base64)
-  - [x] Array of content blocks
-  - [x] `tool_use` blocks
-  - [x] `tool_result` blocks
-  - [x] `thinking` blocks
-- [x] `system` (string or array)
-- [x] `stream`
-- [x] `temperature`
-- [x] `top_p`
-- [x] `top_k`
-- [x] `stop_sequences`
-- [x] `tools`
-- [x] `thinking`
-- [ ] `tool_choice`
-- [ ] `metadata`
-
-#### Supported response fields
-
-- [x] `id`
-- [x] `type`
-- [x] `role`
-- [x] `model`
-- [x] `content` (text, tool_use, thinking blocks)
-- [x] `stop_reason` (end_turn, max_tokens, tool_use)
-- [x] `usage` (input_tokens, output_tokens)
-
-#### Streaming events
-
-- [x] `message_start`
-- [x] `content_block_start`
-- [x] `content_block_delta` (text_delta, input_json_delta, thinking_delta)
-- [x] `content_block_stop`
-- [x] `message_delta`
-- [x] `message_stop`
-- [x] `ping`
-- [x] `error`
-
-## Models
-
-Ollama supports both local and cloud models.
-
-### Local models
-
-Pull a local model before use:
-
-```shell
-ollama pull qwen3-coder
-```
-
-Recommended local models:
-- `qwen3-coder` - Excellent for coding tasks
-- `gpt-oss:20b` - Strong general-purpose model
-
-### Cloud models
-
-Cloud models are available immediately without pulling:
-
-- `glm-4.7:cloud` - High-performance cloud model
-- `minimax-m2.1:cloud` - Fast cloud model
-
-### Default model names
-
-For tooling that relies on default Anthropic model names such as `claude-3-5-sonnet`, use `ollama cp` to copy an existing model name:
-
-```shell
-ollama cp qwen3-coder claude-3-5-sonnet
-```
-
-Afterwards, this new model name can be specified in the `model` field:
-
-```shell
-curl http://localhost:11434/v1/messages \
-    -H "Content-Type: application/json" \
-    -d '{
-        "model": "claude-3-5-sonnet",
-        "max_tokens": 1024,
-        "messages": [
-            {
-                "role": "user",
-                "content": "Hello!"
-            }
-        ]
-    }'
-```
-
-## Differences from the Anthropic API
-
-### Behavior differences
-
-- API key is accepted but not validated
-- `anthropic-version` header is accepted but not used
-- Token counts are approximations based on the underlying model's tokenizer
-
-### Not supported
-
-The following Anthropic API features are not currently supported:
-
-| Feature | Description |
-|---------|-------------|
-| `/v1/messages/count_tokens` | Token counting endpoint |
-| `tool_choice` | Forcing specific tool use or disabling tools |
-| `metadata` | Request metadata (user_id) |
-| Prompt caching | `cache_control` blocks for caching prefixes |
-| Batches API | `/v1/messages/batches` for async batch processing |
-| Citations | `citations` content blocks |
-| PDF support | `document` content blocks with PDF files |
-| Server-sent errors | `error` events during streaming (errors return HTTP status) |
-
-### Partial support
-
-| Feature | Status |
-|---------|--------|
-| Image content | Base64 images supported; URL images not supported |
-| Extended thinking | Basic support; `budget_tokens` accepted but not enforced |

docs/docs.json

@@ -32,9 +32,7 @@
     "codeblocks": "system"
   },
   "contextual": {
-    "options": [
-      "copy"
-    ]
+    "options": ["copy"]
   },
   "navbar": {
     "links": [
@@ -54,9 +52,7 @@
       "display": "simple"
     },
     "examples": {
-      "languages": [
-        "curl"
-      ]
+      "languages": ["curl"]
     }
   },
   "redirects": [
@@ -101,7 +97,6 @@
           {
             "group": "Integrations",
             "pages": [
-              "/integrations/claude-code",
               "/integrations/vscode",
               "/integrations/jetbrains",
               "/integrations/codex",
@@ -144,8 +139,7 @@
               "/api/streaming",
               "/api/usage",
               "/api/errors",
-              "/api/openai-compatibility",
-              "/api/anthropic-compatibility"
+              "/api/openai-compatibility"
             ]
           },
           {

docs/integrations/claude-code.mdx

@@ -1,69 +0,0 @@
----
-title: Claude Code
----
-
-## Install
-
-Install [Claude Code](https://code.claude.com/docs/en/overview):
-
-<CodeGroup>
-
-```shell macOS / Linux
-curl -fsSL https://claude.ai/install.sh | bash
-```
-
-```powershell Windows
-irm https://claude.ai/install.ps1 | iex
-```
-
-</CodeGroup>
-
-## Usage with Ollama
-
-Claude Code connects to Ollama using the Anthropic-compatible API.
-
-1. Set the environment variables:
-
-```shell
-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
-```
-
-Or run with environment variables inline:
-
-```shell
-ANTHROPIC_BASE_URL=http://localhost:11434 ANTHROPIC_API_KEY=ollama claude --model qwen3-coder
-```
-
-## Connecting to ollama.com
-
-1. Create an [API key](https://ollama.com/settings/keys) on ollama.com
-2. Set the environment variables:
-
-```shell
-export ANTHROPIC_BASE_URL=https://ollama.com
-export ANTHROPIC_API_KEY=<your-api-key>
-```
-
-3. Run Claude Code with a cloud model:
-
-```shell
-claude --model glm-4.7:cloud
-```
-
-## Recommended Models
-
-### Cloud models
-- `glm-4.7:cloud` - High-performance cloud model
-- `minimax-m2.1:cloud` - Fast cloud model
-- `qwen3-coder:480b` - Large coding model
-
-### Local models
-- `qwen3-coder` - Excellent for coding tasks
-- `gpt-oss:20b` - Strong general-purpose model

docs/linux.mdx

@@ -1,5 +1,5 @@
 ---
-title: "Linux"
+title: Linux
 ---
 
 ## Install
@@ -13,7 +13,8 @@ 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:
@@ -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
@@ -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
-```
+```

fs/config.go

@@ -1,7 +1,5 @@
 package fs
 
-import "iter"
-
 type Config interface {
 	Architecture() string
 	String(string, ...string) string
@@ -13,8 +11,4 @@ type Config interface {
 	Ints(string, ...[]int32) []int32
 	Floats(string, ...[]float32) []float32
 	Bools(string, ...[]bool) []bool
-
-	Len() int
-	Keys() iter.Seq[string]
-	Value(key string) any
 }

fs/ggml/ggml.go

@@ -6,9 +6,7 @@ import (
 	"errors"
 	"fmt"
 	"io"
-	"iter"
 	"log/slog"
-	"maps"
 	"math"
 	"slices"
 	"strings"
@@ -241,18 +239,6 @@ func (kv KV) Bools(key string, defaultValue ...[]bool) []bool {
 	return val.values
 }
 
-func (kv KV) Len() int {
-	return len(kv)
-}
-
-func (kv KV) Keys() iter.Seq[string] {
-	return maps.Keys(kv)
-}
-
-func (kv KV) Value(key string) any {
-	return kv[key]
-}
-
 func (kv KV) OllamaEngineRequired() bool {
 	return slices.Contains([]string{
 		"bert",

fs/ggml/gguf.go

@@ -8,12 +8,12 @@ import (
 	"fmt"
 	"io"
 	"log/slog"
+	"maps"
 	"os"
 	"runtime"
 	"slices"
 	"strings"
 
-	"github.com/ollama/ollama/fs"
 	"golang.org/x/sync/errgroup"
 )
 
@@ -508,7 +508,7 @@ func writeGGUFArray[S ~[]E, E any](w io.Writer, t uint32, s S) error {
 	return binary.Write(w, binary.LittleEndian, s)
 }
 
-func WriteGGUF(f *os.File, kv fs.Config, ts []*Tensor) error {
+func WriteGGUF(f *os.File, kv KV, ts []*Tensor) error {
 	arch := kv.String("general.architecture")
 	if arch == "" {
 		return fmt.Errorf("architecture not set")
@@ -526,12 +526,12 @@ func WriteGGUF(f *os.File, kv fs.Config, ts []*Tensor) error {
 		return err
 	}
 
-	if err := binary.Write(f, binary.LittleEndian, uint64(kv.Len())); err != nil {
+	if err := binary.Write(f, binary.LittleEndian, uint64(len(kv))); err != nil {
 		return err
 	}
 
-	for _, key := range slices.Sorted(kv.Keys()) {
-		if err := ggufWriteKV(f, arch, key, kv.Value(key)); err != nil {
+	for _, key := range slices.Sorted(maps.Keys(kv)) {
+		if err := ggufWriteKV(f, arch, key, kv[key]); err != nil {
 			return err
 		}
 	}

middleware/anthropic.go

@@ -1,149 +0,0 @@
-package middleware
-
-import (
-	"bytes"
-	"encoding/json"
-	"fmt"
-	"io"
-	"net/http"
-
-	"github.com/gin-gonic/gin"
-
-	"github.com/ollama/ollama/anthropic"
-	"github.com/ollama/ollama/api"
-)
-
-// AnthropicWriter wraps the response writer to transform Ollama responses to Anthropic format
-type AnthropicWriter struct {
-	BaseWriter
-	stream    bool
-	id        string
-	model     string
-	converter *anthropic.StreamConverter
-}
-
-func (w *AnthropicWriter) writeError(data []byte) (int, error) {
-	var errData struct {
-		Error string `json:"error"`
-	}
-	if err := json.Unmarshal(data, &errData); err != nil {
-		return 0, err
-	}
-
-	w.ResponseWriter.Header().Set("Content-Type", "application/json")
-	err := json.NewEncoder(w.ResponseWriter).Encode(anthropic.NewError(w.ResponseWriter.Status(), errData.Error))
-	if err != nil {
-		return 0, err
-	}
-
-	return len(data), nil
-}
-
-func (w *AnthropicWriter) writeEvent(eventType string, data any) error {
-	d, err := json.Marshal(data)
-	if err != nil {
-		return err
-	}
-	_, err = w.ResponseWriter.Write([]byte(fmt.Sprintf("event: %s\ndata: %s\n\n", eventType, d)))
-	if err != nil {
-		return err
-	}
-	if f, ok := w.ResponseWriter.(http.Flusher); ok {
-		f.Flush()
-	}
-	return nil
-}
-
-func (w *AnthropicWriter) writeResponse(data []byte) (int, error) {
-	var chatResponse api.ChatResponse
-	err := json.Unmarshal(data, &chatResponse)
-	if err != nil {
-		return 0, err
-	}
-
-	if w.stream {
-		w.ResponseWriter.Header().Set("Content-Type", "text/event-stream")
-
-		events := w.converter.Process(chatResponse)
-		for _, event := range events {
-			if err := w.writeEvent(event.Event, event.Data); err != nil {
-				return 0, err
-			}
-		}
-		return len(data), nil
-	}
-
-	w.ResponseWriter.Header().Set("Content-Type", "application/json")
-	response := anthropic.ToMessagesResponse(w.id, chatResponse)
-	return len(data), json.NewEncoder(w.ResponseWriter).Encode(response)
-}
-
-func (w *AnthropicWriter) Write(data []byte) (int, error) {
-	code := w.ResponseWriter.Status()
-	if code != http.StatusOK {
-		return w.writeError(data)
-	}
-
-	return w.writeResponse(data)
-}
-
-// AnthropicMessagesMiddleware handles Anthropic Messages API requests
-func AnthropicMessagesMiddleware() gin.HandlerFunc {
-	return func(c *gin.Context) {
-		var req anthropic.MessagesRequest
-		err := c.ShouldBindJSON(&req)
-		if err != nil {
-			c.AbortWithStatusJSON(http.StatusBadRequest, anthropic.NewError(http.StatusBadRequest, err.Error()))
-			return
-		}
-
-		if req.Model == "" {
-			c.AbortWithStatusJSON(http.StatusBadRequest, anthropic.NewError(http.StatusBadRequest, "model is required"))
-			return
-		}
-
-		if req.MaxTokens <= 0 {
-			c.AbortWithStatusJSON(http.StatusBadRequest, anthropic.NewError(http.StatusBadRequest, "max_tokens is required and must be positive"))
-			return
-		}
-
-		if len(req.Messages) == 0 {
-			c.AbortWithStatusJSON(http.StatusBadRequest, anthropic.NewError(http.StatusBadRequest, "messages is required"))
-			return
-		}
-
-		chatReq, err := anthropic.FromMessagesRequest(req)
-		if err != nil {
-			c.AbortWithStatusJSON(http.StatusBadRequest, anthropic.NewError(http.StatusBadRequest, err.Error()))
-			return
-		}
-
-		var b bytes.Buffer
-		if err := json.NewEncoder(&b).Encode(chatReq); err != nil {
-			c.AbortWithStatusJSON(http.StatusInternalServerError, anthropic.NewError(http.StatusInternalServerError, err.Error()))
-			return
-		}
-
-		c.Request.Body = io.NopCloser(&b)
-
-		messageID := anthropic.GenerateMessageID()
-
-		w := &AnthropicWriter{
-			BaseWriter: BaseWriter{ResponseWriter: c.Writer},
-			stream:     req.Stream,
-			id:         messageID,
-			model:      req.Model,
-			converter:  anthropic.NewStreamConverter(messageID, req.Model),
-		}
-
-		if req.Stream {
-			c.Writer.Header().Set("Content-Type", "text/event-stream")
-			c.Writer.Header().Set("Cache-Control", "no-cache")
-			c.Writer.Header().Set("Connection", "keep-alive")
-		}
-
-		c.Writer = w
-
-		c.Next()
-	}
-}

middleware/anthropic_test.go

@@ -1,584 +0,0 @@
-package middleware
-
-import (
-	"bytes"
-	"encoding/json"
-	"io"
-	"net/http"
-	"net/http/httptest"
-	"strings"
-	"testing"
-
-	"github.com/gin-gonic/gin"
-	"github.com/google/go-cmp/cmp"
-	"github.com/google/go-cmp/cmp/cmpopts"
-
-	"github.com/ollama/ollama/anthropic"
-	"github.com/ollama/ollama/api"
-)
-
-func captureAnthropicRequest(capturedRequest any) gin.HandlerFunc {
-	return func(c *gin.Context) {
-		bodyBytes, _ := io.ReadAll(c.Request.Body)
-		c.Request.Body = io.NopCloser(bytes.NewReader(bodyBytes))
-		_ = json.Unmarshal(bodyBytes, capturedRequest)
-		c.Next()
-	}
-}
-
-// testProps creates ToolPropertiesMap from a map (convenience function for tests)
-func testProps(m map[string]api.ToolProperty) *api.ToolPropertiesMap {
-	props := api.NewToolPropertiesMap()
-	for k, v := range m {
-		props.Set(k, v)
-	}
-	return props
-}
-
-func TestAnthropicMessagesMiddleware(t *testing.T) {
-	type testCase struct {
-		name string
-		body string
-		req  api.ChatRequest
-		err  anthropic.ErrorResponse
-	}
-
-	var capturedRequest *api.ChatRequest
-	stream := true
-
-	testCases := []testCase{
-		{
-			name: "basic message",
-			body: `{
-				"model": "test-model",
-				"max_tokens": 1024,
-				"messages": [
-					{"role": "user", "content": "Hello"}
-				]
-			}`,
-			req: api.ChatRequest{
-				Model: "test-model",
-				Messages: []api.Message{
-					{Role: "user", Content: "Hello"},
-				},
-				Options: map[string]any{"num_predict": 1024},
-				Stream:  &False,
-			},
-		},
-		{
-			name: "with system prompt",
-			body: `{
-				"model": "test-model",
-				"max_tokens": 1024,
-				"system": "You are helpful.",
-				"messages": [
-					{"role": "user", "content": "Hello"}
-				]
-			}`,
-			req: api.ChatRequest{
-				Model: "test-model",
-				Messages: []api.Message{
-					{Role: "system", Content: "You are helpful."},
-					{Role: "user", Content: "Hello"},
-				},
-				Options: map[string]any{"num_predict": 1024},
-				Stream:  &False,
-			},
-		},
-		{
-			name: "with options",
-			body: `{
-				"model": "test-model",
-				"max_tokens": 2048,
-				"temperature": 0.7,
-				"top_p": 0.9,
-				"top_k": 40,
-				"stop_sequences": ["\n", "END"],
-				"messages": [
-					{"role": "user", "content": "Hello"}
-				]
-			}`,
-			req: api.ChatRequest{
-				Model: "test-model",
-				Messages: []api.Message{
-					{Role: "user", Content: "Hello"},
-				},
-				Options: map[string]any{
-					"num_predict": 2048,
-					"temperature": 0.7,
-					"top_p":       0.9,
-					"top_k":       40,
-					"stop":        []string{"\n", "END"},
-				},
-				Stream: &False,
-			},
-		},
-		{
-			name: "streaming",
-			body: `{
-				"model": "test-model",
-				"max_tokens": 1024,
-				"stream": true,
-				"messages": [
-					{"role": "user", "content": "Hello"}
-				]
-			}`,
-			req: api.ChatRequest{
-				Model: "test-model",
-				Messages: []api.Message{
-					{Role: "user", Content: "Hello"},
-				},
-				Options: map[string]any{"num_predict": 1024},
-				Stream:  &stream,
-			},
-		},
-		{
-			name: "with tools",
-			body: `{
-				"model": "test-model",
-				"max_tokens": 1024,
-				"messages": [
-					{"role": "user", "content": "What's the weather?"}
-				],
-				"tools": [{
-					"name": "get_weather",
-					"description": "Get current weather",
-					"input_schema": {
-						"type": "object",
-						"properties": {
-							"location": {"type": "string"}
-						},
-						"required": ["location"]
-					}
-				}]
-			}`,
-			req: api.ChatRequest{
-				Model: "test-model",
-				Messages: []api.Message{
-					{Role: "user", Content: "What's the weather?"},
-				},
-				Tools: []api.Tool{
-					{
-						Type: "function",
-						Function: api.ToolFunction{
-							Name:        "get_weather",
-							Description: "Get current weather",
-							Parameters: api.ToolFunctionParameters{
-								Type:     "object",
-								Required: []string{"location"},
-								Properties: testProps(map[string]api.ToolProperty{
-									"location": {Type: api.PropertyType{"string"}},
-								}),
-							},
-						},
-					},
-				},
-				Options: map[string]any{"num_predict": 1024},
-				Stream:  &False,
-			},
-		},
-		{
-			name: "with tool result",
-			body: `{
-				"model": "test-model",
-				"max_tokens": 1024,
-				"messages": [
-					{"role": "user", "content": "What's the weather?"},
-					{"role": "assistant", "content": [
-						{"type": "tool_use", "id": "call_123", "name": "get_weather", "input": {"location": "Paris"}}
-					]},
-					{"role": "user", "content": [
-						{"type": "tool_result", "tool_use_id": "call_123", "content": "Sunny, 22°C"}
-					]}
-				]
-			}`,
-			req: api.ChatRequest{
-				Model: "test-model",
-				Messages: []api.Message{
-					{Role: "user", Content: "What's the weather?"},
-					{
-						Role: "assistant",
-						ToolCalls: []api.ToolCall{
-							{
-								ID: "call_123",
-								Function: api.ToolCallFunction{
-									Name:      "get_weather",
-									Arguments: testArgs(map[string]any{"location": "Paris"}),
-								},
-							},
-						},
-					},
-					{Role: "tool", Content: "Sunny, 22°C", ToolCallID: "call_123"},
-				},
-				Options: map[string]any{"num_predict": 1024},
-				Stream:  &False,
-			},
-		},
-		{
-			name: "with thinking enabled",
-			body: `{
-				"model": "test-model",
-				"max_tokens": 1024,
-				"thinking": {"type": "enabled", "budget_tokens": 1000},
-				"messages": [
-					{"role": "user", "content": "Hello"}
-				]
-			}`,
-			req: api.ChatRequest{
-				Model: "test-model",
-				Messages: []api.Message{
-					{Role: "user", Content: "Hello"},
-				},
-				Options: map[string]any{"num_predict": 1024},
-				Stream:  &False,
-				Think:   &api.ThinkValue{Value: true},
-			},
-		},
-		{
-			name: "missing model error",
-			body: `{
-				"max_tokens": 1024,
-				"messages": [
-					{"role": "user", "content": "Hello"}
-				]
-			}`,
-			err: anthropic.ErrorResponse{
-				Type: "error",
-				Error: anthropic.Error{
-					Type:    "invalid_request_error",
-					Message: "model is required",
-				},
-			},
-		},
-		{
-			name: "missing max_tokens error",
-			body: `{
-				"model": "test-model",
-				"messages": [
-					{"role": "user", "content": "Hello"}
-				]
-			}`,
-			err: anthropic.ErrorResponse{
-				Type: "error",
-				Error: anthropic.Error{
-					Type:    "invalid_request_error",
-					Message: "max_tokens is required and must be positive",
-				},
-			},
-		},
-		{
-			name: "missing messages error",
-			body: `{
-				"model": "test-model",
-				"max_tokens": 1024
-			}`,
-			err: anthropic.ErrorResponse{
-				Type: "error",
-				Error: anthropic.Error{
-					Type:    "invalid_request_error",
-					Message: "messages is required",
-				},
-			},
-		},
-		{
-			name: "tool_use missing id error",
-			body: `{
-				"model": "test-model",
-				"max_tokens": 1024,
-				"messages": [
-					{"role": "assistant", "content": [
-						{"type": "tool_use", "name": "test"}
-					]}
-				]
-			}`,
-			err: anthropic.ErrorResponse{
-				Type: "error",
-				Error: anthropic.Error{
-					Type:    "invalid_request_error",
-					Message: "tool_use block missing required 'id' field",
-				},
-			},
-		},
-	}
-
-	endpoint := func(c *gin.Context) {
-		c.Status(http.StatusOK)
-	}
-
-	gin.SetMode(gin.TestMode)
-	router := gin.New()
-	router.Use(AnthropicMessagesMiddleware(), captureAnthropicRequest(&capturedRequest))
-	router.Handle(http.MethodPost, "/v1/messages", endpoint)
-
-	for _, tc := range testCases {
-		t.Run(tc.name, func(t *testing.T) {
-			req, _ := http.NewRequest(http.MethodPost, "/v1/messages", 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.Type != "" {
-				// Expect error
-				if resp.Code == http.StatusOK {
-					t.Fatalf("expected error response, got 200 OK")
-				}
-				var errResp anthropic.ErrorResponse
-				if err := json.Unmarshal(resp.Body.Bytes(), &errResp); err != nil {
-					t.Fatalf("failed to unmarshal error: %v", err)
-				}
-				if errResp.Type != tc.err.Type {
-					t.Errorf("expected error type %q, got %q", tc.err.Type, errResp.Type)
-				}
-				if errResp.Error.Type != tc.err.Error.Type {
-					t.Errorf("expected error.type %q, got %q", tc.err.Error.Type, errResp.Error.Type)
-				}
-				if errResp.Error.Message != tc.err.Error.Message {
-					t.Errorf("expected error.message %q, got %q", tc.err.Error.Message, errResp.Error.Message)
-				}
-				return
-			}
-
-			if resp.Code != http.StatusOK {
-				t.Fatalf("unexpected status code: %d, body: %s", resp.Code, resp.Body.String())
-			}
-
-			if capturedRequest == nil {
-				t.Fatal("request was not captured")
-			}
-
-			// Compare relevant fields
-			if capturedRequest.Model != tc.req.Model {
-				t.Errorf("model mismatch: got %q, want %q", capturedRequest.Model, tc.req.Model)
-			}
-
-			if diff := cmp.Diff(tc.req.Messages, capturedRequest.Messages,
-				cmpopts.IgnoreUnexported(api.ToolCallFunctionArguments{}, api.ToolPropertiesMap{})); diff != "" {
-				t.Errorf("messages mismatch (-want +got):\n%s", diff)
-			}
-
-			if tc.req.Stream != nil && capturedRequest.Stream != nil {
-				if *tc.req.Stream != *capturedRequest.Stream {
-					t.Errorf("stream mismatch: got %v, want %v", *capturedRequest.Stream, *tc.req.Stream)
-				}
-			}
-
-			if tc.req.Think != nil {
-				if capturedRequest.Think == nil {
-					t.Error("expected Think to be set")
-				} else if capturedRequest.Think.Value != tc.req.Think.Value {
-					t.Errorf("Think mismatch: got %v, want %v", capturedRequest.Think.Value, tc.req.Think.Value)
-				}
-			}
-		})
-	}
-}
-
-func TestAnthropicMessagesMiddleware_Headers(t *testing.T) {
-	gin.SetMode(gin.TestMode)
-
-	t.Run("streaming sets correct headers", func(t *testing.T) {
-		router := gin.New()
-		router.Use(AnthropicMessagesMiddleware())
-		router.POST("/v1/messages", func(c *gin.Context) {
-			// Check headers were set
-			if c.Writer.Header().Get("Content-Type") != "text/event-stream" {
-				t.Errorf("expected Content-Type text/event-stream, got %q", c.Writer.Header().Get("Content-Type"))
-			}
-			if c.Writer.Header().Get("Cache-Control") != "no-cache" {
-				t.Errorf("expected Cache-Control no-cache, got %q", c.Writer.Header().Get("Cache-Control"))
-			}
-			c.Status(http.StatusOK)
-		})
-
-		body := `{"model": "test", "max_tokens": 100, "stream": true, "messages": [{"role": "user", "content": "Hi"}]}`
-		req, _ := http.NewRequest(http.MethodPost, "/v1/messages", strings.NewReader(body))
-		req.Header.Set("Content-Type", "application/json")
-
-		resp := httptest.NewRecorder()
-		router.ServeHTTP(resp, req)
-	})
-}
-
-func TestAnthropicMessagesMiddleware_InvalidJSON(t *testing.T) {
-	gin.SetMode(gin.TestMode)
-	router := gin.New()
-	router.Use(AnthropicMessagesMiddleware())
-	router.POST("/v1/messages", func(c *gin.Context) {
-		c.Status(http.StatusOK)
-	})
-
-	req, _ := http.NewRequest(http.MethodPost, "/v1/messages", strings.NewReader(`{invalid json`))
-	req.Header.Set("Content-Type", "application/json")
-
-	resp := httptest.NewRecorder()
-	router.ServeHTTP(resp, req)
-
-	if resp.Code != http.StatusBadRequest {
-		t.Errorf("expected status 400, got %d", resp.Code)
-	}
-
-	var errResp anthropic.ErrorResponse
-	if err := json.Unmarshal(resp.Body.Bytes(), &errResp); err != nil {
-		t.Fatalf("failed to unmarshal error: %v", err)
-	}
-
-	if errResp.Type != "error" {
-		t.Errorf("expected type 'error', got %q", errResp.Type)
-	}
-	if errResp.Error.Type != "invalid_request_error" {
-		t.Errorf("expected error type 'invalid_request_error', got %q", errResp.Error.Type)
-	}
-}
-
-func TestAnthropicWriter_NonStreaming(t *testing.T) {
-	gin.SetMode(gin.TestMode)
-
-	router := gin.New()
-	router.Use(AnthropicMessagesMiddleware())
-	router.POST("/v1/messages", func(c *gin.Context) {
-		// Simulate Ollama response
-		resp := api.ChatResponse{
-			Model: "test-model",
-			Message: api.Message{
-				Role:    "assistant",
-				Content: "Hello there!",
-			},
-			Done:       true,
-			DoneReason: "stop",
-			Metrics: api.Metrics{
-				PromptEvalCount: 10,
-				EvalCount:       5,
-			},
-		}
-		data, _ := json.Marshal(resp)
-		c.Writer.WriteHeader(http.StatusOK)
-		_, _ = c.Writer.Write(data)
-	})
-
-	body := `{"model": "test-model", "max_tokens": 100, "messages": [{"role": "user", "content": "Hi"}]}`
-	req, _ := http.NewRequest(http.MethodPost, "/v1/messages", strings.NewReader(body))
-	req.Header.Set("Content-Type", "application/json")
-
-	resp := httptest.NewRecorder()
-	router.ServeHTTP(resp, req)
-
-	if resp.Code != http.StatusOK {
-		t.Fatalf("expected status 200, got %d", resp.Code)
-	}
-
-	var result anthropic.MessagesResponse
-	if err := json.Unmarshal(resp.Body.Bytes(), &result); err != nil {
-		t.Fatalf("failed to unmarshal response: %v", err)
-	}
-
-	if result.Type != "message" {
-		t.Errorf("expected type 'message', got %q", result.Type)
-	}
-	if result.Role != "assistant" {
-		t.Errorf("expected role 'assistant', got %q", result.Role)
-	}
-	if len(result.Content) != 1 {
-		t.Fatalf("expected 1 content block, got %d", len(result.Content))
-	}
-	if result.Content[0].Text == nil || *result.Content[0].Text != "Hello there!" {
-		t.Errorf("expected text 'Hello there!', got %v", result.Content[0].Text)
-	}
-	if result.StopReason != "end_turn" {
-		t.Errorf("expected stop_reason 'end_turn', got %q", result.StopReason)
-	}
-	if result.Usage.InputTokens != 10 {
-		t.Errorf("expected input_tokens 10, got %d", result.Usage.InputTokens)
-	}
-	if result.Usage.OutputTokens != 5 {
-		t.Errorf("expected output_tokens 5, got %d", result.Usage.OutputTokens)
-	}
-}
-
-// TestAnthropicWriter_ErrorFromRoutes tests error handling when routes.go sends
-// gin.H{"error": "message"} without a StatusCode field (which is the common case)
-func TestAnthropicWriter_ErrorFromRoutes(t *testing.T) {
-	gin.SetMode(gin.TestMode)
-
-	tests := []struct {
-		name          string
-		statusCode    int
-		errorPayload  any
-		wantErrorType string
-		wantMessage   string
-	}{
-		// routes.go sends errors without StatusCode in JSON, so we must use HTTP status
-		{
-			name:          "404 with gin.H error (model not found)",
-			statusCode:    http.StatusNotFound,
-			errorPayload:  gin.H{"error": "model 'nonexistent' not found"},
-			wantErrorType: "not_found_error",
-			wantMessage:   "model 'nonexistent' not found",
-		},
-		{
-			name:          "400 with gin.H error (bad request)",
-			statusCode:    http.StatusBadRequest,
-			errorPayload:  gin.H{"error": "model is required"},
-			wantErrorType: "invalid_request_error",
-			wantMessage:   "model is required",
-		},
-		{
-			name:          "500 with gin.H error (internal error)",
-			statusCode:    http.StatusInternalServerError,
-			errorPayload:  gin.H{"error": "something went wrong"},
-			wantErrorType: "api_error",
-			wantMessage:   "something went wrong",
-		},
-		{
-			name:       "404 with api.StatusError",
-			statusCode: http.StatusNotFound,
-			errorPayload: api.StatusError{
-				StatusCode:   http.StatusNotFound,
-				ErrorMessage: "model not found via StatusError",
-			},
-			wantErrorType: "not_found_error",
-			wantMessage:   "model not found via StatusError",
-		},
-	}
-
-	for _, tt := range tests {
-		t.Run(tt.name, func(t *testing.T) {
-			router := gin.New()
-			router.Use(AnthropicMessagesMiddleware())
-			router.POST("/v1/messages", func(c *gin.Context) {
-				// Simulate what routes.go does - set status and write error JSON
-				data, _ := json.Marshal(tt.errorPayload)
-				c.Writer.WriteHeader(tt.statusCode)
-				_, _ = c.Writer.Write(data)
-			})
-
-			body := `{"model": "test-model", "max_tokens": 100, "messages": [{"role": "user", "content": "Hi"}]}`
-			req, _ := http.NewRequest(http.MethodPost, "/v1/messages", strings.NewReader(body))
-			req.Header.Set("Content-Type", "application/json")
-
-			resp := httptest.NewRecorder()
-			router.ServeHTTP(resp, req)
-
-			if resp.Code != tt.statusCode {
-				t.Errorf("expected status %d, got %d", tt.statusCode, resp.Code)
-			}
-
-			var errResp anthropic.ErrorResponse
-			if err := json.Unmarshal(resp.Body.Bytes(), &errResp); err != nil {
-				t.Fatalf("failed to unmarshal error response: %v\nbody: %s", err, resp.Body.String())
-			}
-
-			if errResp.Type != "error" {
-				t.Errorf("expected type 'error', got %q", errResp.Type)
-			}
-			if errResp.Error.Type != tt.wantErrorType {
-				t.Errorf("expected error type %q, got %q", tt.wantErrorType, errResp.Error.Type)
-			}
-			if errResp.Error.Message != tt.wantMessage {
-				t.Errorf("expected message %q, got %q", tt.wantMessage, errResp.Error.Message)
-			}
-		})
-	}
-}

parser/parser_test.go

@@ -21,7 +21,6 @@ import (
 	"golang.org/x/text/encoding/unicode"
 
 	"github.com/ollama/ollama/api"
-	"github.com/ollama/ollama/convert"
 	"github.com/ollama/ollama/fs/ggml"
 )
 
@@ -802,7 +801,7 @@ func createBinFile(t *testing.T, kv map[string]any, ti []*ggml.Tensor) (string,
 	}
 	defer f.Close()
 
-	var base convert.KV = map[string]any{"general.architecture": "test"}
+	base := map[string]any{"general.architecture": "test"}
 	maps.Copy(base, kv)
 
 	if err := ggml.WriteGGUF(f, base, ti); err != nil {

progress/stepbar.go

@@ -1,33 +0,0 @@
-package progress
-
-import (
-	"fmt"
-	"strings"
-)
-
-// StepBar displays step-based progress (e.g., for image generation steps).
-type StepBar struct {
-	message string
-	current int
-	total   int
-}
-
-func NewStepBar(message string, total int) *StepBar {
-	return &StepBar{message: message, total: total}
-}
-
-func (s *StepBar) Set(current int) {
-	s.current = current
-}
-
-func (s *StepBar) String() string {
-	percent := float64(s.current) / float64(s.total) * 100
-	barWidth := s.total
-	empty := barWidth - s.current
-
-	// "Generating   0% ▕         ▏ 0/9"
-	return fmt.Sprintf("%s %3.0f%% ▕%s%s▏ %d/%d",
-		s.message, percent,
-		strings.Repeat("█", s.current), strings.Repeat(" ", empty),
-		s.current, s.total)
-}

readline/errors.go

@@ -6,6 +6,9 @@ import (
 
 var ErrInterrupt = errors.New("Interrupt")
 
+// ErrExpandOutput is returned when user presses Ctrl+O to expand tool output
+var ErrExpandOutput = errors.New("ExpandOutput")
+
 type InterruptError struct {
 	Line []rune
 }

readline/readline.go

@@ -206,6 +206,9 @@ func (i *Instance) Readline() (string, error) {
 			buf.DeleteBefore()
 		case CharCtrlL:
 			buf.ClearScreen()
+		case CharCtrlO:
+			// Ctrl+O - expand tool output
+			return "", ErrExpandOutput
 		case CharCtrlW:
 			buf.DeleteWord()
 		case CharCtrlZ:

runner/runner.go

@@ -3,7 +3,6 @@ package runner
 import (
 	"github.com/ollama/ollama/runner/llamarunner"
 	"github.com/ollama/ollama/runner/ollamarunner"
-	imagerunner "github.com/ollama/ollama/x/imagegen/runner"
 )
 
 func Execute(args []string) error {
@@ -12,19 +11,12 @@ func Execute(args []string) error {
 	}
 
 	var newRunner bool
-	var imageRunner bool
-	if len(args) > 0 && args[0] == "--ollama-engine" {
+	if args[0] == "--ollama-engine" {
 		args = args[1:]
 		newRunner = true
 	}
-	if len(args) > 0 && args[0] == "--image-engine" {
-		args = args[1:]
-		imageRunner = true
-	}
 
-	if imageRunner {
-		return imagerunner.Execute(args)
-	} else if newRunner {
+	if newRunner {
 		return ollamarunner.Execute(args)
 	} else {
 		return llamarunner.Execute(args)

scripts/build_darwin.sh

@@ -42,39 +42,18 @@ shift $(( $OPTIND - 1 ))
 _build_darwin() {
     for ARCH in $ARCHS; do
         status "Building darwin $ARCH"
-        INSTALL_PREFIX=dist/darwin-$ARCH/        
+        INSTALL_PREFIX=dist/darwin-$ARCH/
+        GOOS=darwin GOARCH=$ARCH CGO_ENABLED=1 go build -o $INSTALL_PREFIX .
 
         if [ "$ARCH" = "amd64" ]; then
             status "Building darwin $ARCH dynamic backends"
-            BUILD_DIR=build/darwin-$ARCH
-            cmake -B $BUILD_DIR \
+            cmake -B build/darwin-$ARCH \
                 -DCMAKE_OSX_ARCHITECTURES=x86_64 \
-                -DCMAKE_OSX_DEPLOYMENT_TARGET=14.0 \
-                -DCMAKE_INSTALL_PREFIX=$INSTALL_PREFIX \
-                -DMLX_ENGINE=ON \
-                -DMLX_ENABLE_X64_MAC=ON \
-                -DOLLAMA_RUNNER_DIR=./
-            cmake --build $BUILD_DIR --target ggml-cpu -j
-            cmake --build $BUILD_DIR --target mlx mlxc -j
-            cmake --install $BUILD_DIR --component CPU
-            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)/$BUILD_DIR/lib/ollama -lmlxc -lmlx -Wl,-rpath,@executable_path -lc++ -framework Accelerate -mmacosx-version-min=14.0"
-        else
-            BUILD_DIR=build
-            cmake --preset MLX \
-                -DOLLAMA_RUNNER_DIR=./ \
-                -DCMAKE_OSX_DEPLOYMENT_TARGET=14.0 \
+                -DCMAKE_OSX_DEPLOYMENT_TARGET=11.3 \
                 -DCMAKE_INSTALL_PREFIX=$INSTALL_PREFIX
-            cmake --build --preset MLX --parallel
-            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)/$BUILD_DIR/lib/ollama -lmlxc -lmlx -Wl,-rpath,@executable_path -lc++ -framework Metal -framework Foundation -framework Accelerate -mmacosx-version-min=14.0"
+            cmake --build build/darwin-$ARCH --target ggml-cpu -j
+            cmake --install build/darwin-$ARCH --component CPU
         fi
-        GOOS=darwin GOARCH=$ARCH CGO_ENABLED=1 CGO_CFLAGS="$MLX_CGO_CFLAGS" CGO_LDFLAGS="$MLX_CGO_LDFLAGS" go build -tags mlx -o $INSTALL_PREFIX/imagegen ./x/imagegen/cmd/engine
-        GOOS=darwin GOARCH=$ARCH CGO_ENABLED=1 go build -o $INSTALL_PREFIX .
     done
 }
 
@@ -82,12 +61,10 @@ _sign_darwin() {
     status "Creating universal binary..."
     mkdir -p dist/darwin
     lipo -create -output dist/darwin/ollama dist/darwin-*/ollama
-    lipo -create -output dist/darwin/imagegen dist/darwin-*/imagegen
     chmod +x dist/darwin/ollama
-    chmod +x dist/darwin/imagegen
 
     if [ -n "$APPLE_IDENTITY" ]; then
-        for F in dist/darwin/ollama dist/darwin-*/lib/ollama/* dist/darwin/imagegen; do
+        for F in dist/darwin/ollama dist/darwin-amd64/lib/ollama/*; do
             codesign -f --timestamp -s "$APPLE_IDENTITY" --identifier ai.ollama.ollama --options=runtime $F
         done
 
@@ -154,23 +131,17 @@ _build_macapp() {
     mkdir -p dist/Ollama.app/Contents/Resources
     if [ -d dist/darwin-amd64 ]; then
         lipo -create -output dist/Ollama.app/Contents/Resources/ollama dist/darwin-amd64/ollama dist/darwin-arm64/ollama
-        lipo -create -output dist/Ollama.app/Contents/Resources/imagegen dist/darwin-amd64/imagegen dist/darwin-arm64/imagegen
-        for F in dist/darwin-amd64/lib/ollama/*mlx*.dylib ; do
-            lipo -create -output dist/darwin/$(basename $F) $F dist/darwin-arm64/lib/ollama/$(basename $F)
-        done
-        cp dist/darwin-*/lib/ollama/*.so dist/darwin-*/lib/ollama/*.dylib dist/Ollama.app/Contents/Resources/
-        cp dist/darwin/*.dylib dist/Ollama.app/Contents/Resources/
+        cp dist/darwin-amd64/lib/ollama/*.so dist/darwin-amd64/lib/ollama/*.dylib dist/Ollama.app/Contents/Resources/
     else
         cp -a dist/darwin/ollama dist/Ollama.app/Contents/Resources/ollama
         cp dist/darwin/*.so dist/darwin/*.dylib dist/Ollama.app/Contents/Resources/
     fi
-    cp -a dist/darwin/imagegen dist/Ollama.app/Contents/Resources/imagegen
     chmod a+x dist/Ollama.app/Contents/Resources/ollama
 
     # Sign
     if [ -n "$APPLE_IDENTITY" ]; then
         codesign -f --timestamp -s "$APPLE_IDENTITY" --identifier ai.ollama.ollama --options=runtime dist/Ollama.app/Contents/Resources/ollama
-        for lib in dist/Ollama.app/Contents/Resources/*.so dist/Ollama.app/Contents/Resources/*.dylib dist/Ollama.app/Contents/Resources/imagegen ; do
+        for lib in dist/Ollama.app/Contents/Resources/*.so dist/Ollama.app/Contents/Resources/*.dylib ; 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
@@ -178,7 +149,7 @@ _build_macapp() {
 
     rm -f dist/Ollama-darwin.zip
     ditto -c -k --keepParent dist/Ollama.app dist/Ollama-darwin.zip
-    (cd dist/Ollama.app/Contents/Resources/; tar -cf - ollama imagegen *.so *.dylib) | gzip -9vc > dist/ollama-darwin.tgz
+    (cd dist/Ollama.app/Contents/Resources/; tar -cf - ollama *.so *.dylib) | gzip -9vc > dist/ollama-darwin.tgz
 
     # Notarize and Staple
     if [ -n "$APPLE_IDENTITY" ]; then

scripts/build_linux.sh

@@ -12,17 +12,6 @@ set -eu
 
 . $(dirname $0)/env.sh
 
-# Check for required tools
-if ! command -v zstd >/dev/null 2>&1; then
-    echo "ERROR: zstd is required but not installed." >&2
-    echo "Please install zstd:" >&2
-    echo "  - macOS: brew install zstd" >&2
-    echo "  - Debian/Ubuntu: sudo apt-get install zstd" >&2
-    echo "  - RHEL/CentOS/Fedora: sudo dnf install zstd" >&2
-    echo "  - Arch: sudo pacman -S zstd" >&2
-    exit 1
-fi
-
 mkdir -p dist
 
 docker buildx build \
@@ -48,68 +37,19 @@ if echo $PLATFORM | grep "amd64" > /dev/null; then
         .
 fi
 
-# Deduplicate CUDA libraries across mlx_* and cuda_* directories
-deduplicate_cuda_libs() {
-    local base_dir="$1"
-    echo "Deduplicating CUDA libraries in ${base_dir}..."
-
-    # Find all mlx_cuda_* directories
-    for mlx_dir in "${base_dir}"/lib/ollama/mlx_cuda_*; do
-        [ -d "${mlx_dir}" ] || continue
-
-        # Extract CUDA version (e.g., v12, v13)
-        cuda_version=$(basename "${mlx_dir}" | sed 's/mlx_cuda_//')
-        cuda_dir="${base_dir}/lib/ollama/cuda_${cuda_version}"
-
-        # Skip if corresponding cuda_* directory doesn't exist
-        [ -d "${cuda_dir}" ] || continue
-
-        echo "  Checking ${mlx_dir} against ${cuda_dir}..."
-
-        # Find all .so* files in mlx directory
-        find "${mlx_dir}" -type f -name "*.so*" | while read mlx_file; do
-            filename=$(basename "${mlx_file}")
-            cuda_file="${cuda_dir}/${filename}"
-
-            # Skip if file doesn't exist in cuda directory
-            [ -f "${cuda_file}" ] || continue
-
-            # Compare checksums
-            mlx_sum=$(sha256sum "${mlx_file}" | awk '{print $1}')
-            cuda_sum=$(sha256sum "${cuda_file}" | awk '{print $1}')
-
-            if [ "${mlx_sum}" = "${cuda_sum}" ]; then
-                echo "    Deduplicating ${filename}"
-                # Calculate relative path from mlx_dir to cuda_dir
-                rel_path="../cuda_${cuda_version}/${filename}"
-                rm -f "${mlx_file}"
-                ln -s "${rel_path}" "${mlx_file}"
-            fi
-        done
-    done
-}
-
-# Run deduplication for each platform output directory
-if echo $PLATFORM | grep "," > /dev/null ; then
-    deduplicate_cuda_libs "./dist/linux_amd64"
-    deduplicate_cuda_libs "./dist/linux_arm64"
-elif echo $PLATFORM | grep "amd64\|arm64" > /dev/null ; then
-    deduplicate_cuda_libs "./dist"
-fi
-
 # buildx behavior changes for single vs. multiplatform
 echo "Compressing linux tar bundles..."
 if echo $PLATFORM | grep "," > /dev/null ; then
-        tar c -C ./dist/linux_arm64 --exclude cuda_jetpack5 --exclude cuda_jetpack6 . | zstd --ultra -22 -T0 >./dist/ollama-linux-arm64.tar.zst
-        tar c -C ./dist/linux_arm64 ./lib/ollama/cuda_jetpack5  | zstd --ultra -22 -T0 >./dist/ollama-linux-arm64-jetpack5.tar.zst
-        tar c -C ./dist/linux_arm64 ./lib/ollama/cuda_jetpack6  | zstd --ultra -22 -T0 >./dist/ollama-linux-arm64-jetpack6.tar.zst
-        tar c -C ./dist/linux_amd64 --exclude rocm . | zstd --ultra -22 -T0 >./dist/ollama-linux-amd64.tar.zst
-        tar c -C ./dist/linux_amd64 ./lib/ollama/rocm  | zstd --ultra -22 -T0 >./dist/ollama-linux-amd64-rocm.tar.zst
+        tar c -C ./dist/linux_arm64 --exclude cuda_jetpack5 --exclude cuda_jetpack6 . | pigz -9vc >./dist/ollama-linux-arm64.tgz
+        tar c -C ./dist/linux_arm64 ./lib/ollama/cuda_jetpack5  | pigz -9vc >./dist/ollama-linux-arm64-jetpack5.tgz
+        tar c -C ./dist/linux_arm64 ./lib/ollama/cuda_jetpack6  | pigz -9vc >./dist/ollama-linux-arm64-jetpack6.tgz
+        tar c -C ./dist/linux_amd64 --exclude rocm . | pigz -9vc >./dist/ollama-linux-amd64.tgz
+        tar c -C ./dist/linux_amd64 ./lib/ollama/rocm  | pigz -9vc >./dist/ollama-linux-amd64-rocm.tgz
 elif echo $PLATFORM | grep "arm64" > /dev/null ; then
-        tar c -C ./dist/ --exclude cuda_jetpack5 --exclude cuda_jetpack6 bin lib | zstd --ultra -22 -T0 >./dist/ollama-linux-arm64.tar.zst
-        tar c -C ./dist/ ./lib/ollama/cuda_jetpack5  | zstd --ultra -22 -T0 >./dist/ollama-linux-arm64-jetpack5.tar.zst
-        tar c -C ./dist/ ./lib/ollama/cuda_jetpack6  | zstd --ultra -22 -T0 >./dist/ollama-linux-arm64-jetpack6.tar.zst
+        tar c -C ./dist/ --exclude cuda_jetpack5 --exclude cuda_jetpack6 bin lib | pigz -9vc >./dist/ollama-linux-arm64.tgz
+        tar c -C ./dist/ ./lib/ollama/cuda_jetpack5  | pigz -9vc >./dist/ollama-linux-arm64-jetpack5.tgz
+        tar c -C ./dist/ ./lib/ollama/cuda_jetpack6  | pigz -9vc >./dist/ollama-linux-arm64-jetpack6.tgz
 elif echo $PLATFORM | grep "amd64" > /dev/null ; then
-        tar c -C ./dist/ --exclude rocm bin lib | zstd --ultra -22 -T0 >./dist/ollama-linux-amd64.tar.zst
-        tar c -C ./dist/ ./lib/ollama/rocm  | zstd --ultra -22 -T0 >./dist/ollama-linux-amd64-rocm.tar.zst
+        tar c -C ./dist/ --exclude rocm bin lib | pigz -9vc >./dist/ollama-linux-amd64.tgz
+        tar c -C ./dist/ ./lib/ollama/rocm  | pigz -9vc >./dist/ollama-linux-amd64-rocm.tgz
 fi

scripts/install.sh

@@ -66,36 +66,6 @@ if [ -n "$NEEDS" ]; then
     exit 1
 fi
 
-# Function to download and extract with fallback from zst to tgz
-download_and_extract() {
-    local url_base="$1"
-    local dest_dir="$2"
-    local filename="$3"
-
-    # Check if .tar.zst is available
-    if curl --fail --silent --head --location "${url_base}/${filename}.tar.zst${VER_PARAM}" >/dev/null 2>&1; then
-        # zst file exists - check if we have zstd tool
-        if ! available zstd; then
-            error "This version requires zstd for extraction. Please install zstd and try again:
-  - Debian/Ubuntu: sudo apt-get install zstd
-  - RHEL/CentOS/Fedora: sudo dnf install zstd
-  - Arch: sudo pacman -S zstd"
-        fi
-
-        status "Downloading ${filename}.tar.zst"
-        curl --fail --show-error --location --progress-bar \
-            "${url_base}/${filename}.tar.zst${VER_PARAM}" | \
-            zstd -d | $SUDO tar -xf - -C "${dest_dir}"
-        return 0
-    fi
-
-    # Fall back to .tgz for older versions
-    status "Downloading ${filename}.tgz"
-    curl --fail --show-error --location --progress-bar \
-        "${url_base}/${filename}.tgz${VER_PARAM}" | \
-        $SUDO tar -xzf - -C "${dest_dir}"
-}
-
 for BINDIR in /usr/local/bin /usr/bin /bin; do
     echo $PATH | grep -q $BINDIR && break || continue
 done
@@ -108,7 +78,10 @@ fi
 status "Installing ollama to $OLLAMA_INSTALL_DIR"
 $SUDO install -o0 -g0 -m755 -d $BINDIR
 $SUDO install -o0 -g0 -m755 -d "$OLLAMA_INSTALL_DIR/lib/ollama"
-download_and_extract "https://ollama.com/download" "$OLLAMA_INSTALL_DIR" "ollama-linux-${ARCH}"
+status "Downloading Linux ${ARCH} bundle"
+curl --fail --show-error --location --progress-bar \
+    "https://ollama.com/download/ollama-linux-${ARCH}.tgz${VER_PARAM}" | \
+    $SUDO tar -xzf - -C "$OLLAMA_INSTALL_DIR"
 
 if [ "$OLLAMA_INSTALL_DIR/bin/ollama" != "$BINDIR/ollama" ] ; then
     status "Making ollama accessible in the PATH in $BINDIR"
@@ -118,9 +91,15 @@ fi
 # Check for NVIDIA JetPack systems with additional downloads
 if [ -f /etc/nv_tegra_release ] ; then
     if grep R36 /etc/nv_tegra_release > /dev/null ; then
-        download_and_extract "https://ollama.com/download" "$OLLAMA_INSTALL_DIR" "ollama-linux-${ARCH}-jetpack6"
+        status "Downloading JetPack 6 components"
+        curl --fail --show-error --location --progress-bar \
+            "https://ollama.com/download/ollama-linux-${ARCH}-jetpack6.tgz${VER_PARAM}" | \
+            $SUDO tar -xzf - -C "$OLLAMA_INSTALL_DIR"
     elif grep R35 /etc/nv_tegra_release > /dev/null ; then
-        download_and_extract "https://ollama.com/download" "$OLLAMA_INSTALL_DIR" "ollama-linux-${ARCH}-jetpack5"
+        status "Downloading JetPack 5 components"
+        curl --fail --show-error --location --progress-bar \
+            "https://ollama.com/download/ollama-linux-${ARCH}-jetpack5.tgz${VER_PARAM}" | \
+            $SUDO tar -xzf - -C "$OLLAMA_INSTALL_DIR"
     else
         warning "Unsupported JetPack version detected.  GPU may not be supported"
     fi
@@ -243,7 +222,10 @@ if ! check_gpu lspci nvidia && ! check_gpu lshw nvidia && ! check_gpu lspci amdg
 fi
 
 if check_gpu lspci amdgpu || check_gpu lshw amdgpu; then
-    download_and_extract "https://ollama.com/download" "$OLLAMA_INSTALL_DIR" "ollama-linux-${ARCH}-rocm"
+    status "Downloading Linux ROCm ${ARCH} bundle"
+    curl --fail --show-error --location --progress-bar \
+        "https://ollama.com/download/ollama-linux-${ARCH}-rocm.tgz${VER_PARAM}" | \
+        $SUDO tar -xzf - -C "$OLLAMA_INSTALL_DIR"
 
     install_success
     status "AMD GPU ready."

server/create.go

@@ -26,7 +26,6 @@ import (
 	"github.com/ollama/ollama/convert"
 	"github.com/ollama/ollama/envconfig"
 	"github.com/ollama/ollama/format"
-	ofs "github.com/ollama/ollama/fs"
 	"github.com/ollama/ollama/fs/ggml"
 	"github.com/ollama/ollama/template"
 	"github.com/ollama/ollama/types/errtypes"
@@ -455,7 +454,7 @@ func convertFromSafetensors(files map[string]string, baseLayers []*layerGGML, is
 	return layers, nil
 }
 
-func kvFromLayers(baseLayers []*layerGGML) (ofs.Config, error) {
+func kvFromLayers(baseLayers []*layerGGML) (ggml.KV, error) {
 	for _, l := range baseLayers {
 		if l.GGML != nil {
 			return l.KV(), nil

server/images.go

@@ -30,7 +30,6 @@ import (
 	"github.com/ollama/ollama/thinking"
 	"github.com/ollama/ollama/types/model"
 	"github.com/ollama/ollama/version"
-	"github.com/ollama/ollama/x/imagegen/transfer"
 )
 
 var (
@@ -74,11 +73,6 @@ type Model struct {
 func (m *Model) Capabilities() []model.Capability {
 	capabilities := []model.Capability{}
 
-	// Check for image generation model via config capabilities
-	if slices.Contains(m.Config.Capabilities, "image") {
-		return []model.Capability{model.CapabilityImageGeneration}
-	}
-
 	// Check for completion capability
 	if m.ModelPath != "" {
 		f, err := gguf.Open(m.ModelPath)
@@ -561,24 +555,6 @@ func PushModel(ctx context.Context, name string, regOpts *registryOptions, fn fu
 		layers = append(layers, manifest.Config)
 	}
 
-	// Use fast transfer for models with tensor layers (many small blobs)
-	if hasTensorLayers(layers) {
-		// Read raw manifest JSON to preserve tensor metadata fields
-		manifestPath, err := mp.GetManifestPath()
-		if err != nil {
-			return err
-		}
-		manifestJSON, err := os.ReadFile(manifestPath)
-		if err != nil {
-			return err
-		}
-		if err := pushWithTransfer(ctx, mp, layers, manifestJSON, regOpts, fn); err != nil {
-			return err
-		}
-		fn(api.ProgressResponse{Status: "success"})
-		return nil
-	}
-
 	for _, layer := range layers {
 		if err := uploadBlob(ctx, mp, layer, regOpts, fn); err != nil {
 			slog.Info(fmt.Sprintf("error uploading blob: %v", err))
@@ -644,15 +620,6 @@ func PullModel(ctx context.Context, name string, regOpts *registryOptions, fn fu
 		layers = append(layers, manifest.Config)
 	}
 
-	// Use fast transfer for models with tensor layers (many small blobs)
-	if hasTensorLayers(layers) {
-		if err := pullWithTransfer(ctx, mp, layers, manifest, regOpts, fn); err != nil {
-			return err
-		}
-		fn(api.ProgressResponse{Status: "success"})
-		return nil
-	}
-
 	skipVerify := make(map[string]bool)
 	for _, layer := range layers {
 		cacheHit, err := downloadBlob(ctx, downloadOpts{
@@ -667,6 +634,7 @@ func PullModel(ctx context.Context, name string, regOpts *registryOptions, fn fu
 		skipVerify[layer.Digest] = cacheHit
 		delete(deleteMap, layer.Digest)
 	}
+	delete(deleteMap, manifest.Config.Digest)
 
 	fn(api.ProgressResponse{Status: "verifying sha256 digest"})
 	for _, layer := range layers {
@@ -675,11 +643,13 @@ func PullModel(ctx context.Context, name string, regOpts *registryOptions, fn fu
 		}
 		if err := verifyBlob(layer.Digest); err != nil {
 			if errors.Is(err, errDigestMismatch) {
+				// something went wrong, delete the blob
 				fp, err := GetBlobsPath(layer.Digest)
 				if err != nil {
 					return err
 				}
 				if err := os.Remove(fp); err != nil {
+					// log this, but return the original error
 					slog.Info(fmt.Sprintf("couldn't remove file with digest mismatch '%s': %v", fp, err))
 				}
 			}
@@ -687,11 +657,6 @@ func PullModel(ctx context.Context, name string, regOpts *registryOptions, fn fu
 		}
 	}
 
-	for _, layer := range layers {
-		delete(deleteMap, layer.Digest)
-	}
-	delete(deleteMap, manifest.Config.Digest)
-
 	fn(api.ProgressResponse{Status: "writing manifest"})
 
 	manifestJSON, err := json.Marshal(manifest)
@@ -725,148 +690,6 @@ func PullModel(ctx context.Context, name string, regOpts *registryOptions, fn fu
 	return nil
 }
 
-// hasTensorLayers checks if any layer has tensor media type.
-func hasTensorLayers(layers []Layer) bool {
-	for _, layer := range layers {
-		if layer.MediaType == MediaTypeImageTensor {
-			return true
-		}
-	}
-	return false
-}
-
-// pullWithTransfer uses the simplified x/transfer package for downloading blobs.
-func pullWithTransfer(ctx context.Context, mp ModelPath, layers []Layer, manifest *Manifest, regOpts *registryOptions, fn func(api.ProgressResponse)) error {
-	blobs := make([]transfer.Blob, len(layers))
-	for i, layer := range layers {
-		blobs[i] = transfer.Blob{
-			Digest: layer.Digest,
-			Size:   layer.Size,
-		}
-	}
-
-	destDir, err := GetBlobsPath("")
-	if err != nil {
-		return err
-	}
-
-	base := mp.BaseURL()
-	if base.Scheme != "http" && regOpts != nil && regOpts.Insecure {
-		base.Scheme = "http"
-	}
-	baseURL := base.String()
-
-	var totalSize int64
-	for _, blob := range blobs {
-		totalSize += blob.Size
-	}
-
-	progress := func(completed, total int64) {
-		fn(api.ProgressResponse{
-			Status:    "pulling model",
-			Digest:    "sha256:model",
-			Total:     total,
-			Completed: completed,
-		})
-	}
-
-	getToken := func(ctx context.Context, challenge transfer.AuthChallenge) (string, error) {
-		return getAuthorizationToken(ctx, registryChallenge{
-			Realm:   challenge.Realm,
-			Service: challenge.Service,
-			Scope:   challenge.Scope,
-		})
-	}
-
-	if err := transfer.Download(ctx, transfer.DownloadOptions{
-		Blobs:      blobs,
-		BaseURL:    baseURL,
-		DestDir:    destDir,
-		Repository: mp.GetNamespaceRepository(),
-		Progress:   progress,
-		Token:      regOpts.Token,
-		GetToken:   getToken,
-		Logger:     slog.Default(),
-	}); err != nil {
-		return err
-	}
-
-	// Write manifest
-	fn(api.ProgressResponse{Status: "writing manifest"})
-	manifestJSON, err := json.Marshal(manifest)
-	if err != nil {
-		return err
-	}
-
-	fp, err := mp.GetManifestPath()
-	if err != nil {
-		return err
-	}
-	if err := os.MkdirAll(filepath.Dir(fp), 0o755); err != nil {
-		return err
-	}
-
-	return os.WriteFile(fp, manifestJSON, 0o644)
-}
-
-// pushWithTransfer uses the simplified x/transfer package for uploading blobs and manifest.
-func pushWithTransfer(ctx context.Context, mp ModelPath, layers []Layer, manifestJSON []byte, regOpts *registryOptions, fn func(api.ProgressResponse)) error {
-	blobs := make([]transfer.Blob, len(layers))
-	for i, layer := range layers {
-		blobs[i] = transfer.Blob{
-			Digest: layer.Digest,
-			Size:   layer.Size,
-			From:   layer.From,
-		}
-	}
-
-	srcDir, err := GetBlobsPath("")
-	if err != nil {
-		return err
-	}
-
-	base := mp.BaseURL()
-	if base.Scheme != "http" && regOpts != nil && regOpts.Insecure {
-		base.Scheme = "http"
-	}
-	baseURL := base.String()
-
-	var totalSize int64
-	for _, blob := range blobs {
-		totalSize += blob.Size
-	}
-
-	progress := func(completed, total int64) {
-		fn(api.ProgressResponse{
-			Status:    "pushing model",
-			Digest:    "sha256:model",
-			Total:     total,
-			Completed: completed,
-		})
-	}
-
-	getToken := func(ctx context.Context, challenge transfer.AuthChallenge) (string, error) {
-		return getAuthorizationToken(ctx, registryChallenge{
-			Realm:   challenge.Realm,
-			Service: challenge.Service,
-			Scope:   challenge.Scope,
-		})
-	}
-
-	return transfer.Upload(ctx, transfer.UploadOptions{
-		Blobs:       blobs,
-		BaseURL:     baseURL,
-		SrcDir:      srcDir,
-		Progress:    progress,
-		Token:       regOpts.Token,
-		GetToken:    getToken,
-		Logger:      slog.Default(),
-		Manifest:    manifestJSON,
-		ManifestRef: mp.Tag,
-		Repository:  mp.GetNamespaceRepository(),
-	})
-}
-
 func pullModelManifest(ctx context.Context, mp ModelPath, regOpts *registryOptions) (*Manifest, error) {
 	requestURL := mp.BaseURL().JoinPath("v2", mp.GetNamespaceRepository(), "manifests", mp.Tag)
 

server/images_test.go

@@ -47,15 +47,6 @@ func TestModelCapabilities(t *testing.T) {
 		model        Model
 		expectedCaps []model.Capability
 	}{
-		{
-			name: "model with image generation capability via config",
-			model: Model{
-				Config: model.ConfigV2{
-					Capabilities: []string{"image"},
-				},
-			},
-			expectedCaps: []model.Capability{model.CapabilityImageGeneration},
-		},
 		{
 			name: "model with completion capability",
 			model: Model{

server/layer.go

@@ -13,14 +13,9 @@ type Layer struct {
 	Digest    string `json:"digest"`
 	Size      int64  `json:"size"`
 	From      string `json:"from,omitempty"`
-	Name      string `json:"name,omitempty"` // tensor name, e.g., "text_encoder/model.embed_tokens.weight"
 	status    string
 }
 
-const (
-	MediaTypeImageTensor = "application/vnd.ollama.image.tensor"
-)
-
 func NewLayer(r io.Reader, mediatype string) (Layer, error) {
 	blobs, err := GetBlobsPath("")
 	if err != nil {

server/routes.go

@@ -50,8 +50,6 @@ import (
 	"github.com/ollama/ollama/types/errtypes"
 	"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"
 )
 
 const signinURLStr = "https://ollama.com/connect?name=%s&key=%s"
@@ -164,29 +162,6 @@ 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 +189,6 @@ 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
@@ -1575,12 +1544,6 @@ func (s *Server) GenerateRoutes(rc *ollama.Registry) (http.Handler, error) {
 	r.GET("/v1/models/:model", middleware.RetrieveMiddleware(), s.ShowHandler)
 	r.POST("/v1/responses", middleware.ResponsesMiddleware(), s.ChatHandler)
 
-	// 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{

server/routes_create_test.go

@@ -22,7 +22,6 @@ import (
 	gocmpopts "github.com/google/go-cmp/cmp/cmpopts"
 
 	"github.com/ollama/ollama/api"
-	"github.com/ollama/ollama/convert"
 	"github.com/ollama/ollama/envconfig"
 	"github.com/ollama/ollama/fs/ggml"
 	"github.com/ollama/ollama/types/model"
@@ -42,7 +41,7 @@ func createBinFile(t *testing.T, kv map[string]any, ti []*ggml.Tensor) (string,
 	}
 	defer f.Close()
 
-	var base convert.KV = map[string]any{"general.architecture": "test"}
+	base := map[string]any{"general.architecture": "test"}
 	maps.Copy(base, kv)
 
 	if err := ggml.WriteGGUF(f, base, ti); err != nil {

server/sched.go

@@ -21,7 +21,6 @@ import (
 	"github.com/ollama/ollama/logutil"
 	"github.com/ollama/ollama/ml"
 	"github.com/ollama/ollama/types/model"
-	"github.com/ollama/ollama/x/imagegen"
 )
 
 type LlmRequest struct {
@@ -195,14 +194,6 @@ func (s *Scheduler) processPending(ctx context.Context) {
 						slog.Debug("updating default concurrency", "OLLAMA_MAX_LOADED_MODELS", maxRunners, "gpu_count", len(gpus))
 					}
 
-					// Check for image generation model before attempting GGML load
-					if slices.Contains(pending.model.Config.Capabilities, "image") {
-						if s.loadImageGen(pending) {
-							break
-						}
-						continue
-					}
-
 					// Load model for fitting
 					logutil.Trace("loading model metadata", "model", pending.model.ModelPath)
 					ggml, err := llm.LoadModel(pending.model.ModelPath, 1024)
@@ -552,48 +543,6 @@ iGPUScan:
 	return false
 }
 
-// loadImageGen loads an image generation model.
-func (s *Scheduler) loadImageGen(req *LlmRequest) bool {
-	// Use model name for imagegen (it resolves manifests by name, not file path)
-	modelName := req.model.ShortName
-	server, err := imagegen.NewServer(modelName)
-	if err != nil {
-		req.errCh <- err
-		return true
-	}
-
-	sessionDuration := envconfig.KeepAlive()
-	if req.sessionDuration != nil {
-		sessionDuration = req.sessionDuration.Duration
-	}
-
-	runner := &runnerRef{
-		model:           req.model,
-		modelPath:       req.model.ModelPath,
-		llama:           server,
-		Options:         &req.opts,
-		loading:         false,
-		sessionDuration: sessionDuration,
-		refCount:        1,
-	}
-
-	s.loadedMu.Lock()
-	s.loaded[req.model.ModelPath] = runner
-	s.loadedMu.Unlock()
-
-	// Set up expiration timer
-	runner.refMu.Lock()
-	if sessionDuration > 0 {
-		runner.expireTimer = time.AfterFunc(sessionDuration, func() {
-			s.expiredCh <- runner
-		})
-	}
-	runner.refMu.Unlock()
-
-	req.useLoadedRunner(runner, s.finishedReqCh)
-	return true
-}
-
 func (s *Scheduler) updateFreeSpace(allGpus []ml.DeviceInfo) {
 	if len(allGpus) == 0 {
 		return

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) {
@@ -806,61 +804,3 @@ func (s *mockLlm) GetPort() int                                       { return -
 func (s *mockLlm) GetDeviceInfos(ctx context.Context) []ml.DeviceInfo { return nil }
 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.
-func TestImageGenRunnerCanBeEvicted(t *testing.T) {
-	ctx, done := context.WithTimeout(t.Context(), 500*time.Millisecond)
-	defer done()
-
-	s := InitScheduler(ctx)
-	s.getGpuFn = getGpuFn
-	s.getSystemInfoFn = getSystemInfoFn
-
-	// Simulate an image gen runner already loaded
-	imageGenRunner := &runnerRef{
-		model:           &Model{Name: "z-image", ModelPath: "/fake/image/model"},
-		modelPath:       "/fake/image/model",
-		llama:           &mockLlm{vramSize: 21 * format.GigaByte, vramByGPU: map[ml.DeviceID]uint64{}},
-		sessionDuration: 5 * time.Millisecond,
-		refCount:        0, // idle
-	}
-
-	s.loadedMu.Lock()
-	s.loaded["/fake/image/model"] = imageGenRunner
-	s.loadedMu.Unlock()
-
-	// Verify the image gen runner is loaded
-	s.loadedMu.Lock()
-	require.Len(t, s.loaded, 1)
-	s.loadedMu.Unlock()
-
-	// findRunnerToUnload should find the idle image gen runner
-	runner := s.findRunnerToUnload()
-	require.NotNil(t, runner)
-	require.Equal(t, "/fake/image/model", runner.modelPath)
-}

template/template.go

@@ -381,28 +381,6 @@ func (t templateTools) String() string {
 	return string(bts)
 }
 
-// templateArgs is a map type with JSON string output for templates.
-type templateArgs map[string]any
-
-func (t templateArgs) String() string {
-	if t == nil {
-		return "{}"
-	}
-	bts, _ := json.Marshal(t)
-	return string(bts)
-}
-
-// templateProperties is a map type with JSON string output for templates.
-type templateProperties map[string]api.ToolProperty
-
-func (t templateProperties) String() string {
-	if t == nil {
-		return "{}"
-	}
-	bts, _ := json.Marshal(t)
-	return string(bts)
-}
-
 // templateTool is a template-compatible representation of api.Tool
 // with Properties as a regular map for template ranging.
 type templateTool struct {
@@ -418,11 +396,11 @@ type templateToolFunction struct {
 }
 
 type templateToolFunctionParameters struct {
-	Type       string             `json:"type"`
-	Defs       any                `json:"$defs,omitempty"`
-	Items      any                `json:"items,omitempty"`
-	Required   []string           `json:"required,omitempty"`
-	Properties templateProperties `json:"properties"`
+	Type       string                      `json:"type"`
+	Defs       any                         `json:"$defs,omitempty"`
+	Items      any                         `json:"items,omitempty"`
+	Required   []string                    `json:"required,omitempty"`
+	Properties map[string]api.ToolProperty `json:"properties"`
 }
 
 // templateToolCall is a template-compatible representation of api.ToolCall
@@ -435,7 +413,7 @@ type templateToolCall struct {
 type templateToolCallFunction struct {
 	Index     int
 	Name      string
-	Arguments templateArgs
+	Arguments map[string]any
 }
 
 // templateMessage is a template-compatible representation of api.Message
@@ -468,7 +446,7 @@ func convertToolsForTemplate(tools api.Tools) templateTools {
 					Defs:       tool.Function.Parameters.Defs,
 					Items:      tool.Function.Parameters.Items,
 					Required:   tool.Function.Parameters.Required,
-					Properties: templateProperties(tool.Function.Parameters.Properties.ToMap()),
+					Properties: tool.Function.Parameters.Properties.ToMap(),
 				},
 			},
 		}
@@ -490,7 +468,7 @@ func convertMessagesForTemplate(messages []*api.Message) []*templateMessage {
 				Function: templateToolCallFunction{
 					Index:     tc.Function.Index,
 					Name:      tc.Function.Name,
-					Arguments: templateArgs(tc.Function.Arguments.ToMap()),
+					Arguments: tc.Function.Arguments.ToMap(),
 				},
 			})
 		}

template/template_test.go

@@ -613,159 +613,3 @@ func TestCollate(t *testing.T) {
 		})
 	}
 }
-
-func TestTemplateArgumentsJSON(t *testing.T) {
-	// Test that {{ .Function.Arguments }} outputs valid JSON, not map[key:value]
-	tmpl := `{{- range .Messages }}{{- range .ToolCalls }}{{ .Function.Arguments }}{{- end }}{{- end }}`
-
-	template, err := Parse(tmpl)
-	if err != nil {
-		t.Fatal(err)
-	}
-
-	args := api.NewToolCallFunctionArguments()
-	args.Set("location", "Tokyo")
-	args.Set("unit", "celsius")
-
-	var buf bytes.Buffer
-	err = template.Execute(&buf, Values{
-		Messages: []api.Message{{
-			Role: "assistant",
-			ToolCalls: []api.ToolCall{{
-				Function: api.ToolCallFunction{
-					Name:      "get_weather",
-					Arguments: args,
-				},
-			}},
-		}},
-	})
-	if err != nil {
-		t.Fatal(err)
-	}
-
-	got := buf.String()
-	// Should be valid JSON, not "map[location:Tokyo unit:celsius]"
-	if strings.HasPrefix(got, "map[") {
-		t.Errorf("Arguments output as Go map format: %s", got)
-	}
-
-	var parsed map[string]any
-	if err := json.Unmarshal([]byte(got), &parsed); err != nil {
-		t.Errorf("Arguments not valid JSON: %s, error: %v", got, err)
-	}
-}
-
-func TestTemplatePropertiesJSON(t *testing.T) {
-	// Test that {{ .Function.Parameters.Properties }} outputs valid JSON
-	// Note: template must reference .Messages to trigger the modern code path that converts Tools
-	tmpl := `{{- range .Messages }}{{- end }}{{- range .Tools }}{{ .Function.Parameters.Properties }}{{- end }}`
-
-	template, err := Parse(tmpl)
-	if err != nil {
-		t.Fatal(err)
-	}
-
-	props := api.NewToolPropertiesMap()
-	props.Set("location", api.ToolProperty{Type: api.PropertyType{"string"}, Description: "City name"})
-
-	var buf bytes.Buffer
-	err = template.Execute(&buf, Values{
-		Messages: []api.Message{{Role: "user", Content: "test"}},
-		Tools: api.Tools{{
-			Type: "function",
-			Function: api.ToolFunction{
-				Name:        "get_weather",
-				Description: "Get weather",
-				Parameters: api.ToolFunctionParameters{
-					Type:       "object",
-					Properties: props,
-				},
-			},
-		}},
-	})
-	if err != nil {
-		t.Fatal(err)
-	}
-
-	got := buf.String()
-	// Should be valid JSON, not "map[location:{...}]"
-	if strings.HasPrefix(got, "map[") {
-		t.Errorf("Properties output as Go map format: %s", got)
-	}
-
-	var parsed map[string]any
-	if err := json.Unmarshal([]byte(got), &parsed); err != nil {
-		t.Errorf("Properties not valid JSON: %s, error: %v", got, err)
-	}
-}
-
-func TestTemplateArgumentsRange(t *testing.T) {
-	// Test that we can range over Arguments in templates
-	tmpl := `{{- range .Messages }}{{- range .ToolCalls }}{{- range $k, $v := .Function.Arguments }}{{ $k }}={{ $v }};{{- end }}{{- end }}{{- end }}`
-
-	template, err := Parse(tmpl)
-	if err != nil {
-		t.Fatal(err)
-	}
-
-	args := api.NewToolCallFunctionArguments()
-	args.Set("city", "Tokyo")
-
-	var buf bytes.Buffer
-	err = template.Execute(&buf, Values{
-		Messages: []api.Message{{
-			Role: "assistant",
-			ToolCalls: []api.ToolCall{{
-				Function: api.ToolCallFunction{
-					Name:      "get_weather",
-					Arguments: args,
-				},
-			}},
-		}},
-	})
-	if err != nil {
-		t.Fatal(err)
-	}
-
-	got := buf.String()
-	if got != "city=Tokyo;" {
-		t.Errorf("Range over Arguments failed, got: %s, want: city=Tokyo;", got)
-	}
-}
-
-func TestTemplatePropertiesRange(t *testing.T) {
-	// Test that we can range over Properties in templates
-	// Note: template must reference .Messages to trigger the modern code path that converts Tools
-	tmpl := `{{- range .Messages }}{{- end }}{{- range .Tools }}{{- range $name, $prop := .Function.Parameters.Properties }}{{ $name }}:{{ $prop.Type }};{{- end }}{{- end }}`
-
-	template, err := Parse(tmpl)
-	if err != nil {
-		t.Fatal(err)
-	}
-
-	props := api.NewToolPropertiesMap()
-	props.Set("location", api.ToolProperty{Type: api.PropertyType{"string"}})
-
-	var buf bytes.Buffer
-	err = template.Execute(&buf, Values{
-		Messages: []api.Message{{Role: "user", Content: "test"}},
-		Tools: api.Tools{{
-			Type: "function",
-			Function: api.ToolFunction{
-				Name: "get_weather",
-				Parameters: api.ToolFunctionParameters{
-					Type:       "object",
-					Properties: props,
-				},
-			},
-		}},
-	})
-	if err != nil {
-		t.Fatal(err)
-	}
-
-	got := buf.String()
-	if got != "location:string;" {
-		t.Errorf("Range over Properties failed, got: %s, want: location:string;", got)
-	}
-}

types/model/capability.go

@@ -3,13 +3,12 @@ package model
 type Capability string
 
 const (
-	CapabilityCompletion      = Capability("completion")
-	CapabilityTools           = Capability("tools")
-	CapabilityInsert          = Capability("insert")
-	CapabilityVision          = Capability("vision")
-	CapabilityEmbedding       = Capability("embedding")
-	CapabilityThinking        = Capability("thinking")
-	CapabilityImageGeneration = Capability("image")
+	CapabilityCompletion = Capability("completion")
+	CapabilityTools      = Capability("tools")
+	CapabilityInsert     = Capability("insert")
+	CapabilityVision     = Capability("vision")
+	CapabilityEmbedding  = Capability("embedding")
+	CapabilityThinking   = Capability("thinking")
 )
 
 func (c Capability) String() string {

x/README.md

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

x/agent/approval.go

@@ -33,29 +33,10 @@ type ApprovalResult struct {
 // Option labels for the selector (numbered for quick selection)
 var optionLabels = []string{
 	"1. Execute once",
-	"2. Allow for this session",
+	"2. Always allow",
 	"3. Deny",
 }
 
-// toolDisplayNames maps internal tool names to human-readable display names.
-var toolDisplayNames = map[string]string{
-	"bash":       "Bash",
-	"web_search": "Web Search",
-}
-
-// ToolDisplayName returns the human-readable display name for a tool.
-func ToolDisplayName(toolName string) string {
-	if displayName, ok := toolDisplayNames[toolName]; ok {
-		return displayName
-	}
-	// Default: capitalize first letter and replace underscores with spaces
-	name := strings.ReplaceAll(toolName, "_", " ")
-	if len(name) > 0 {
-		return strings.ToUpper(name[:1]) + name[1:]
-	}
-	return toolName
-}
-
 // autoAllowCommands are commands that are always allowed without prompting.
 // These are zero-risk, read-only commands.
 var autoAllowCommands = map[string]bool{
@@ -494,32 +475,16 @@ func (a *ApprovalManager) RequestApproval(toolName string, args map[string]any)
 	// This prevents buffered input from causing double-press issues
 	flushStdin(fd)
 
+	// Check if bash command targets paths outside cwd
 	isWarning := false
-	var warningMsg string
-	var allowlistInfo string
 	if toolName == "bash" {
 		if cmd, ok := args["command"].(string); ok {
-			if isCommandOutsideCwd(cmd) {
-				isWarning = true
-				warningMsg = "command targets paths outside project"
-			}
-			if prefix := extractBashPrefix(cmd); prefix != "" {
-				colonIdx := strings.Index(prefix, ":")
-				if colonIdx != -1 {
-					cmdName := prefix[:colonIdx]
-					dirPath := prefix[colonIdx+1:]
-					if dirPath != "./" {
-						allowlistInfo = fmt.Sprintf("%s in %s directory (includes subdirs)", cmdName, dirPath)
-					} else {
-						allowlistInfo = fmt.Sprintf("%s in %s directory", cmdName, dirPath)
-					}
-				}
-			}
+			isWarning = isCommandOutsideCwd(cmd)
 		}
 	}
 
 	// Run interactive selector
-	selected, denyReason, err := runSelector(fd, oldState, toolDisplay, isWarning, warningMsg, allowlistInfo)
+	selected, denyReason, err := runSelector(fd, oldState, toolDisplay, isWarning)
 	if err != nil {
 		term.Restore(fd, oldState)
 		return ApprovalResult{Decision: ApprovalDeny}, err
@@ -544,12 +509,11 @@ func (a *ApprovalManager) RequestApproval(toolName string, args map[string]any)
 // formatToolDisplay creates the display string for a tool call.
 func formatToolDisplay(toolName string, args map[string]any) string {
 	var sb strings.Builder
-	displayName := ToolDisplayName(toolName)
 
 	// For bash, show command directly
 	if toolName == "bash" {
 		if cmd, ok := args["command"].(string); ok {
-			sb.WriteString(fmt.Sprintf("Tool: %s\n", displayName))
+			sb.WriteString(fmt.Sprintf("Tool: %s\n", toolName))
 			sb.WriteString(fmt.Sprintf("Command: %s", cmd))
 			return sb.String()
 		}
@@ -558,7 +522,7 @@ func formatToolDisplay(toolName string, args map[string]any) string {
 	// For web search, show query and internet notice
 	if toolName == "web_search" {
 		if query, ok := args["query"].(string); ok {
-			sb.WriteString(fmt.Sprintf("Tool: %s\n", displayName))
+			sb.WriteString(fmt.Sprintf("Tool: %s\n", toolName))
 			sb.WriteString(fmt.Sprintf("Query: %s\n", query))
 			sb.WriteString("Uses internet via ollama.com")
 			return sb.String()
@@ -566,7 +530,7 @@ func formatToolDisplay(toolName string, args map[string]any) string {
 	}
 
 	// Generic display
-	sb.WriteString(fmt.Sprintf("Tool: %s", displayName))
+	sb.WriteString(fmt.Sprintf("Tool: %s", toolName))
 	if len(args) > 0 {
 		sb.WriteString("\nArguments: ")
 		first := true
@@ -583,28 +547,24 @@ func formatToolDisplay(toolName string, args map[string]any) string {
 
 // selectorState holds the state for the interactive selector
 type selectorState struct {
-	toolDisplay    string
-	selected       int
-	totalLines     int
-	termWidth      int
-	termHeight     int
-	boxWidth       int
-	innerWidth     int
-	denyReason     string // deny reason (always visible in box)
-	isWarning      bool   // true if command has warning
-	warningMessage string // dynamic warning message to display
-	allowlistInfo  string // show what will be allowlisted (for "Allow for this session" option)
+	toolDisplay string
+	selected    int
+	totalLines  int
+	termWidth   int
+	termHeight  int
+	boxWidth    int
+	innerWidth  int
+	denyReason  string // deny reason (always visible in box)
+	isWarning   bool   // true if command targets paths outside cwd (red box)
 }
 
 // runSelector runs the interactive selector and returns the selected index and optional deny reason.
 // If isWarning is true, the box is rendered in red to indicate the command targets paths outside cwd.
-func runSelector(fd int, oldState *term.State, toolDisplay string, isWarning bool, warningMessage string, allowlistInfo string) (int, string, error) {
+func runSelector(fd int, oldState *term.State, toolDisplay string, isWarning bool) (int, string, error) {
 	state := &selectorState{
-		toolDisplay:    toolDisplay,
-		selected:       0,
-		isWarning:      isWarning,
-		warningMessage: warningMessage,
-		allowlistInfo:  allowlistInfo,
+		toolDisplay: toolDisplay,
+		selected:    0,
+		isWarning:   isWarning,
 	}
 
 	// Get terminal size
@@ -764,7 +724,7 @@ func wrapText(text string, maxWidth int) []string {
 
 // getHintLines returns the hint text wrapped to terminal width
 func getHintLines(state *selectorState) []string {
-	hint := "up/down select, enter confirm, 1-3 quick select, ctrl+c cancel"
+	hint := "↑/↓ navigate, Enter confirm, 1-3 quick, Ctrl+C cancel"
 	if state.termWidth >= len(hint)+1 {
 		return []string{hint}
 	}
@@ -774,70 +734,86 @@ func getHintLines(state *selectorState) []string {
 
 // calculateTotalLines calculates how many lines the selector will use
 func calculateTotalLines(state *selectorState) int {
-	toolLines := strings.Split(state.toolDisplay, "\n")
+	toolLines := wrapText(state.toolDisplay, state.innerWidth)
 	hintLines := getHintLines(state)
-	// warning line (if applicable) + tool lines + blank line + options + blank line + hint lines
+	// top border + (warning line if applicable) + tool lines + separator + options + bottom border + hint lines
 	warningLines := 0
 	if state.isWarning {
-		warningLines = 2 // warning line + blank line after
+		warningLines = 1
 	}
-	return warningLines + len(toolLines) + 1 + len(optionLabels) + 1 + len(hintLines)
+	return 1 + warningLines + len(toolLines) + 1 + len(optionLabels) + 1 + len(hintLines)
 }
 
-// renderSelectorBox renders the selector (minimal, no box)
+// renderSelectorBox renders the complete selector box
 func renderSelectorBox(state *selectorState) {
-	toolLines := strings.Split(state.toolDisplay, "\n")
+	toolLines := wrapText(state.toolDisplay, state.innerWidth)
 	hintLines := getHintLines(state)
 
-	// Draw warning line if needed
+	// Use red for warning (outside cwd), cyan for normal
+	boxColor := "\033[36m" // cyan
 	if state.isWarning {
-		if state.warningMessage != "" {
-			fmt.Fprintf(os.Stderr, "\033[1mwarning:\033[0m %s\033[K\r\n", state.warningMessage)
-		} else {
-			fmt.Fprintf(os.Stderr, "\033[1mwarning:\033[0m command targets paths outside project\033[K\r\n")
+		boxColor = "\033[91m" // bright red
+	}
+
+	// Draw box top
+	fmt.Fprintf(os.Stderr, "%s┌%s┐\033[0m\033[K\r\n", boxColor, strings.Repeat("─", state.boxWidth-2))
+
+	// Draw warning line if needed (inside the box)
+	if state.isWarning {
+		warning := "!! OUTSIDE PROJECT !!"
+		padding := (state.innerWidth - len(warning)) / 2
+		if padding < 0 {
+			padding = 0
 		}
-		fmt.Fprintf(os.Stderr, "\033[K\r\n") // blank line after warning
+		fmt.Fprintf(os.Stderr, "%s│\033[0m %s%s%s %s│\033[0m\033[K\r\n", boxColor,
+			strings.Repeat(" ", padding), warning, strings.Repeat(" ", state.innerWidth-len(warning)-padding), boxColor)
 	}
 
-	// Draw tool info (plain white)
+	// Draw tool info
 	for _, line := range toolLines {
-		fmt.Fprintf(os.Stderr, "%s\033[K\r\n", line)
+		fmt.Fprintf(os.Stderr, "%s│\033[0m %-*s %s│\033[0m\033[K\r\n", boxColor, state.innerWidth, line, boxColor)
 	}
 
-	// Blank line separator
-	fmt.Fprintf(os.Stderr, "\033[K\r\n")
+	// Draw separator
+	fmt.Fprintf(os.Stderr, "%s├%s┤\033[0m\033[K\r\n", boxColor, strings.Repeat("─", state.boxWidth-2))
 
+	// Draw options with numbers (Deny option includes reason input)
 	for i, label := range optionLabels {
-		if i == 2 {
+		if i == 2 { // Deny option - show with reason input beside it
 			denyLabel := "3. Deny: "
+			availableWidth := state.innerWidth - 2 - len(denyLabel)
+			if availableWidth < 5 {
+				availableWidth = 5
+			}
 			inputDisplay := state.denyReason
-			if inputDisplay == "" {
-				inputDisplay = "\033[90m(optional reason)\033[0m"
+			if len(inputDisplay) > availableWidth {
+				inputDisplay = inputDisplay[len(inputDisplay)-availableWidth:]
 			}
 			if i == state.selected {
-				fmt.Fprintf(os.Stderr, "  \033[1m%s\033[0m%s\033[K\r\n", denyLabel, inputDisplay)
+				fmt.Fprintf(os.Stderr, "%s│\033[0m \033[1;32m> %s\033[0m%-*s %s│\033[0m\033[K\r\n", boxColor, denyLabel, availableWidth, inputDisplay, boxColor)
 			} else {
-				fmt.Fprintf(os.Stderr, "  \033[37m%s\033[0m%s\033[K\r\n", denyLabel, inputDisplay)
+				fmt.Fprintf(os.Stderr, "%s│\033[0m   \033[90m%s\033[0m%-*s %s│\033[0m\033[K\r\n", boxColor, denyLabel, availableWidth, inputDisplay, boxColor)
 			}
 		} else {
 			displayLabel := label
-			if i == 1 && state.allowlistInfo != "" {
-				displayLabel = fmt.Sprintf("%s  \033[90m%s\033[0m", label, state.allowlistInfo)
+			if len(displayLabel) > state.innerWidth-2 {
+				displayLabel = displayLabel[:state.innerWidth-5] + "..."
 			}
 			if i == state.selected {
-				fmt.Fprintf(os.Stderr, "  \033[1m%s\033[0m\033[K\r\n", displayLabel)
+				fmt.Fprintf(os.Stderr, "%s│\033[0m \033[1;32m> %-*s\033[0m %s│\033[0m\033[K\r\n", boxColor, state.innerWidth-2, displayLabel, boxColor)
 			} else {
-				fmt.Fprintf(os.Stderr, "  \033[37m%s\033[0m\033[K\r\n", displayLabel)
+				fmt.Fprintf(os.Stderr, "%s│\033[0m   %-*s %s│\033[0m\033[K\r\n", boxColor, state.innerWidth-2, displayLabel, boxColor)
 			}
 		}
 	}
 
-	// Blank line before hint
-	fmt.Fprintf(os.Stderr, "\033[K\r\n")
+	// Draw box bottom
+	fmt.Fprintf(os.Stderr, "%s└%s┘\033[0m\033[K\r\n", boxColor, strings.Repeat("─", state.boxWidth-2))
 
-	// Draw hint (dark grey)
+	// Draw hint (may be multiple lines)
 	for i, line := range hintLines {
 		if i == len(hintLines)-1 {
+			// Last line - no newline
 			fmt.Fprintf(os.Stderr, "\033[90m%s\033[0m\033[K", line)
 		} else {
 			fmt.Fprintf(os.Stderr, "\033[90m%s\033[0m\033[K\r\n", line)
@@ -849,39 +825,50 @@ func renderSelectorBox(state *selectorState) {
 func updateSelectorOptions(state *selectorState) {
 	hintLines := getHintLines(state)
 
+	// Use red for warning (outside cwd), cyan for normal
+	boxColor := "\033[36m" // cyan
+	if state.isWarning {
+		boxColor = "\033[91m" // bright red
+	}
+
 	// Move up to the first option line
 	// Cursor is at end of last hint line, need to go up:
-	// (hint lines - 1) + 1 (blank line) + numOptions
+	// (hint lines - 1) + 1 (bottom border) + numOptions
 	linesToMove := len(hintLines) - 1 + 1 + len(optionLabels)
 	fmt.Fprintf(os.Stderr, "\033[%dA\r", linesToMove)
 
+	// Redraw options (Deny option includes reason input)
 	for i, label := range optionLabels {
-		if i == 2 {
+		if i == 2 { // Deny option
 			denyLabel := "3. Deny: "
+			availableWidth := state.innerWidth - 2 - len(denyLabel)
+			if availableWidth < 5 {
+				availableWidth = 5
+			}
 			inputDisplay := state.denyReason
-			if inputDisplay == "" {
-				inputDisplay = "\033[90m(optional reason)\033[0m"
+			if len(inputDisplay) > availableWidth {
+				inputDisplay = inputDisplay[len(inputDisplay)-availableWidth:]
 			}
 			if i == state.selected {
-				fmt.Fprintf(os.Stderr, "  \033[1m%s\033[0m%s\033[K\r\n", denyLabel, inputDisplay)
+				fmt.Fprintf(os.Stderr, "%s│\033[0m \033[1;32m> %s\033[0m%-*s %s│\033[0m\033[K\r\n", boxColor, denyLabel, availableWidth, inputDisplay, boxColor)
 			} else {
-				fmt.Fprintf(os.Stderr, "  \033[37m%s\033[0m%s\033[K\r\n", denyLabel, inputDisplay)
+				fmt.Fprintf(os.Stderr, "%s│\033[0m   \033[90m%s\033[0m%-*s %s│\033[0m\033[K\r\n", boxColor, denyLabel, availableWidth, inputDisplay, boxColor)
 			}
 		} else {
 			displayLabel := label
-			if i == 1 && state.allowlistInfo != "" {
-				displayLabel = fmt.Sprintf("%s  \033[90m%s\033[0m", label, state.allowlistInfo)
+			if len(displayLabel) > state.innerWidth-2 {
+				displayLabel = displayLabel[:state.innerWidth-5] + "..."
 			}
 			if i == state.selected {
-				fmt.Fprintf(os.Stderr, "  \033[1m%s\033[0m\033[K\r\n", displayLabel)
+				fmt.Fprintf(os.Stderr, "%s│\033[0m \033[1;32m> %-*s\033[0m %s│\033[0m\033[K\r\n", boxColor, state.innerWidth-2, displayLabel, boxColor)
 			} else {
-				fmt.Fprintf(os.Stderr, "  \033[37m%s\033[0m\033[K\r\n", displayLabel)
+				fmt.Fprintf(os.Stderr, "%s│\033[0m   %-*s %s│\033[0m\033[K\r\n", boxColor, state.innerWidth-2, displayLabel, boxColor)
 			}
 		}
 	}
 
-	// Blank line + hint
-	fmt.Fprintf(os.Stderr, "\033[K\r\n")
+	// Redraw bottom and hint
+	fmt.Fprintf(os.Stderr, "%s└%s┘\033[0m\033[K\r\n", boxColor, strings.Repeat("─", state.boxWidth-2))
 	for i, line := range hintLines {
 		if i == len(hintLines)-1 {
 			fmt.Fprintf(os.Stderr, "\033[90m%s\033[0m\033[K", line)
@@ -895,26 +882,36 @@ func updateSelectorOptions(state *selectorState) {
 func updateReasonInput(state *selectorState) {
 	hintLines := getHintLines(state)
 
+	// Use red for warning (outside cwd), cyan for normal
+	boxColor := "\033[36m" // cyan
+	if state.isWarning {
+		boxColor = "\033[91m" // bright red
+	}
+
 	// Move up to the Deny line (3rd option, index 2)
 	// Cursor is at end of last hint line, need to go up:
-	// (hint lines - 1) + 1 (blank line) + 1 (Deny is last option)
+	// (hint lines - 1) + 1 (bottom border) + 1 (Deny is last option)
 	linesToMove := len(hintLines) - 1 + 1 + 1
 	fmt.Fprintf(os.Stderr, "\033[%dA\r", linesToMove)
 
 	// Redraw Deny line with reason
 	denyLabel := "3. Deny: "
+	availableWidth := state.innerWidth - 2 - len(denyLabel)
+	if availableWidth < 5 {
+		availableWidth = 5
+	}
 	inputDisplay := state.denyReason
-	if inputDisplay == "" {
-		inputDisplay = "\033[90m(optional reason)\033[0m"
+	if len(inputDisplay) > availableWidth {
+		inputDisplay = inputDisplay[len(inputDisplay)-availableWidth:]
 	}
 	if state.selected == 2 {
-		fmt.Fprintf(os.Stderr, "  \033[1m%s\033[0m%s\033[K\r\n", denyLabel, inputDisplay)
+		fmt.Fprintf(os.Stderr, "%s│\033[0m \033[1;32m> %s\033[0m%-*s %s│\033[0m\033[K\r\n", boxColor, denyLabel, availableWidth, inputDisplay, boxColor)
 	} else {
-		fmt.Fprintf(os.Stderr, "  \033[37m%s\033[0m%s\033[K\r\n", denyLabel, inputDisplay)
+		fmt.Fprintf(os.Stderr, "%s│\033[0m   \033[90m%s\033[0m%-*s %s│\033[0m\033[K\r\n", boxColor, denyLabel, availableWidth, inputDisplay, boxColor)
 	}
 
-	// Blank line + hint
-	fmt.Fprintf(os.Stderr, "\033[K\r\n")
+	// Redraw bottom and hint
+	fmt.Fprintf(os.Stderr, "%s└%s┘\033[0m\033[K\r\n", boxColor, strings.Repeat("─", state.boxWidth-2))
 	for i, line := range hintLines {
 		if i == len(hintLines)-1 {
 			fmt.Fprintf(os.Stderr, "\033[90m%s\033[0m\033[K", line)
@@ -938,10 +935,11 @@ func clearSelectorBox(state *selectorState) {
 // fallbackApproval handles approval when terminal control isn't available.
 func (a *ApprovalManager) fallbackApproval(toolDisplay string) (ApprovalResult, error) {
 	fmt.Fprintln(os.Stderr)
+	fmt.Fprintln(os.Stderr, "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━")
 	fmt.Fprintln(os.Stderr, toolDisplay)
-	fmt.Fprintln(os.Stderr)
-	fmt.Fprintln(os.Stderr, "[1] Execute once  [2] Allow for this session  [3] Deny")
-	fmt.Fprint(os.Stderr, "choice: ")
+	fmt.Fprintln(os.Stderr, "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━")
+	fmt.Fprintln(os.Stderr, "[1] Execute once  [2] Always allow  [3] Deny")
+	fmt.Fprint(os.Stderr, "Choice: ")
 
 	var input string
 	fmt.Scanln(&input)
@@ -984,16 +982,19 @@ func (a *ApprovalManager) AllowedTools() []string {
 
 // FormatApprovalResult returns a formatted string showing the approval result.
 func FormatApprovalResult(toolName string, args map[string]any, result ApprovalResult) string {
-	var label string
-	displayName := ToolDisplayName(toolName)
+	var status string
+	var icon string
 
 	switch result.Decision {
 	case ApprovalOnce:
-		label = "Approved"
+		status = "Approved"
+		icon = "\033[32m✓\033[0m"
 	case ApprovalAlways:
-		label = "Always allowed"
+		status = "Always allowed"
+		icon = "\033[32m✓\033[0m"
 	case ApprovalDeny:
-		label = "Denied"
+		status = "Denied"
+		icon = "\033[31m✗\033[0m"
 	}
 
 	// Format based on tool type
@@ -1003,7 +1004,7 @@ func FormatApprovalResult(toolName string, args map[string]any, result ApprovalR
 			if len(cmd) > 40 {
 				cmd = cmd[:37] + "..."
 			}
-			return fmt.Sprintf("\033[1m%s:\033[0m %s: %s", label, displayName, cmd)
+			return fmt.Sprintf("▶ bash: %s [%s] %s", cmd, status, icon)
 		}
 	}
 
@@ -1013,11 +1014,11 @@ func FormatApprovalResult(toolName string, args map[string]any, result ApprovalR
 			if len(query) > 40 {
 				query = query[:37] + "..."
 			}
-			return fmt.Sprintf("\033[1m%s:\033[0m %s: %s", label, displayName, query)
+			return fmt.Sprintf("▶ web_search: %s [%s] %s", query, status, icon)
 		}
 	}
 
-	return fmt.Sprintf("\033[1m%s:\033[0m %s", label, displayName)
+	return fmt.Sprintf("▶ %s [%s] %s", toolName, status, icon)
 }
 
 // FormatDenyResult returns the tool result message when a tool is denied.
@@ -1048,14 +1049,15 @@ func PromptYesNo(question string) (bool, error) {
 	renderYesNo := func() {
 		// Move to start of line and clear
 		fmt.Fprintf(os.Stderr, "\r\033[K")
-		fmt.Fprintf(os.Stderr, "%s  ", question)
+		fmt.Fprintf(os.Stderr, "\033[36m%s\033[0m ", question)
 		for i, opt := range options {
 			if i == selected {
-				fmt.Fprintf(os.Stderr, "\033[1m%s\033[0m  ", opt)
+				fmt.Fprintf(os.Stderr, "\033[1;32m[%s]\033[0m ", opt)
 			} else {
-				fmt.Fprintf(os.Stderr, "\033[37m%s\033[0m  ", opt)
+				fmt.Fprintf(os.Stderr, "\033[90m %s \033[0m ", opt)
 			}
 		}
+		fmt.Fprintf(os.Stderr, "\033[90m(←/→ or y/n, Enter to confirm)\033[0m")
 	}
 
 	renderYesNo()
@@ -1102,3 +1104,108 @@ func PromptYesNo(question string) (bool, error) {
 		}
 	}
 }
+
+// CloudModelOption represents a suggested cloud model for the selection prompt.
+type CloudModelOption struct {
+	Name        string
+	Description string
+}
+
+// PromptModelChoice displays a model selection prompt with multiple options.
+// Returns the selected model name, or empty string if user declined or cancelled.
+func PromptModelChoice(question string, models []CloudModelOption) (string, error) {
+	fd := int(os.Stdin.Fd())
+	oldState, err := term.MakeRaw(fd)
+	if err != nil {
+		return "", err
+	}
+	defer term.Restore(fd, oldState)
+
+	// Build options: models + "No thanks, continue"
+	optionCount := len(models) + 1
+	selected := 0
+
+	// Total lines: question + models + "no thanks" + hint = optionCount + 2
+	totalLines := optionCount + 2
+
+	// Hide cursor
+	fmt.Fprint(os.Stderr, "\033[?25l")
+	defer fmt.Fprint(os.Stderr, "\033[?25h")
+
+	firstRender := true
+
+	render := func() {
+		if !firstRender {
+			fmt.Fprintf(os.Stderr, "\033[%dA\r", totalLines-1)
+		}
+		firstRender = false
+
+		// \r\n needed in raw mode for proper line breaks
+		fmt.Fprintf(os.Stderr, "\033[K\033[36m%s\033[0m\r\n", question)
+
+		for i, model := range models {
+			fmt.Fprintf(os.Stderr, "\033[K")
+			if i == selected {
+				fmt.Fprintf(os.Stderr, "  \033[1;32m> %s\033[0m  \033[90m%s\033[0m\r\n", model.Name, model.Description)
+			} else {
+				fmt.Fprintf(os.Stderr, "    \033[90m%s  %s\033[0m\r\n", model.Name, model.Description)
+			}
+		}
+
+		fmt.Fprintf(os.Stderr, "\033[K")
+		if selected == len(models) {
+			fmt.Fprintf(os.Stderr, "  \033[1;32m> No thanks, continue\033[0m\r\n")
+		} else {
+			fmt.Fprintf(os.Stderr, "    \033[90mNo thanks, continue\033[0m\r\n")
+		}
+
+		fmt.Fprintf(os.Stderr, "\033[K\033[90m(↑/↓ to navigate, Enter to confirm)\033[0m")
+	}
+
+	render()
+
+	buf := make([]byte, 3)
+	for {
+		n, err := os.Stdin.Read(buf)
+		if err != nil {
+			return "", err
+		}
+
+		if n == 1 {
+			switch buf[0] {
+			case 'j', 'J':
+				if selected < optionCount-1 {
+					selected++
+				}
+				render()
+			case 'k', 'K':
+				if selected > 0 {
+					selected--
+				}
+				render()
+			case '\r', '\n':
+				fmt.Fprintf(os.Stderr, "\n")
+				if selected < len(models) {
+					return models[selected].Name, nil
+				}
+				return "", nil
+			case 3: // Ctrl+C
+				fmt.Fprintf(os.Stderr, "\n")
+				return "", nil
+			}
+		} else if n == 3 && buf[0] == 27 && buf[1] == 91 {
+			switch buf[2] {
+			case 'A': // Up
+				if selected > 0 {
+					selected--
+				}
+				render()
+			case 'B': // Down
+				if selected < optionCount-1 {
+					selected++
+				}
+				render()
+			}
+		}
+	}
+}

x/agent/prompt_test.go

@@ -0,0 +1,25 @@
+package agent
+
+import (
+	"testing"
+)
+
+func TestCloudModelOptionStruct(t *testing.T) {
+	// Test that the struct is defined correctly
+	models := []CloudModelOption{
+		{Name: "glm-4.7:cloud", Description: "GLM 4.7 Cloud"},
+		{Name: "qwen3-coder:480b-cloud", Description: "Qwen3 Coder 480B"},
+	}
+
+	if len(models) != 2 {
+		t.Errorf("expected 2 models, got %d", len(models))
+	}
+
+	if models[0].Name != "glm-4.7:cloud" {
+		t.Errorf("expected glm-4.7:cloud, got %s", models[0].Name)
+	}
+
+	if models[1].Description != "Qwen3 Coder 480B" {
+		t.Errorf("expected 'Qwen3 Coder 480B', got %s", models[1].Description)
+	}
+}

x/cmd/cloudmodel_test.go

@@ -0,0 +1,41 @@
+package cmd
+
+import (
+	"errors"
+	"testing"
+)
+
+func TestCloudModelSwitchRequest(t *testing.T) {
+	// Test the error type
+	req := &CloudModelSwitchRequest{Model: "glm-4.7:cloud"}
+
+	// Test Error() method
+	errMsg := req.Error()
+	expected := "switch to model: glm-4.7:cloud"
+	if errMsg != expected {
+		t.Errorf("expected %q, got %q", expected, errMsg)
+	}
+
+	// Test errors.As
+	var err error = req
+	var switchReq *CloudModelSwitchRequest
+	if !errors.As(err, &switchReq) {
+		t.Error("errors.As should return true for CloudModelSwitchRequest")
+	}
+
+	if switchReq.Model != "glm-4.7:cloud" {
+		t.Errorf("expected model glm-4.7:cloud, got %s", switchReq.Model)
+	}
+}
+
+func TestSuggestedCloudModels(t *testing.T) {
+	// Verify the suggested models are defined
+	if len(suggestedCloudModels) == 0 {
+		t.Error("suggestedCloudModels should not be empty")
+	}
+
+	// Check first model
+	if suggestedCloudModels[0].Name != "glm-4.7:cloud" {
+		t.Errorf("expected first model to be glm-4.7:cloud, got %s", suggestedCloudModels[0].Name)
+	}
+}

x/cmd/run.go

@@ -37,6 +37,22 @@ const (
 	charsPerToken = 4
 )
 
+// suggestedCloudModels are the models suggested to users after signing in.
+// TODO(parthsareen): Dynamically recommend models based on user context instead of hardcoding
+var suggestedCloudModels = []agent.CloudModelOption{
+	{Name: "glm-4.7:cloud", Description: "GLM 4.7 Cloud"},
+	{Name: "qwen3-coder:480b-cloud", Description: "Qwen3 Coder 480B"},
+}
+
+// CloudModelSwitchRequest signals that the user wants to switch to a different model.
+type CloudModelSwitchRequest struct {
+	Model string
+}
+
+func (c *CloudModelSwitchRequest) Error() string {
+	return fmt.Sprintf("switch to model: %s", c.Model)
+}
+
 // isLocalModel checks if the model is running locally (not a cloud model).
 // TODO: Improve local/cloud model identification - could check model metadata
 func isLocalModel(modelName string) bool {
@@ -91,8 +107,8 @@ func waitForOllamaSignin(ctx context.Context) error {
 		var aErr api.AuthorizationError
 		if errors.As(err, &aErr) && aErr.SigninURL != "" {
 			fmt.Fprintf(os.Stderr, "\n  To sign in, navigate to:\n")
-			fmt.Fprintf(os.Stderr, "      %s\n\n", aErr.SigninURL)
-			fmt.Fprintf(os.Stderr, "  \033[90mwaiting for sign in to complete...\033[0m")
+			fmt.Fprintf(os.Stderr, "      \033[36m%s\033[0m\n\n", aErr.SigninURL)
+			fmt.Fprintf(os.Stderr, "  \033[90mWaiting for sign in to complete...\033[0m")
 
 			// Poll until auth succeeds
 			ticker := time.NewTicker(2 * time.Second)
@@ -106,7 +122,7 @@ func waitForOllamaSignin(ctx context.Context) error {
 				case <-ticker.C:
 					user, whoamiErr := client.Whoami(ctx)
 					if whoamiErr == nil && user != nil && user.Name != "" {
-						fmt.Fprintf(os.Stderr, "\r\033[K\033[A\r\033[K  \033[1msigned in:\033[0m %s\n", user.Name)
+						fmt.Fprintf(os.Stderr, "\r\033[K  \033[32mSigned in as %s\033[0m\n", user.Name)
 						return nil
 					}
 					// Still waiting, show dot
@@ -119,6 +135,21 @@ func waitForOllamaSignin(ctx context.Context) error {
 	return nil
 }
 
+// promptCloudModelSuggestion shows cloud model suggestions after successful sign-in.
+// Returns the selected model name, or empty string if user declines.
+func promptCloudModelSuggestion() string {
+	fmt.Fprintf(os.Stderr, "\n")
+	fmt.Fprintf(os.Stderr, "\033[1;36mTry cloud models for free!\033[0m\n")
+	fmt.Fprintf(os.Stderr, "\033[90mCloud models offer powerful capabilities without local hardware requirements.\033[0m\n")
+	fmt.Fprintf(os.Stderr, "\n")
+
+	selectedModel, err := agent.PromptModelChoice("Try a cloud model now?", suggestedCloudModels)
+	if err != nil || selectedModel == "" {
+		return ""
+	}
+	return selectedModel
+}
+
 // RunOptions contains options for running an interactive agent session.
 type RunOptions struct {
 	Model        string
@@ -137,6 +168,47 @@ type RunOptions struct {
 
 	// YoloMode skips all tool approval prompts
 	YoloMode bool
+
+	// LastToolOutput stores the full output of the last tool execution
+	// for Ctrl+O expansion. Updated by Chat(), read by caller.
+	LastToolOutput *string
+
+	// LastToolOutputTruncated stores the truncated version shown inline
+	LastToolOutputTruncated *string
+
+	// ActiveModel points to the current model name - can be updated mid-turn
+	// for model switching. If nil, opts.Model is used.
+	ActiveModel *string
+}
+
+// getActiveModel returns the current model name, checking ActiveModel pointer first.
+func getActiveModel(opts *RunOptions) string {
+	if opts.ActiveModel != nil && *opts.ActiveModel != "" {
+		return *opts.ActiveModel
+	}
+	return opts.Model
+}
+
+// showModelConnection displays "Connecting to X on ollama.com" for cloud models.
+func showModelConnection(ctx context.Context, modelName string) error {
+	client, err := api.ClientFromEnvironment()
+	if err != nil {
+		return err
+	}
+
+	info, err := client.Show(ctx, &api.ShowRequest{Model: modelName})
+	if err != nil {
+		return err
+	}
+
+	if info.RemoteHost != "" {
+		if strings.HasPrefix(info.RemoteHost, "https://ollama.com") {
+			fmt.Fprintf(os.Stderr, "Connecting to '%s' on 'ollama.com' ⚡\n", info.RemoteModel)
+		} else {
+			fmt.Fprintf(os.Stderr, "Connecting to '%s' on '%s'\n", info.RemoteModel, info.RemoteHost)
+		}
+	}
+	return nil
 }
 
 // Chat runs an agent chat loop with tool support.
@@ -236,7 +308,7 @@ func Chat(ctx context.Context, opts RunOptions) (*api.Message, error) {
 	// Agentic loop: continue until no more tool calls
 	for {
 		req := &api.ChatRequest{
-			Model:    opts.Model,
+			Model:    getActiveModel(&opts),
 			Messages: messages,
 			Format:   json.RawMessage(opts.Format),
 			Options:  opts.Options,
@@ -260,17 +332,20 @@ func Chat(ctx context.Context, opts RunOptions) (*api.Message, error) {
 				return nil, nil
 			}
 
-			// Check for 401 Unauthorized - prompt user to sign in
 			var authErr api.AuthorizationError
 			if errors.As(err, &authErr) {
 				p.StopAndClear()
-				fmt.Fprintf(os.Stderr, "\033[1mauth required:\033[0m cloud model requires authentication\n")
+				fmt.Fprintf(os.Stderr, "\033[33mAuthentication required to use this cloud model.\033[0m\n")
 				result, promptErr := agent.PromptYesNo("Sign in to Ollama?")
 				if promptErr == nil && result {
 					if signinErr := waitForOllamaSignin(ctx); signinErr == nil {
-						// Retry the chat request
-						fmt.Fprintf(os.Stderr, "\033[90mretrying...\033[0m\n")
-						continue // Retry the loop
+						suggestedModel := promptCloudModelSuggestion()
+						if suggestedModel != "" {
+							return nil, &CloudModelSwitchRequest{Model: suggestedModel}
+						}
+
+						fmt.Fprintf(os.Stderr, "\033[90mRetrying...\033[0m\n")
+						continue
 					}
 				}
 				return nil, fmt.Errorf("authentication required - run 'ollama signin' to authenticate")
@@ -283,11 +358,11 @@ func Chat(ctx context.Context, opts RunOptions) (*api.Message, error) {
 				p.StopAndClear()
 
 				if consecutiveErrors >= 3 {
-					fmt.Fprintf(os.Stderr, "\033[1merror:\033[0m too many consecutive errors, giving up\n")
+					fmt.Fprintf(os.Stderr, "\033[31m✗ Too many consecutive errors, giving up\033[0m\n")
 					return nil, fmt.Errorf("too many consecutive server errors: %s", statusErr.ErrorMessage)
 				}
 
-				fmt.Fprintf(os.Stderr, "\033[1mwarning:\033[0m server error (attempt %d/3): %s\n", consecutiveErrors, statusErr.ErrorMessage)
+				fmt.Fprintf(os.Stderr, "\033[33m⚠ Server error (attempt %d/3): %s\033[0m\n", consecutiveErrors, statusErr.ErrorMessage)
 
 				// Include both the model's response and the error so it can learn
 				assistantContent := fullResponse.String()
@@ -353,8 +428,8 @@ func Chat(ctx context.Context, opts RunOptions) (*api.Message, error) {
 				if cmd, ok := args["command"].(string); ok {
 					// Check if command is denied (dangerous pattern)
 					if denied, pattern := agent.IsDenied(cmd); denied {
-						fmt.Fprintf(os.Stderr, "\033[1mblocked:\033[0m %s\n", formatToolShort(toolName, args))
-						fmt.Fprintf(os.Stderr, "  matches dangerous pattern: %s\n", pattern)
+						fmt.Fprintf(os.Stderr, "\033[91m✗ Blocked: %s\033[0m\n", formatToolShort(toolName, args))
+						fmt.Fprintf(os.Stderr, "\033[91m  Matches dangerous pattern: %s\033[0m\n", pattern)
 						toolResults = append(toolResults, api.Message{
 							Role:       "tool",
 							Content:    agent.FormatDeniedResult(cmd, pattern),
@@ -364,11 +439,10 @@ func Chat(ctx context.Context, opts RunOptions) (*api.Message, error) {
 					}
 
 					// Check if command is auto-allowed (safe command)
-					// TODO(parthsareen): re-enable with tighter scoped allowlist
-					// if agent.IsAutoAllowed(cmd) {
-					// 	fmt.Fprintf(os.Stderr, "\033[1mauto-allowed:\033[0m %s\n", formatToolShort(toolName, args))
-					// 	skipApproval = true
-					// }
+					if agent.IsAutoAllowed(cmd) {
+						fmt.Fprintf(os.Stderr, "\033[90m▶ Auto-allowed: %s\033[0m\n", formatToolShort(toolName, args))
+						skipApproval = true
+					}
 				}
 			}
 
@@ -376,7 +450,7 @@ func Chat(ctx context.Context, opts RunOptions) (*api.Message, error) {
 			// In yolo mode, skip all approval prompts
 			if opts.YoloMode {
 				if !skipApproval {
-					fmt.Fprintf(os.Stderr, "\033[1mrunning:\033[0m %s\n", formatToolShort(toolName, args))
+					fmt.Fprintf(os.Stderr, "\033[90m▶ Running: %s\033[0m\n", formatToolShort(toolName, args))
 				}
 			} else if !skipApproval && !approval.IsAllowed(toolName, args) {
 				result, err := approval.RequestApproval(toolName, args)
@@ -406,22 +480,23 @@ func Chat(ctx context.Context, opts RunOptions) (*api.Message, error) {
 				}
 			} else if !skipApproval {
 				// Already allowed - show running indicator
-				fmt.Fprintf(os.Stderr, "\033[1mrunning:\033[0m %s\n", formatToolShort(toolName, args))
+				fmt.Fprintf(os.Stderr, "\033[90m▶ Running: %s\033[0m\n", formatToolShort(toolName, args))
 			}
 
-			// Execute the tool
 			toolResult, err := toolRegistry.Execute(call)
 			if err != nil {
-				// Check if web search needs authentication
 				if errors.Is(err, tools.ErrWebSearchAuthRequired) {
-					// Prompt user to sign in
-					fmt.Fprintf(os.Stderr, "\033[1mauth required:\033[0m web search requires authentication\n")
+					fmt.Fprintf(os.Stderr, "\033[33m  Web search requires authentication.\033[0m\n")
 					result, promptErr := agent.PromptYesNo("Sign in to Ollama?")
 					if promptErr == nil && result {
-						// Get signin URL and wait for auth completion
 						if signinErr := waitForOllamaSignin(ctx); signinErr == nil {
-							// Retry the web search
-							fmt.Fprintf(os.Stderr, "\033[90mretrying web search...\033[0m\n")
+							suggestedModel := promptCloudModelSuggestion()
+							if suggestedModel != "" && opts.ActiveModel != nil {
+								*opts.ActiveModel = suggestedModel
+								showModelConnection(ctx, suggestedModel)
+							}
+
+							fmt.Fprintf(os.Stderr, "\033[90mRetrying web search...\033[0m\n")
 							toolResult, err = toolRegistry.Execute(call)
 							if err == nil {
 								goto toolSuccess
@@ -429,7 +504,7 @@ func Chat(ctx context.Context, opts RunOptions) (*api.Message, error) {
 						}
 					}
 				}
-				fmt.Fprintf(os.Stderr, "\033[1merror:\033[0m %v\n", err)
+				fmt.Fprintf(os.Stderr, "\033[31m  Error: %v\033[0m\n", err)
 				toolResults = append(toolResults, api.Message{
 					Role:       "tool",
 					Content:    fmt.Sprintf("Error: %v", err),
@@ -440,17 +515,27 @@ func Chat(ctx context.Context, opts RunOptions) (*api.Message, error) {
 		toolSuccess:
 
 			// Display tool output (truncated for display)
+			truncatedOutput := ""
 			if toolResult != "" {
 				output := toolResult
 				if len(output) > 300 {
-					output = output[:300] + "... (truncated)"
+					output = output[:300] + "... (truncated, press Ctrl+O to expand)"
 				}
+				truncatedOutput = output
 				// Show result in grey, indented
 				fmt.Fprintf(os.Stderr, "\033[90m  %s\033[0m\n", strings.ReplaceAll(output, "\n", "\n  "))
 			}
 
+			// Store full and truncated output for Ctrl+O toggle
+			if opts.LastToolOutput != nil {
+				*opts.LastToolOutput = toolResult
+			}
+			if opts.LastToolOutputTruncated != nil {
+				*opts.LastToolOutputTruncated = truncatedOutput
+			}
+
 			// Truncate output to prevent context overflow
-			toolResultForLLM := truncateToolOutput(toolResult, opts.Model)
+			toolResultForLLM := truncateToolOutput(toolResult, getActiveModel(&opts))
 
 			toolResults = append(toolResults, api.Message{
 				Role:       "tool",
@@ -500,18 +585,17 @@ func truncateUTF8(s string, limit int) string {
 
 // formatToolShort returns a short description of a tool call.
 func formatToolShort(toolName string, args map[string]any) string {
-	displayName := agent.ToolDisplayName(toolName)
 	if toolName == "bash" {
 		if cmd, ok := args["command"].(string); ok {
-			return fmt.Sprintf("%s: %s", displayName, truncateUTF8(cmd, 50))
+			return fmt.Sprintf("bash: %s", truncateUTF8(cmd, 50))
 		}
 	}
 	if toolName == "web_search" {
 		if query, ok := args["query"].(string); ok {
-			return fmt.Sprintf("%s: %s", displayName, truncateUTF8(query, 50))
+			return fmt.Sprintf("web_search: %s", truncateUTF8(query, 50))
 		}
 	}
-	return displayName
+	return toolName
 }
 
 // Helper types and functions for display
@@ -610,25 +694,28 @@ func renderToolCalls(toolCalls []api.ToolCall, plainText bool) string {
 	return out
 }
 
-// checkModelCapabilities checks if the model supports tools.
-func checkModelCapabilities(ctx context.Context, modelName string) (supportsTools bool, err error) {
+// checkModelCapabilities checks if the model supports tools and thinking.
+func checkModelCapabilities(ctx context.Context, modelName string) (supportsTools bool, supportsThinking bool, err error) {
 	client, err := api.ClientFromEnvironment()
 	if err != nil {
-		return false, err
+		return false, false, err
 	}
 
 	resp, err := client.Show(ctx, &api.ShowRequest{Model: modelName})
 	if err != nil {
-		return false, err
+		return false, false, err
 	}
 
 	for _, cap := range resp.Capabilities {
 		if cap == model.CapabilityTools {
-			return true, nil
+			supportsTools = true
+		}
+		if cap == model.CapabilityThinking {
+			supportsThinking = true
 		}
 	}
 
-	return false, nil
+	return supportsTools, supportsThinking, nil
 }
 
 // GenerateInteractive runs an interactive agent session.
@@ -648,28 +735,29 @@ func GenerateInteractive(cmd *cobra.Command, modelName string, wordWrap bool, op
 	fmt.Print(readline.StartBracketedPaste)
 	defer fmt.Printf(readline.EndBracketedPaste)
 
-	// Check if model supports tools
-	supportsTools, err := checkModelCapabilities(cmd.Context(), modelName)
+	// Check if model supports tools and thinking
+	supportsTools, supportsThinking, err := checkModelCapabilities(cmd.Context(), modelName)
 	if err != nil {
-		fmt.Fprintf(os.Stderr, "\033[1mwarning:\033[0m could not check model capabilities: %v\n", err)
+		fmt.Fprintf(os.Stderr, "\033[33mWarning: Could not check model capabilities: %v\033[0m\n", err)
 		supportsTools = false
+		supportsThinking = false
 	}
 
+	// Track if session is using thinking mode
+	usingThinking := think != nil && supportsThinking
+
 	// Create tool registry only if model supports tools
 	var toolRegistry *tools.Registry
 	if supportsTools {
 		toolRegistry = tools.DefaultRegistry()
-
-		if toolRegistry.Has("bash") {
-			fmt.Fprintln(os.Stderr)
-			fmt.Fprintln(os.Stderr, "This experimental version of Ollama has the \033[1mbash\033[0m tool enabled.")
-			fmt.Fprintln(os.Stderr, "Models can read files on your computer, or run commands (after you allow them).")
-			fmt.Fprintln(os.Stderr)
+		if toolRegistry.Count() > 0 {
+			fmt.Fprintf(os.Stderr, "\033[90mTools available: %s\033[0m\n", strings.Join(toolRegistry.Names(), ", "))
 		}
-
 		if yoloMode {
-			fmt.Fprintf(os.Stderr, "\033[1mwarning:\033[0m yolo mode - all tool approvals will be skipped\n")
+			fmt.Fprintf(os.Stderr, "\033[33m⚠ YOLO mode: All tool approvals will be skipped\033[0m\n")
 		}
+	} else {
+		fmt.Fprintf(os.Stderr, "\033[33mNote: Model does not support tools - running in chat-only mode\033[0m\n")
 	}
 
 	// Create approval manager for session
@@ -678,6 +766,11 @@ func GenerateInteractive(cmd *cobra.Command, modelName string, wordWrap bool, op
 	var messages []api.Message
 	var sb strings.Builder
 
+	// Track last tool output for Ctrl+O toggle
+	var lastToolOutput string
+	var lastToolOutputTruncated string
+	var toolOutputExpanded bool
+
 	for {
 		line, err := scanner.Readline()
 		switch {
@@ -690,6 +783,20 @@ func GenerateInteractive(cmd *cobra.Command, modelName string, wordWrap bool, op
 			}
 			sb.Reset()
 			continue
+		case errors.Is(err, readline.ErrExpandOutput):
+			// Ctrl+O pressed - toggle between expanded and collapsed tool output
+			if lastToolOutput == "" {
+				fmt.Fprintf(os.Stderr, "\033[90mNo tool output to expand\033[0m\n")
+			} else if toolOutputExpanded {
+				// Currently expanded, show truncated
+				fmt.Fprintf(os.Stderr, "\033[90m  %s\033[0m\n", strings.ReplaceAll(lastToolOutputTruncated, "\n", "\n  "))
+				toolOutputExpanded = false
+			} else {
+				// Currently collapsed, show full
+				fmt.Fprintf(os.Stderr, "\033[90m  %s\033[0m\n", strings.ReplaceAll(lastToolOutput, "\n", "\n  "))
+				toolOutputExpanded = true
+			}
+			continue
 		case err != nil:
 			return err
 		}
@@ -726,26 +833,44 @@ func GenerateInteractive(cmd *cobra.Command, modelName string, wordWrap bool, op
 		if sb.Len() > 0 {
 			newMessage := api.Message{Role: "user", Content: sb.String()}
 			messages = append(messages, newMessage)
+			toolOutputExpanded = false
 
-			opts := RunOptions{
-				Model:        modelName,
-				Messages:     messages,
-				WordWrap:     wordWrap,
-				Options:      options,
-				Think:        think,
-				HideThinking: hideThinking,
-				KeepAlive:    keepAlive,
-				Tools:        toolRegistry,
-				Approval:     approval,
-				YoloMode:     yoloMode,
-			}
+		retryChat:
+			for {
+				opts := RunOptions{
+					Model:                   modelName,
+					Messages:                messages,
+					WordWrap:                wordWrap,
+					Options:                 options,
+					Think:                   think,
+					HideThinking:            hideThinking,
+					KeepAlive:               keepAlive,
+					Tools:                   toolRegistry,
+					Approval:                approval,
+					YoloMode:                yoloMode,
+					LastToolOutput:          &lastToolOutput,
+					LastToolOutputTruncated: &lastToolOutputTruncated,
+					ActiveModel:             &modelName,
+				}
 
-			assistant, err := Chat(cmd.Context(), opts)
-			if err != nil {
-				return err
-			}
-			if assistant != nil {
-				messages = append(messages, *assistant)
+				assistant, err := Chat(cmd.Context(), opts)
+				if err != nil {
+					var switchReq *CloudModelSwitchRequest
+					if errors.As(err, &switchReq) {
+						newModel := switchReq.Model
+						if err := switchToModel(cmd.Context(), newModel, &modelName, &supportsTools, &supportsThinking, &toolRegistry, usingThinking); err != nil {
+							fmt.Fprintf(os.Stderr, "\033[33m%v\033[0m\n", err)
+							fmt.Fprintf(os.Stderr, "\033[90mContinuing with %s...\033[0m\n", modelName)
+						}
+						continue retryChat
+					}
+					return err
+				}
+
+				if assistant != nil {
+					messages = append(messages, *assistant)
+				}
+				break retryChat
 			}
 
 			sb.Reset()
@@ -753,6 +878,52 @@ func GenerateInteractive(cmd *cobra.Command, modelName string, wordWrap bool, op
 	}
 }
 
+// switchToModel handles model switching with capability checks and UI updates.
+func switchToModel(ctx context.Context, newModel string, modelName *string, supportsTools, supportsThinking *bool, toolRegistry **tools.Registry, usingThinking bool) error {
+	client, err := api.ClientFromEnvironment()
+	if err != nil {
+		return fmt.Errorf("could not create client: %w", err)
+	}
+
+	newSupportsTools, newSupportsThinking, capErr := checkModelCapabilities(ctx, newModel)
+	if capErr != nil {
+		return fmt.Errorf("could not check model capabilities: %w", capErr)
+	}
+
+	// TODO(parthsareen): Handle thinking -> non-thinking model switch gracefully
+	if usingThinking && !newSupportsThinking {
+		return fmt.Errorf("%s does not support thinking mode", newModel)
+	}
+
+	// Show "Connecting to X on ollama.com" for cloud models
+	info, err := client.Show(ctx, &api.ShowRequest{Model: newModel})
+	if err == nil && info.RemoteHost != "" {
+		if strings.HasPrefix(info.RemoteHost, "https://ollama.com") {
+			fmt.Fprintf(os.Stderr, "Connecting to '%s' on 'ollama.com' ⚡\n", info.RemoteModel)
+		} else {
+			fmt.Fprintf(os.Stderr, "Connecting to '%s' on '%s'\n", info.RemoteModel, info.RemoteHost)
+		}
+	}
+
+	*modelName = newModel
+	*supportsTools = newSupportsTools
+	*supportsThinking = newSupportsThinking
+
+	if *supportsTools {
+		if *toolRegistry == nil {
+			*toolRegistry = tools.DefaultRegistry()
+		}
+		if (*toolRegistry).Count() > 0 {
+			fmt.Fprintf(os.Stderr, "\033[90mTools available: %s\033[0m\n", strings.Join((*toolRegistry).Names(), ", "))
+		}
+	} else {
+		*toolRegistry = nil
+		fmt.Fprintf(os.Stderr, "\033[33mNote: Model does not support tools - running in chat-only mode\033[0m\n")
+	}
+
+	return nil
+}
+
 // showToolsStatus displays the current tools and approval status.
 func showToolsStatus(registry *tools.Registry, approval *agent.ApprovalManager, supportsTools bool) {
 	if !supportsTools || registry == nil {

x/imagegen/.gitignore

@@ -1,38 +0,0 @@
-# Build directories
-build/
-dist/
-
-# CMake
-CMakeCache.txt
-CMakeFiles/
-cmake_install.cmake
-Makefile
-*.cmake
-
-# IDE
-.idea/
-.vscode/
-*.swp
-*.swo
-*~
-
-# macOS
-.DS_Store
-*.dSYM/
-
-# Go
-*.exe
-*.exe~
-*.dll
-*.so
-*.dylib
-
-# Python
-*.npy
-
-/engine
-weights
-outputs
-
-prompt.txt
-negative.txt

x/imagegen/README.md

@@ -1,236 +0,0 @@
-# Image Generation in Ollama (Experimental)
-
-Generate images from text prompts using local AI models.
-
-## Quick Start
-
-```bash
-# Run with a prompt
-ollama run z-image "a sunset over mountains"
-Generating: step 30/30
-Image saved to: /tmp/ollama-image-1704067200.png
-```
-
-On macOS, the generated image will automatically open in Preview.
-
-## Supported Models
-
-| Model | VRAM Required | Notes |
-|-------|---------------|-------|
-| z-image | ~12GB | Based on Flux architecture |
-
-## CLI Usage
-
-```bash
-# Generate an image
-ollama run z-image "a cat playing piano"
-
-# Check if model is running
-ollama ps
-
-# Stop the model
-ollama stop z-image
-```
-
-## API
-
-### OpenAI-Compatible Endpoint
-
-```bash
-POST /v1/images/generations
-```
-
-**Request:**
-```json
-{
-  "model": "z-image",
-  "prompt": "a sunset over mountains",
-  "size": "1024x1024",
-  "response_format": "b64_json"
-}
-```
-
-**Response:**
-```json
-{
-  "created": 1704067200,
-  "data": [
-    {
-      "b64_json": "iVBORw0KGgo..."
-    }
-  ]
-}
-```
-
-### Example: cURL
-
-```bash
-curl http://localhost:11434/v1/images/generations \
-  -H "Content-Type: application/json" \
-  -d '{
-    "model": "z-image",
-    "prompt": "a white cat",
-    "size": "1024x1024"
-  }'
-```
-
-### Example: Save to File
-
-```bash
-curl -s http://localhost:11434/v1/images/generations \
-  -H "Content-Type: application/json" \
-  -d '{
-    "model": "z-image",
-    "prompt": "a white cat",
-    "size": "1024x1024"
-  }' | jq -r '.data[0].b64_json' | base64 -d > image.png
-```
-
-### Streaming Progress
-
-Enable streaming to receive progress updates via SSE:
-
-```bash
-curl http://localhost:11434/v1/images/generations \
-  -H "Content-Type: application/json" \
-  -d '{"model": "z-image", "prompt": "a sunset", "stream": true}'
-```
-
-Events:
-```
-event: progress
-data: {"step": 1, "total": 30}
-
-event: progress
-data: {"step": 2, "total": 30}
-...
-
-event: done
-data: {"created": 1704067200, "data": [{"b64_json": "..."}]}
-```
-
-## Parameters
-
-| Parameter | Type | Default | Description |
-|-----------|------|---------|-------------|
-| model | string | required | Model name |
-| prompt | string | required | Text description of image |
-| size | string | "1024x1024" | Image dimensions (WxH) |
-| n | int | 1 | Number of images (currently only 1 supported) |
-| response_format | string | "b64_json" | "b64_json" or "url" |
-| stream | bool | false | Enable progress streaming |
-
-## Requirements
-
-- macOS with Apple Silicon (M1/M2/M3/M4)
-- CUDA: tested on CUDA 12 Blackwell, more testing coming soon
-- Sufficient VRAM (see model table above)
-- Ollama built with MLX support
-
-## Limitations
-
-- macOS only (uses MLX backend)
-- Single image per request
-- Fixed step count (30 steps)
-- Modelfiles not yet supported (use `ollama create` from model directory)
-
----
-
-# Tensor Model Storage Format
-
-Tensor models store each tensor as a separate blob with metadata in the manifest. This enables faster downloads (parallel fetching) and deduplication (shared tensors are stored once).
-
-## Manifest Structure
-
-The manifest follows the standard ollama format with tensor-specific layer metadata:
-
-```json
-{
-  "schemaVersion": 2,
-  "mediaType": "application/vnd.docker.distribution.manifest.v2+json",
-  "config": { "digest": "sha256:...", "size": 1234 },
-  "layers": [
-    {
-      "mediaType": "application/vnd.ollama.image.tensor",
-      "digest": "sha256:25b36eed...",
-      "size": 49807448,
-      "name": "text_encoder/model.layers.0.mlp.down_proj.weight",
-      "dtype": "BF16",
-      "shape": [2560, 9728]
-    },
-    {
-      "mediaType": "application/vnd.ollama.image.json",
-      "digest": "sha256:abc123...",
-      "size": 512,
-      "name": "text_encoder/config.json"
-    }
-  ]
-}
-```
-
-Each tensor layer includes:
-- `name`: Path-style tensor name (e.g., `text_encoder/model.layers.0.mlp.down_proj.weight`)
-- `dtype`: Data type (BF16, F32, etc.)
-- `shape`: Tensor dimensions
-
-Config layers use the same path-style naming (e.g., `tokenizer/tokenizer.json`).
-
-## Blob Format
-
-Each tensor blob is a minimal safetensors file:
-
-```
-[8 bytes: header size (uint64 LE)]
-[~80 bytes: JSON header, padded to 8-byte alignment]
-[N bytes: raw tensor data]
-```
-
-Header contains a single tensor named `"data"`:
-
-```json
-{"data":{"dtype":"BF16","shape":[2560,9728],"data_offsets":[0,49807360]}}
-```
-
-## Why Include the Header?
-
-The ~88 byte safetensors header enables MLX's native `mlx_load_safetensors` function, which:
-
-1. **Uses mmap** - Maps file directly into memory, no copies
-2. **Zero-copy to GPU** - MLX reads directly from mapped pages
-3. **No custom code** - Standard MLX API, battle-tested
-
-Without the header, we'd need custom C++ code to create MLX arrays from raw mmap'd data. MLX's public API doesn't expose this - it always copies when creating arrays from external pointers.
-
-The overhead is negligible: 88 bytes per tensor = ~100KB total for a 13GB model (0.0007%).
-
-## Why Per-Tensor Blobs?
-
-**Deduplication**: Blobs are content-addressed by SHA256. If two models share identical tensors (same weights, dtype, shape), they share the same blob file.
-
-Example: Model A and Model B both use the same text encoder. The text encoder's 400 tensors are stored once, referenced by both manifests.
-
-```
-~/.ollama/models/
-  blobs/
-    sha256-25b36eed...  <- shared by both models
-    sha256-abc123...
-  manifests/
-    library/model-a/latest  <- references sha256-25b36eed
-    library/model-b/latest  <- references sha256-25b36eed
-```
-
-## Import Flow
-
-```
-cd ./weights/Z-Image-Turbo
-ollama create z-image
-
-1. Scan component directories (text_encoder/, transformer/, vae/)
-2. For each .safetensors file:
-   - Extract individual tensors
-   - Wrap each in minimal safetensors format (88B header + data)
-   - Write to blob store (SHA256 content-addressed)
-   - Add layer entry to manifest with path-style name
-3. Copy config files (*.json) as config layers
-4. Write manifest
-```

x/imagegen/api/handler.go

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

x/imagegen/api/types.go

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

x/imagegen/cache/cache.go

@@ -1,156 +0,0 @@
-//go:build mlx
-
-package cache
-
-import "github.com/ollama/ollama/x/imagegen/mlx"
-
-type Cache interface {
-	Update(k, v *mlx.Array, seqLen int) (*mlx.Array, *mlx.Array)
-	Offset() int
-	Len() int
-	State() []*mlx.Array
-}
-
-type KVCache struct {
-	keys, values *mlx.Array
-	offset       int
-	step         int
-}
-
-func NewKVCache() *KVCache {
-	return &KVCache{step: 256}
-}
-
-func (c *KVCache) Update(k, v *mlx.Array, seqLen int) (*mlx.Array, *mlx.Array) {
-	prev := c.offset
-	shape := k.Shape()
-	B, H, Dk := shape[0], shape[1], shape[3]
-	Dv := v.Shape()[3]
-
-	// Grow buffer if needed
-	if c.keys == nil || (prev+seqLen) > int(c.keys.Shape()[2]) {
-		nSteps := (c.step + seqLen - 1) / c.step
-		newK := mlx.Zeros([]int32{B, H, int32(nSteps * c.step), Dk}, k.Dtype())
-		newV := mlx.Zeros([]int32{B, H, int32(nSteps * c.step), Dv}, v.Dtype())
-
-		if c.keys != nil {
-			if prev%c.step != 0 {
-				c.keys = mlx.Slice(c.keys, []int32{0, 0, 0, 0}, []int32{B, H, int32(prev), Dk})
-				c.values = mlx.Slice(c.values, []int32{0, 0, 0, 0}, []int32{B, H, int32(prev), Dv})
-			}
-			c.keys = mlx.Concatenate([]*mlx.Array{c.keys, newK}, 2)
-			c.values = mlx.Concatenate([]*mlx.Array{c.values, newV}, 2)
-		} else {
-			c.keys, c.values = newK, newV
-		}
-	}
-
-	c.offset += seqLen
-	c.keys = mlx.SliceUpdateInplace(c.keys, k, []int32{0, 0, int32(prev), 0}, []int32{B, H, int32(c.offset), Dk})
-	c.values = mlx.SliceUpdateInplace(c.values, v, []int32{0, 0, int32(prev), 0}, []int32{B, H, int32(c.offset), Dv})
-
-	return mlx.Slice(c.keys, []int32{0, 0, 0, 0}, []int32{B, H, int32(c.offset), Dk}),
-		mlx.Slice(c.values, []int32{0, 0, 0, 0}, []int32{B, H, int32(c.offset), Dv})
-}
-
-func (c *KVCache) State() []*mlx.Array {
-	if c.keys == nil {
-		return nil
-	}
-	return []*mlx.Array{c.keys, c.values}
-}
-
-func (c *KVCache) Offset() int { return c.offset }
-func (c *KVCache) Len() int    { return c.offset }
-
-// RotatingKVCache implements sliding window attention with bounded memory
-type RotatingKVCache struct {
-	keys, values *mlx.Array
-	offset       int
-	maxSize      int
-	step         int
-	idx          int
-}
-
-func NewRotatingKVCache(maxSize int) *RotatingKVCache {
-	return &RotatingKVCache{maxSize: maxSize, step: 256}
-}
-
-func (c *RotatingKVCache) Update(k, v *mlx.Array, seqLen int) (*mlx.Array, *mlx.Array) {
-	if seqLen > 1 {
-		return c.updateConcat(k, v, seqLen)
-	}
-	return c.updateInPlace(k, v)
-}
-
-func (c *RotatingKVCache) updateInPlace(k, v *mlx.Array) (*mlx.Array, *mlx.Array) {
-	shape := k.Shape()
-	B, H, Dk := shape[0], shape[1], shape[3]
-	Dv := v.Shape()[3]
-
-	// Grow buffer if not yet at max
-	if c.keys == nil || (c.idx >= int(c.keys.Shape()[2]) && int(c.keys.Shape()[2]) < c.maxSize) {
-		var cap int
-		if c.keys != nil {
-			cap = int(c.keys.Shape()[2])
-		}
-		newSize := min(c.step, c.maxSize-cap)
-		newK := mlx.Zeros([]int32{B, H, int32(newSize), Dk}, k.Dtype())
-		newV := mlx.Zeros([]int32{B, H, int32(newSize), Dv}, v.Dtype())
-		if c.keys != nil {
-			c.keys = mlx.Concatenate([]*mlx.Array{c.keys, newK}, 2)
-			c.values = mlx.Concatenate([]*mlx.Array{c.values, newV}, 2)
-		} else {
-			c.keys, c.values = newK, newV
-		}
-	}
-
-	// Rotate when hitting max
-	if c.idx >= c.maxSize {
-		c.idx = 0
-	}
-
-	c.keys = mlx.SliceUpdateInplace(c.keys, k, []int32{0, 0, int32(c.idx), 0}, []int32{B, H, int32(c.idx + 1), Dk})
-	c.values = mlx.SliceUpdateInplace(c.values, v, []int32{0, 0, int32(c.idx), 0}, []int32{B, H, int32(c.idx + 1), Dv})
-
-	c.offset++
-	c.idx++
-
-	validLen := int32(min(c.offset, c.maxSize))
-	return mlx.Slice(c.keys, []int32{0, 0, 0, 0}, []int32{B, H, validLen, Dk}),
-		mlx.Slice(c.values, []int32{0, 0, 0, 0}, []int32{B, H, validLen, Dv})
-}
-
-func (c *RotatingKVCache) updateConcat(k, v *mlx.Array, seqLen int) (*mlx.Array, *mlx.Array) {
-	shape := k.Shape()
-	B, H, Dk := shape[0], shape[1], shape[3]
-	Dv := v.Shape()[3]
-
-	if c.keys == nil {
-		c.keys, c.values = k, v
-	} else {
-		c.keys = mlx.Concatenate([]*mlx.Array{c.keys, k}, 2)
-		c.values = mlx.Concatenate([]*mlx.Array{c.values, v}, 2)
-	}
-	c.offset += seqLen
-
-	// Trim to max_size to maintain sliding window
-	cap := int(c.keys.Shape()[2])
-	if trim := cap - c.maxSize; trim > 0 {
-		c.keys = mlx.Slice(c.keys, []int32{0, 0, int32(trim), 0}, []int32{B, H, int32(cap), Dk})
-		c.values = mlx.Slice(c.values, []int32{0, 0, int32(trim), 0}, []int32{B, H, int32(cap), Dv})
-	}
-
-	c.idx = int(c.keys.Shape()[2])
-	return c.keys, c.values
-}
-
-func (c *RotatingKVCache) State() []*mlx.Array {
-	if c.keys == nil {
-		return nil
-	}
-	return []*mlx.Array{c.keys, c.values}
-}
-
-func (c *RotatingKVCache) Offset() int { return c.offset }
-func (c *RotatingKVCache) Len() int    { return min(c.offset, c.maxSize) }

x/imagegen/cache/step.go

@@ -1,164 +0,0 @@
-//go:build mlx
-
-package cache
-
-import "github.com/ollama/ollama/x/imagegen/mlx"
-
-// StepCache caches layer outputs across diffusion denoising steps.
-// Based on DeepCache (CVPR 2024) and Learning-to-Cache (NeurIPS 2024):
-// shallow layers change little between consecutive steps, so we can
-// cache their outputs and skip recomputation on non-refresh steps.
-//
-// Supports both single-stream (Z-Image) and dual-stream (Qwen-Image) architectures:
-//   - Single-stream: use Get/Set for the single output per layer
-//   - Dual-stream: use Get/Set for stream 1 (imgH), Get2/Set2 for stream 2 (txtH)
-//
-// Usage (single-stream):
-//
-//	cache := NewStepCache(15)  // cache first 15 layers
-//	for step := 0; step < numSteps; step++ {
-//	    refresh := cache.ShouldRefresh(step, 3)  // refresh every 3 steps
-//	    for i, layer := range layers {
-//	        if i < 15 && !refresh && cache.Get(i) != nil {
-//	            output = cache.Get(i)  // reuse cached
-//	        } else {
-//	            output = layer.Forward(input)
-//	            if i < 15 && refresh {
-//	                cache.Set(i, output)
-//	            }
-//	        }
-//	    }
-//	}
-//	cache.Free()  // cleanup when done
-//
-// Usage (dual-stream):
-//
-//	cache := NewStepCache(15)
-//	for step := 0; step < numSteps; step++ {
-//	    refresh := cache.ShouldRefresh(step, 3)
-//	    for i, layer := range layers {
-//	        if i < 15 && !refresh && cache.Get(i) != nil {
-//	            imgH, txtH = cache.Get(i), cache.Get2(i)
-//	        } else {
-//	            imgH, txtH = layer.Forward(imgH, txtH, ...)
-//	            if i < 15 && refresh {
-//	                cache.Set(i, imgH)
-//	                cache.Set2(i, txtH)
-//	            }
-//	        }
-//	    }
-//	}
-type StepCache struct {
-	layers   []*mlx.Array // cached layer outputs (stream 1)
-	layers2  []*mlx.Array // cached layer outputs (stream 2, for dual-stream models)
-	constant *mlx.Array   // optional constant (e.g., text embeddings)
-}
-
-// NewStepCache creates a cache for the given number of layers.
-func NewStepCache(numLayers int) *StepCache {
-	return &StepCache{
-		layers:  make([]*mlx.Array, numLayers),
-		layers2: make([]*mlx.Array, numLayers),
-	}
-}
-
-// ShouldRefresh returns true if the cache should be refreshed at this step.
-// Refresh happens on step 0, interval, 2*interval, etc.
-func (c *StepCache) ShouldRefresh(step, interval int) bool {
-	return step%interval == 0
-}
-
-// Get returns the cached output for a layer, or nil if not cached.
-func (c *StepCache) Get(layer int) *mlx.Array {
-	if layer < len(c.layers) {
-		return c.layers[layer]
-	}
-	return nil
-}
-
-// Set stores a layer output (stream 1), freeing any previous value.
-func (c *StepCache) Set(layer int, arr *mlx.Array) {
-	if layer < len(c.layers) {
-		if c.layers[layer] != nil {
-			c.layers[layer].Free()
-		}
-		c.layers[layer] = arr
-	}
-}
-
-// Get2 returns the cached output for a layer (stream 2), or nil if not cached.
-// Used for dual-stream architectures like Qwen-Image.
-func (c *StepCache) Get2(layer int) *mlx.Array {
-	if layer < len(c.layers2) {
-		return c.layers2[layer]
-	}
-	return nil
-}
-
-// Set2 stores a layer output (stream 2), freeing any previous value.
-// Used for dual-stream architectures like Qwen-Image.
-func (c *StepCache) Set2(layer int, arr *mlx.Array) {
-	if layer < len(c.layers2) {
-		if c.layers2[layer] != nil {
-			c.layers2[layer].Free()
-		}
-		c.layers2[layer] = arr
-	}
-}
-
-// GetConstant returns the cached constant value.
-func (c *StepCache) GetConstant() *mlx.Array {
-	return c.constant
-}
-
-// SetConstant stores a constant value, freeing any previous value.
-func (c *StepCache) SetConstant(arr *mlx.Array) {
-	if c.constant != nil {
-		c.constant.Free()
-	}
-	c.constant = arr
-}
-
-// Arrays returns all non-nil cached arrays (for pool.Keep).
-func (c *StepCache) Arrays() []*mlx.Array {
-	var result []*mlx.Array
-	if c.constant != nil {
-		result = append(result, c.constant)
-	}
-	for _, arr := range c.layers {
-		if arr != nil {
-			result = append(result, arr)
-		}
-	}
-	for _, arr := range c.layers2 {
-		if arr != nil {
-			result = append(result, arr)
-		}
-	}
-	return result
-}
-
-// Free releases all cached arrays. Call when generation completes.
-func (c *StepCache) Free() {
-	if c.constant != nil {
-		c.constant.Free()
-		c.constant = nil
-	}
-	for i, arr := range c.layers {
-		if arr != nil {
-			arr.Free()
-			c.layers[i] = nil
-		}
-	}
-	for i, arr := range c.layers2 {
-		if arr != nil {
-			arr.Free()
-			c.layers2[i] = nil
-		}
-	}
-}
-
-// NumLayers returns the number of layers this cache can store.
-func (c *StepCache) NumLayers() int {
-	return len(c.layers)
-}

x/imagegen/cli.go

@@ -1,539 +0,0 @@
-// cli.go provides CLI commands for image generation models.
-//
-// TODO (jmorganca): Integrate these commands into cmd/cmd.go when stable.
-// Currently these are separate to keep experimental code isolated.
-
-package imagegen
-
-import (
-	"encoding/base64"
-	"encoding/json"
-	"errors"
-	"fmt"
-	"io"
-	"os"
-	"strconv"
-	"strings"
-	"time"
-
-	"github.com/spf13/cobra"
-
-	"github.com/ollama/ollama/api"
-	"github.com/ollama/ollama/envconfig"
-	"github.com/ollama/ollama/progress"
-	"github.com/ollama/ollama/readline"
-)
-
-// ImageGenOptions holds options for image generation.
-// These can be set via environment variables or interactive commands.
-type ImageGenOptions struct {
-	Width          int
-	Height         int
-	Steps          int
-	Seed           int
-	NegativePrompt string
-}
-
-// DefaultOptions returns the default image generation options.
-func DefaultOptions() ImageGenOptions {
-	return ImageGenOptions{
-		Width:  1024,
-		Height: 1024,
-		Steps:  9,
-		Seed:   0, // 0 means random
-	}
-}
-
-// Show displays information about an image generation model.
-func Show(modelName string, w io.Writer) error {
-	manifest, err := LoadManifest(modelName)
-	if err != nil {
-		return fmt.Errorf("failed to load manifest: %w", err)
-	}
-
-	// Count total size
-	var totalSize int64
-	for _, layer := range manifest.Manifest.Layers {
-		if layer.MediaType == "application/vnd.ollama.image.tensor" {
-			totalSize += layer.Size
-		}
-	}
-
-	// Read model_index.json for architecture
-	var architecture string
-	if data, err := manifest.ReadConfig("model_index.json"); err == nil {
-		var index struct {
-			Architecture string `json:"architecture"`
-		}
-		if json.Unmarshal(data, &index) == nil {
-			architecture = index.Architecture
-		}
-	}
-
-	// Estimate parameter count from total size (assuming BF16 = 2 bytes per param)
-	paramCount := totalSize / 2
-	paramStr := formatParamCount(paramCount)
-
-	// Print Model info
-	fmt.Fprintln(w, "  Model")
-	if architecture != "" {
-		fmt.Fprintf(w, "    %-20s %s\n", "architecture", architecture)
-	}
-	fmt.Fprintf(w, "    %-20s %s\n", "parameters", paramStr)
-	fmt.Fprintf(w, "    %-20s %s\n", "quantization", "BF16")
-	fmt.Fprintln(w)
-
-	// Print Capabilities
-	fmt.Fprintln(w, "  Capabilities")
-	fmt.Fprintf(w, "    %s\n", "image")
-	fmt.Fprintln(w)
-
-	return nil
-}
-
-// formatParamCount formats parameter count as human-readable string.
-func formatParamCount(count int64) string {
-	if count >= 1_000_000_000 {
-		return fmt.Sprintf("%.1fB", float64(count)/1_000_000_000)
-	}
-	if count >= 1_000_000 {
-		return fmt.Sprintf("%.1fM", float64(count)/1_000_000)
-	}
-	return fmt.Sprintf("%d", count)
-}
-
-// RegisterFlags adds image generation flags to the given command.
-// Flags are hidden since they only apply to image generation models.
-func RegisterFlags(cmd *cobra.Command) {
-	cmd.Flags().Int("width", 1024, "Image width")
-	cmd.Flags().Int("height", 1024, "Image height")
-	cmd.Flags().Int("steps", 9, "Denoising steps")
-	cmd.Flags().Int("seed", 0, "Random seed (0 for random)")
-	cmd.Flags().String("negative", "", "Negative prompt")
-	cmd.Flags().MarkHidden("width")
-	cmd.Flags().MarkHidden("height")
-	cmd.Flags().MarkHidden("steps")
-	cmd.Flags().MarkHidden("seed")
-	cmd.Flags().MarkHidden("negative")
-}
-
-// RunCLI handles the CLI for image generation models.
-// Returns true if it handled the request, false if the caller should continue with normal flow.
-// Supports flags: --width, --height, --steps, --seed, --negative
-func RunCLI(cmd *cobra.Command, name string, prompt string, interactive bool, keepAlive *api.Duration) error {
-	// Verify it's a valid image gen model
-	if ResolveModelName(name) == "" {
-		return fmt.Errorf("unknown image generation model: %s", name)
-	}
-
-	// Get options from flags (with env var defaults)
-	opts := DefaultOptions()
-	if cmd != nil && cmd.Flags() != nil {
-		if v, err := cmd.Flags().GetInt("width"); err == nil && v > 0 {
-			opts.Width = v
-		}
-		if v, err := cmd.Flags().GetInt("height"); err == nil && v > 0 {
-			opts.Height = v
-		}
-		if v, err := cmd.Flags().GetInt("steps"); err == nil && v > 0 {
-			opts.Steps = v
-		}
-		if v, err := cmd.Flags().GetInt("seed"); err == nil && v != 0 {
-			opts.Seed = v
-		}
-		if v, err := cmd.Flags().GetString("negative"); err == nil && v != "" {
-			opts.NegativePrompt = v
-		}
-	}
-
-	if interactive {
-		return runInteractive(cmd, name, keepAlive, opts)
-	}
-
-	// One-shot generation
-	return generateImageWithOptions(cmd, name, prompt, keepAlive, opts)
-}
-
-// generateImageWithOptions generates an image with the given options.
-func generateImageWithOptions(cmd *cobra.Command, modelName, prompt string, keepAlive *api.Duration, opts ImageGenOptions) error {
-	client, err := api.ClientFromEnvironment()
-	if err != nil {
-		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,
-		},
-	}
-	if keepAlive != nil {
-		req.KeepAlive = keepAlive
-	}
-
-	// Show loading spinner until generation starts
-	p := progress.NewProgress(os.Stderr)
-	spinner := progress.NewSpinner("")
-	p.Add("", spinner)
-
-	var stepBar *progress.StepBar
-	var imagePath 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 {
-				spinner.Stop()
-				stepBar = progress.NewStepBar("Generating", total)
-				p.Add("", stepBar)
-			}
-			if stepBar != nil {
-				stepBar.Set(step)
-			}
-			return nil
-		}
-
-		// Handle final response with image path
-		if resp.Done && strings.Contains(content, "Image saved to:") {
-			if idx := strings.Index(content, "Image saved to: "); idx >= 0 {
-				imagePath = strings.TrimSpace(content[idx+16:])
-			}
-		}
-
-		return nil
-	})
-
-	p.Stop()
-	if err != nil {
-		return err
-	}
-
-	if imagePath != "" {
-		displayImageInTerminal(imagePath)
-		fmt.Printf("Image saved to: %s\n", imagePath)
-	}
-
-	return nil
-}
-
-// runInteractive runs an interactive REPL for image generation.
-func runInteractive(cmd *cobra.Command, modelName string, keepAlive *api.Duration, opts ImageGenOptions) error {
-	client, err := api.ClientFromEnvironment()
-	if err != nil {
-		return err
-	}
-
-	scanner, err := readline.New(readline.Prompt{
-		Prompt:      ">>> ",
-		Placeholder: "Describe an image to generate (/help for commands)",
-	})
-	if err != nil {
-		return err
-	}
-
-	if envconfig.NoHistory() {
-		scanner.HistoryDisable()
-	}
-
-	for {
-		line, err := scanner.Readline()
-		switch {
-		case errors.Is(err, io.EOF):
-			fmt.Println()
-			return nil
-		case errors.Is(err, readline.ErrInterrupt):
-			if line == "" {
-				fmt.Println("\nUse Ctrl + d or /bye to exit.")
-			}
-			continue
-		case err != nil:
-			return err
-		}
-
-		line = strings.TrimSpace(line)
-		if line == "" {
-			continue
-		}
-
-		// Handle commands
-		switch {
-		case strings.HasPrefix(line, "/bye"):
-			return nil
-		case strings.HasPrefix(line, "/?"), strings.HasPrefix(line, "/help"):
-			printInteractiveHelp(opts)
-			continue
-		case strings.HasPrefix(line, "/set "):
-			if err := handleSetCommand(line[5:], &opts); err != nil {
-				fmt.Fprintf(os.Stderr, "Error: %v\n", err)
-			}
-			continue
-		case strings.HasPrefix(line, "/show"):
-			printCurrentSettings(opts)
-			continue
-		case strings.HasPrefix(line, "/"):
-			fmt.Fprintf(os.Stderr, "Unknown command: %s (try /help)\n", line)
-			continue
-		}
-
-		// Generate image with current options
-		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,
-			},
-		}
-		if keepAlive != nil {
-			req.KeepAlive = keepAlive
-		}
-
-		// Show loading spinner until generation starts
-		p := progress.NewProgress(os.Stderr)
-		spinner := progress.NewSpinner("")
-		p.Add("", spinner)
-
-		var stepBar *progress.StepBar
-		var imagePath 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 {
-					spinner.Stop()
-					stepBar = progress.NewStepBar("Generating", total)
-					p.Add("", stepBar)
-				}
-				if stepBar != nil {
-					stepBar.Set(step)
-				}
-				return nil
-			}
-
-			// Handle final response with image path
-			if resp.Done && strings.Contains(content, "Image saved to:") {
-				if idx := strings.Index(content, "Image saved to: "); idx >= 0 {
-					imagePath = strings.TrimSpace(content[idx+16:])
-				}
-			}
-
-			return nil
-		})
-
-		p.Stop()
-		if err != nil {
-			fmt.Fprintf(os.Stderr, "Error: %v\n", err)
-			continue
-		}
-
-		// Copy image to current directory with descriptive name
-		if imagePath != "" {
-			// Create filename from prompt (sanitized)
-			safeName := sanitizeFilename(line)
-			if len(safeName) > 50 {
-				safeName = safeName[:50]
-			}
-			timestamp := time.Now().Format("20060102-150405")
-			newName := fmt.Sprintf("%s-%s.png", safeName, timestamp)
-
-			// Copy file to CWD
-			if err := copyFile(imagePath, newName); err != nil {
-				fmt.Fprintf(os.Stderr, "Error saving to current directory: %v\n", err)
-				displayImageInTerminal(imagePath)
-				fmt.Printf("Image saved to: %s\n", imagePath)
-			} else {
-				displayImageInTerminal(newName)
-				fmt.Printf("Image saved to: %s\n", newName)
-			}
-		}
-
-		fmt.Println()
-	}
-}
-
-// sanitizeFilename removes characters that aren't safe for filenames.
-func sanitizeFilename(s string) string {
-	s = strings.ToLower(s)
-	s = strings.ReplaceAll(s, " ", "-")
-	// Remove any character that's not alphanumeric or hyphen
-	var result strings.Builder
-	for _, r := range s {
-		if (r >= 'a' && r <= 'z') || (r >= '0' && r <= '9') || r == '-' {
-			result.WriteRune(r)
-		}
-	}
-	return result.String()
-}
-
-// copyFile copies a file from src to dst.
-func copyFile(src, dst string) error {
-	sourceFile, err := os.Open(src)
-	if err != nil {
-		return err
-	}
-	defer sourceFile.Close()
-
-	destFile, err := os.Create(dst)
-	if err != nil {
-		return err
-	}
-	defer destFile.Close()
-
-	_, err = io.Copy(destFile, sourceFile)
-	return err
-}
-
-// printInteractiveHelp prints help for interactive mode commands.
-func printInteractiveHelp(opts ImageGenOptions) {
-	fmt.Fprintln(os.Stderr, "Commands:")
-	fmt.Fprintln(os.Stderr, "  /set width <n>     Set image width (current:", opts.Width, ")")
-	fmt.Fprintln(os.Stderr, "  /set height <n>    Set image height (current:", opts.Height, ")")
-	fmt.Fprintln(os.Stderr, "  /set steps <n>     Set denoising steps (current:", opts.Steps, ")")
-	fmt.Fprintln(os.Stderr, "  /set seed <n>      Set random seed (current:", opts.Seed, ", 0=random)")
-	fmt.Fprintln(os.Stderr, "  /set negative <s>  Set negative prompt")
-	fmt.Fprintln(os.Stderr, "  /show              Show current settings")
-	fmt.Fprintln(os.Stderr, "  /bye               Exit")
-	fmt.Fprintln(os.Stderr)
-	fmt.Fprintln(os.Stderr, "Or type a prompt to generate an image.")
-	fmt.Fprintln(os.Stderr)
-}
-
-// printCurrentSettings prints the current image generation settings.
-func printCurrentSettings(opts ImageGenOptions) {
-	fmt.Fprintf(os.Stderr, "Current settings:\n")
-	fmt.Fprintf(os.Stderr, "  width:    %d\n", opts.Width)
-	fmt.Fprintf(os.Stderr, "  height:   %d\n", opts.Height)
-	fmt.Fprintf(os.Stderr, "  steps:    %d\n", opts.Steps)
-	fmt.Fprintf(os.Stderr, "  seed:     %d (0=random)\n", opts.Seed)
-	if opts.NegativePrompt != "" {
-		fmt.Fprintf(os.Stderr, "  negative: %s\n", opts.NegativePrompt)
-	}
-	fmt.Fprintln(os.Stderr)
-}
-
-// handleSetCommand handles /set commands to change options.
-func handleSetCommand(args string, opts *ImageGenOptions) error {
-	parts := strings.SplitN(args, " ", 2)
-	if len(parts) < 2 {
-		return fmt.Errorf("usage: /set <option> <value>")
-	}
-
-	key := strings.ToLower(parts[0])
-	value := strings.TrimSpace(parts[1])
-
-	switch key {
-	case "width", "w":
-		v, err := strconv.Atoi(value)
-		if err != nil || v <= 0 {
-			return fmt.Errorf("width must be a positive integer")
-		}
-		opts.Width = v
-		fmt.Fprintf(os.Stderr, "Set width to %d\n", v)
-	case "height", "h":
-		v, err := strconv.Atoi(value)
-		if err != nil || v <= 0 {
-			return fmt.Errorf("height must be a positive integer")
-		}
-		opts.Height = v
-		fmt.Fprintf(os.Stderr, "Set height to %d\n", v)
-	case "steps", "s":
-		v, err := strconv.Atoi(value)
-		if err != nil || v <= 0 {
-			return fmt.Errorf("steps must be a positive integer")
-		}
-		opts.Steps = v
-		fmt.Fprintf(os.Stderr, "Set steps to %d\n", v)
-	case "seed":
-		v, err := strconv.Atoi(value)
-		if err != nil {
-			return fmt.Errorf("seed must be an integer")
-		}
-		opts.Seed = v
-		fmt.Fprintf(os.Stderr, "Set seed to %d\n", v)
-	case "negative", "neg", "n":
-		opts.NegativePrompt = value
-		if value == "" {
-			fmt.Fprintln(os.Stderr, "Cleared negative prompt")
-		} else {
-			fmt.Fprintf(os.Stderr, "Set negative prompt to: %s\n", value)
-		}
-	default:
-		return fmt.Errorf("unknown option: %s (try /help)", key)
-	}
-	return nil
-}
-
-// displayImageInTerminal attempts to render an image inline in the terminal.
-// Supports iTerm2, Kitty, WezTerm, Ghostty, and other terminals with inline image support.
-// Returns true if the image was displayed, false otherwise.
-func displayImageInTerminal(imagePath string) bool {
-	// Check if terminal supports inline images
-	termProgram := os.Getenv("TERM_PROGRAM")
-	kittyWindowID := os.Getenv("KITTY_WINDOW_ID")
-	weztermPane := os.Getenv("WEZTERM_PANE")
-	ghostty := os.Getenv("GHOSTTY_RESOURCES_DIR")
-
-	// Read the image file
-	data, err := os.ReadFile(imagePath)
-	if err != nil {
-		return false
-	}
-
-	encoded := base64.StdEncoding.EncodeToString(data)
-
-	switch {
-	case termProgram == "iTerm.app" || termProgram == "WezTerm" || weztermPane != "":
-		// iTerm2/WezTerm inline image protocol
-		// ESC ] 1337 ; File = [arguments] : base64 BEL
-		fmt.Printf("\033]1337;File=inline=1;preserveAspectRatio=1:%s\a\n", encoded)
-		return true
-
-	case kittyWindowID != "" || ghostty != "" || termProgram == "ghostty":
-		// Kitty graphics protocol (also used by Ghostty)
-		// Send in chunks for large images
-		const chunkSize = 4096
-		for i := 0; i < len(encoded); i += chunkSize {
-			end := i + chunkSize
-			if end > len(encoded) {
-				end = len(encoded)
-			}
-			chunk := encoded[i:end]
-
-			if i == 0 {
-				// First chunk: a=T (transmit), f=100 (PNG), m=1 (more chunks follow) or m=0 (last chunk)
-				more := 1
-				if end >= len(encoded) {
-					more = 0
-				}
-				fmt.Printf("\033_Ga=T,f=100,m=%d;%s\033\\", more, chunk)
-			} else if end >= len(encoded) {
-				// Last chunk
-				fmt.Printf("\033_Gm=0;%s\033\\", chunk)
-			} else {
-				// Middle chunk
-				fmt.Printf("\033_Gm=1;%s\033\\", chunk)
-			}
-		}
-		fmt.Println()
-		return true
-
-	default:
-		return false
-	}
-}

x/imagegen/client/create.go

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

x/imagegen/cmd/engine/README.md

@@ -1,35 +0,0 @@
-# MLX Engine
-
-Experimental MLX backend for running models on Apple Silicon and CUDA.
-
-## Build
-
-```bash
-go build -tags mlx -o engine ./x/imagegen/cmd/engine
-```
-
-## Text Generation
-
-```bash
-./engine -model /path/to/model -prompt "Hello" -max-tokens 100
-```
-
-Options:
-
-- `-temperature` - sampling temperature (default 0.7)
-- `-top-p` - nucleus sampling (default 0.9)
-- `-top-k` - top-k sampling (default 40)
-
-Supports: Llama, Gemma3, GPT-OSS
-
-## Image Generation
-
-```bash
-./engine -zimage -model /path/to/z-image -prompt "a cat" -output cat.png
-```
-
-Options:
-
-- `-width`, `-height` - image dimensions (default 1024x1024)
-- `-steps` - denoising steps (default 9)
-- `-seed` - random seed (default 42)

x/imagegen/cmd/engine/generate.go

@@ -1,359 +0,0 @@
-//go:build mlx
-
-package main
-
-import (
-	"context"
-	"fmt"
-	"time"
-	"unicode/utf8"
-
-	"github.com/ollama/ollama/x/imagegen/cache"
-	"github.com/ollama/ollama/x/imagegen/mlx"
-	"github.com/ollama/ollama/x/imagegen/tokenizer"
-)
-
-// Dedicated stream for generation (like mlx-lm's generation_stream)
-var generationStream *mlx.Stream
-
-// utf8Streamer buffers decoded text and emits only complete UTF-8 characters.
-// This handles cases where tokenizers output partial multi-byte sequences.
-type utf8Streamer struct {
-	buffer []byte
-}
-
-// Write adds decoded text to the buffer and returns complete UTF-8 characters.
-func (s *utf8Streamer) Write(text string) string {
-	s.buffer = append(s.buffer, text...)
-
-	// Find the last position that ends with a complete UTF-8 character
-	validLen := 0
-	for i := 0; i < len(s.buffer); {
-		r, size := utf8.DecodeRune(s.buffer[i:])
-		if r == utf8.RuneError && size == 1 {
-			// Invalid or incomplete UTF-8 sequence at this position
-			// Check if it could be a valid start of a multi-byte sequence
-			if len(s.buffer)-i < 4 {
-				// Might be incomplete, keep it in buffer
-				break
-			}
-			// Definitely invalid, skip this byte
-			i++
-			validLen = i
-		} else {
-			i += size
-			validLen = i
-		}
-	}
-
-	if validLen == 0 {
-		return ""
-	}
-
-	result := string(s.buffer[:validLen])
-	s.buffer = s.buffer[validLen:]
-	return result
-}
-
-// Flush returns any remaining buffered bytes (may be incomplete UTF-8).
-func (s *utf8Streamer) Flush() string {
-	if len(s.buffer) == 0 {
-		return ""
-	}
-	result := string(s.buffer)
-	s.buffer = nil
-	return result
-}
-
-func init() {
-	generationStream = mlx.NewStream()
-}
-
-// withStream runs fn with the generation stream as default
-func withStream(fn func()) {
-	orig := mlx.GetDefaultStream()
-	mlx.SetDefaultStream(generationStream)
-	fn()
-	mlx.SetDefaultStream(orig)
-}
-
-type Model interface {
-	Tokenizer() *tokenizer.Tokenizer
-	VocabSize() int32
-	NewCache(maxSeqLen int32) []cache.Cache
-	Forward(input *mlx.Array, caches []cache.Cache) *mlx.Array
-}
-
-// ChatModel is an optional interface for models that support chat formatting
-type ChatModel interface {
-	FormatPrompt(prompt string) string
-}
-
-// MultimodalModel is for models that support image input
-type MultimodalModel interface {
-	Model
-	FormatPromptWithImage(prompt string) string
-	ExpandImageTokens(tokens []int32) []int32
-	ForwardWithImage(tokens *mlx.Array, image *mlx.Array, caches []cache.Cache) *mlx.Array
-	ImageSize() int32 // Returns expected image size for preprocessing
-}
-
-// ImageLoader loads and preprocesses an image for multimodal models
-// Returns nil if path is empty
-type ImageLoader func(path string, imageSize int32) (*mlx.Array, error)
-
-type input struct {
-	Prompt       string
-	Image        *mlx.Array // Optional preprocessed image for multimodal models
-	MaxTokens    int
-	Temperature  float32
-	TopP         float32
-	TopK         int
-	WiredLimitGB int // Metal wired memory limit in GB (default 32)
-}
-
-type output struct {
-	Text          string
-	Done          bool
-	PrefillTokSec float64
-	GenTokSec     float64
-}
-
-// Decoder wraps model + cache for autoregressive generation.
-type Decoder struct {
-	model         Model
-	caches        []cache.Cache
-	vocabSize     int32
-	temp          float32
-	topK          int
-	topP          float32
-	token         *mlx.Array   // Current token (kept across pools)
-	oldCacheState []*mlx.Array // Preallocated slice for old cache state
-	image         *mlx.Array   // Optional image for multimodal prefill
-}
-
-func NewDecoder(m Model, temp float32, topK int, topP float32) *Decoder {
-	caches := m.NewCache(0)
-	return &Decoder{
-		model:         m,
-		caches:        caches,
-		vocabSize:     m.VocabSize(),
-		temp:          temp,
-		topK:          topK,
-		topP:          topP,
-		oldCacheState: make([]*mlx.Array, 0, len(caches)*2),
-	}
-}
-
-// SetImage sets the image for multimodal prefill (call before prefill)
-func (d *Decoder) SetImage(img *mlx.Array) {
-	d.image = img
-}
-
-func (d *Decoder) prefill(inputIDs []int32) int {
-	processed := 0
-
-	// Track old cache state to free after each chunk
-	var oldCacheState []*mlx.Array
-
-	// For multimodal models with an image, we need to process all tokens together
-	// in the first forward pass so the image embeddings can be inserted properly.
-	// Skip chunking for multimodal prefill.
-	isMultimodal := d.image != nil
-
-	// Process all-but-1 tokens in chunks, eval cache state for memory management
-	// Skip chunking for multimodal - process everything in the final step
-	if !isMultimodal {
-		for len(inputIDs) > 1 {
-			chunkSize := min(2048, len(inputIDs)-1)
-			if chunkSize <= 0 {
-				break
-			}
-			chunk := inputIDs[:chunkSize]
-
-			// Save old cache state before forward
-			oldCacheState = oldCacheState[:0]
-			for _, c := range d.caches {
-				oldCacheState = append(oldCacheState, c.State()...)
-			}
-
-			var cacheState []*mlx.Array
-			withStream(func() {
-				x := mlx.NewArrayInt32(chunk, []int32{1, int32(len(chunk))})
-				d.model.Forward(x, d.caches)
-				for _, c := range d.caches {
-					cacheState = append(cacheState, c.State()...)
-				}
-			})
-			mlx.Eval(cacheState...)
-
-			// Free old cache state
-			for _, arr := range oldCacheState {
-				if arr != nil {
-					arr.Free()
-				}
-			}
-
-			inputIDs = inputIDs[chunkSize:]
-			processed += chunkSize
-		}
-	}
-
-	// Save old cache state before final step
-	oldCacheState = oldCacheState[:0]
-	for _, c := range d.caches {
-		oldCacheState = append(oldCacheState, c.State()...)
-	}
-
-	// Final token + sampling (or all tokens for multimodal)
-	withStream(func() {
-		x := mlx.NewArrayInt32(inputIDs, []int32{1, int32(len(inputIDs))})
-		mlx.Eval(x) // Materialize before any other evals
-
-		var logits *mlx.Array
-		// Use ForwardWithImage if we have an image and model supports it
-		if d.image != nil {
-			if mm, ok := d.model.(MultimodalModel); ok {
-				logits = mm.ForwardWithImage(x, d.image, d.caches)
-				d.image = nil // Only use image for first forward
-			} else {
-				logits = d.model.Forward(x, d.caches)
-			}
-		} else {
-			logits = d.model.Forward(x, d.caches)
-		}
-		d.token = sample(logits, d.temp, d.topK, d.topP, d.vocabSize)
-	})
-	// Keep cache state (token auto-kept by AsyncEval)
-	for _, c := range d.caches {
-		mlx.Keep(c.State()...)
-	}
-	mlx.AsyncEval(d.token)
-
-	// Free old cache state from before final step
-	for _, arr := range oldCacheState {
-		if arr != nil {
-			arr.Free()
-		}
-	}
-
-	mlx.ClearCache()
-
-	return processed + len(inputIDs)
-}
-
-func (d *Decoder) step() int32 {
-	prevToken := d.token
-
-	// Save old cache state (reuse preallocated slice)
-	d.oldCacheState = d.oldCacheState[:0]
-	for _, c := range d.caches {
-		d.oldCacheState = append(d.oldCacheState, c.State()...)
-	}
-
-	withStream(func() {
-		logits := d.model.Forward(mlx.Reshape(prevToken, 1, 1), d.caches)
-		d.token = sample(logits, d.temp, d.topK, d.topP, d.vocabSize)
-	})
-	// Keep token and new cache state so they survive cleanup
-	mlx.Keep(d.token)
-	for _, c := range d.caches {
-		mlx.Keep(c.State()...)
-	}
-	mlx.AsyncEval(d.token)
-
-	// Sync on previous token (GPU already working on next step)
-	val := prevToken.ItemInt32()
-
-	// Free old token and old cache state
-	prevToken.Free()
-	for _, arr := range d.oldCacheState {
-		arr.Free()
-	}
-	return val
-}
-
-func generate(ctx context.Context, m Model, in input, cb func(output)) error {
-	mlx.EnableCompile()
-	wiredLimit := in.WiredLimitGB
-	if wiredLimit <= 0 {
-		wiredLimit = 32 // default 32GB
-	}
-	mlx.MetalSetWiredLimit(uint64(wiredLimit) << 30)
-
-	temp := in.Temperature
-	if temp < 0 {
-		temp = 0.7
-	}
-
-	tok := m.Tokenizer()
-	dec := NewDecoder(m, temp, in.TopK, in.TopP)
-
-	// Apply chat template - use image template if we have an image
-	prompt := in.Prompt
-	var tokens []int32
-	if mm, ok := m.(MultimodalModel); ok && in.Image != nil {
-		prompt = mm.FormatPromptWithImage(prompt)
-		tokens = tok.Encode(prompt, true)
-		tokens = mm.ExpandImageTokens(tokens) // Expand <start_of_image> to 256 image tokens
-		dec.SetImage(in.Image)
-	} else if cm, ok := m.(ChatModel); ok {
-		prompt = cm.FormatPrompt(prompt)
-		tokens = tok.Encode(prompt, true)
-	} else {
-		tokens = tok.Encode(prompt, true)
-	}
-
-	prefillStart := time.Now()
-	prefillTokens := dec.prefill(tokens)
-	// Prefill measurement should include time to first token (like mlx-lm)
-	// Step() waits for prefill to complete and returns first token
-	firstToken := dec.step()
-	prefillTokSec := float64(prefillTokens) / time.Since(prefillStart).Seconds()
-
-	genStart := time.Now()
-	maxTokens := max(in.MaxTokens, 100)
-	var genTokens int
-
-	// UTF-8 streamer to handle partial multi-byte characters
-	streamer := &utf8Streamer{}
-
-	// Handle first token
-	genTokens++
-	if tok.IsEOS(firstToken) {
-		cb(output{Done: true, PrefillTokSec: prefillTokSec, GenTokSec: 0})
-		return nil
-	}
-	if text := streamer.Write(tok.Decode([]int32{firstToken})); text != "" {
-		cb(output{Text: text})
-	}
-
-	for n := 1; n < maxTokens; n++ {
-		if ctx.Err() != nil {
-			return ctx.Err()
-		}
-		token := dec.step()
-		genTokens++
-
-		if tok.IsEOS(token) {
-			break
-		}
-		if text := streamer.Write(tok.Decode([]int32{token})); text != "" {
-			cb(output{Text: text})
-		}
-
-		if n%256 == 0 {
-			mlx.ClearCache()
-		}
-	}
-
-	// Flush any remaining buffered bytes
-	if text := streamer.Flush(); text != "" {
-		cb(output{Text: text})
-	}
-
-	fmt.Printf("\nPeak memory: %.2fGB\n", float64(mlx.MetalGetPeakMemory())/(1<<30))
-	cb(output{Done: true, PrefillTokSec: prefillTokSec,
-		GenTokSec: float64(genTokens) / time.Since(genStart).Seconds()})
-	return nil
-}

x/imagegen/cmd/engine/image.go

@@ -1,89 +0,0 @@
-//go:build mlx
-
-package main
-
-import (
-	"fmt"
-	"image"
-	"image/png"
-	"os"
-	"path/filepath"
-
-	"github.com/ollama/ollama/x/imagegen/mlx"
-)
-
-// saveImageArray saves an MLX array as a PNG image.
-// Expected format: [B, C, H, W] with values in [0, 1] range and C=3 (RGB).
-func saveImageArray(arr *mlx.Array, path string) error {
-	img, err := arrayToImage(arr)
-	if err != nil {
-		return err
-	}
-	return savePNG(img, path)
-}
-
-func savePNG(img *image.RGBA, path string) error {
-	if filepath.Ext(path) != ".png" {
-		path = path + ".png"
-	}
-	f, err := os.Create(path)
-	if err != nil {
-		return err
-	}
-	defer f.Close()
-	return png.Encode(f, img)
-}
-
-func arrayToImage(arr *mlx.Array) (*image.RGBA, error) {
-	shape := arr.Shape()
-	if len(shape) != 4 {
-		return nil, fmt.Errorf("expected 4D array [B, C, H, W], got %v", shape)
-	}
-
-	// Transform to [H, W, C] for image conversion
-	img := mlx.Squeeze(arr, 0)
-	arr.Free()
-	img = mlx.Transpose(img, 1, 2, 0)
-	img = mlx.Contiguous(img)
-	mlx.Eval(img)
-
-	imgShape := img.Shape()
-	H := int(imgShape[0])
-	W := int(imgShape[1])
-	C := int(imgShape[2])
-
-	if C != 3 {
-		img.Free()
-		return nil, fmt.Errorf("expected 3 channels (RGB), got %d", C)
-	}
-
-	// Copy to CPU and free GPU memory
-	data := img.Data()
-	img.Free()
-
-	// Write directly to Pix slice (faster than SetRGBA)
-	goImg := image.NewRGBA(image.Rect(0, 0, W, H))
-	pix := goImg.Pix
-	for y := 0; y < H; y++ {
-		for x := 0; x < W; x++ {
-			srcIdx := (y*W + x) * C
-			dstIdx := (y*W + x) * 4
-			pix[dstIdx+0] = uint8(clampF(data[srcIdx+0]*255+0.5, 0, 255))
-			pix[dstIdx+1] = uint8(clampF(data[srcIdx+1]*255+0.5, 0, 255))
-			pix[dstIdx+2] = uint8(clampF(data[srcIdx+2]*255+0.5, 0, 255))
-			pix[dstIdx+3] = 255
-		}
-	}
-
-	return goImg, nil
-}
-
-func clampF(v, min, max float32) float32 {
-	if v < min {
-		return min
-	}
-	if v > max {
-		return max
-	}
-	return v
-}

x/imagegen/cmd/engine/main.go

@@ -1,286 +0,0 @@
-//go:build mlx
-
-package main
-
-import (
-	"context"
-	"encoding/json"
-	"flag"
-	"fmt"
-	"log"
-	"os"
-	"path/filepath"
-	"runtime/pprof"
-
-	"github.com/ollama/ollama/x/imagegen/mlx"
-	"github.com/ollama/ollama/x/imagegen/models/gemma3"
-	"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"
-	"github.com/ollama/ollama/x/imagegen/models/qwen_image_edit"
-	"github.com/ollama/ollama/x/imagegen/models/zimage"
-	"github.com/ollama/ollama/x/imagegen/safetensors"
-)
-
-// stringSlice is a flag type that accumulates multiple values
-type stringSlice []string
-
-func (s *stringSlice) String() string {
-	return fmt.Sprintf("%v", *s)
-}
-
-func (s *stringSlice) Set(value string) error {
-	*s = append(*s, value)
-	return nil
-}
-
-func main() {
-	modelPath := flag.String("model", "", "Model directory")
-	prompt := flag.String("prompt", "Hello", "Prompt")
-
-	// Text generation params
-	maxTokens := flag.Int("max-tokens", 100, "Max tokens")
-	temperature := flag.Float64("temperature", 0.7, "Temperature")
-	topP := flag.Float64("top-p", 0.9, "Top-p sampling")
-	topK := flag.Int("top-k", 40, "Top-k sampling")
-	imagePath := flag.String("image", "", "Image path for multimodal models")
-
-	// Image generation params
-	width := flag.Int("width", 1024, "Image width")
-	height := flag.Int("height", 1024, "Image height")
-	steps := flag.Int("steps", 9, "Denoising steps")
-	seed := flag.Int64("seed", 42, "Random seed")
-	out := flag.String("output", "output.png", "Output path")
-
-	// Utility flags
-	listTensors := flag.Bool("list", false, "List tensors only")
-	cpuProfile := flag.String("cpuprofile", "", "Write CPU profile to file")
-	gpuCapture := flag.String("gpu-capture", "", "Capture GPU trace to .gputrace file (run with MTL_CAPTURE_ENABLED=1)")
-	layerCache := flag.Bool("layer-cache", false, "Enable layer caching for faster diffusion (Z-Image, Qwen-Image). Not compatible with CFG/negative prompts.")
-	wiredLimitGB := flag.Int("wired-limit", 32, "Metal wired memory limit in GB")
-
-	// Legacy mode flags
-	zimageFlag := flag.Bool("zimage", false, "Z-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
-	flag.Var(&inputImages, "input-image", "Input image for image editing (can be specified multiple times)")
-	negativePrompt := flag.String("negative-prompt", "", "Negative prompt for CFG (empty = no CFG, matching Python)")
-	cfgScale := flag.Float64("cfg-scale", 4.0, "CFG scale for image editing")
-
-	flag.Parse()
-
-	if *modelPath == "" {
-		flag.Usage()
-		return
-	}
-
-	// CPU profiling
-	if *cpuProfile != "" {
-		f, err := os.Create(*cpuProfile)
-		if err != nil {
-			log.Fatal(err)
-		}
-		defer f.Close()
-		if err := pprof.StartCPUProfile(f); err != nil {
-			log.Fatal(err)
-		}
-		defer pprof.StopCPUProfile()
-	}
-
-	var err error
-
-	// Handle legacy mode flags that aren't unified yet
-	switch {
-	case *zimageFlag:
-		m := &zimage.Model{}
-		if loadErr := m.Load(*modelPath); loadErr != nil {
-			log.Fatal(loadErr)
-		}
-		var img *mlx.Array
-		img, err = m.GenerateFromConfig(context.Background(), &zimage.GenerateConfig{
-			Prompt:      *prompt,
-			Width:       int32(*width),
-			Height:      int32(*height),
-			Steps:       *steps,
-			Seed:        *seed,
-			CapturePath: *gpuCapture,
-			LayerCache:  *layerCache,
-		})
-		if err == nil {
-			err = saveImageArray(img, *out)
-		}
-	case *qwenImage:
-		m, loadErr := qwen_image.LoadPersistent(*modelPath)
-		if loadErr != nil {
-			log.Fatal(loadErr)
-		}
-		var img *mlx.Array
-		img, err = m.GenerateFromConfig(&qwen_image.GenerateConfig{
-			Prompt:         *prompt,
-			NegativePrompt: *negativePrompt,
-			CFGScale:       float32(*cfgScale),
-			Width:          int32(*width),
-			Height:         int32(*height),
-			Steps:          *steps,
-			Seed:           *seed,
-			LayerCache:     *layerCache,
-		})
-		if err == nil {
-			err = saveImageArray(img, *out)
-		}
-	case *qwenImageEdit:
-		if len(inputImages) == 0 {
-			log.Fatal("qwen-image-edit requires at least one -input-image")
-		}
-
-		m, loadErr := qwen_image_edit.LoadPersistent(*modelPath)
-		if loadErr != nil {
-			log.Fatal(loadErr)
-		}
-		// For image editing, use 0 for dimensions to auto-detect from input image
-		// unless explicitly overridden from defaults
-		editWidth := int32(0)
-		editHeight := int32(0)
-		if *width != 1024 {
-			editWidth = int32(*width)
-		}
-		if *height != 1024 {
-			editHeight = int32(*height)
-		}
-
-		cfg := &qwen_image_edit.GenerateConfig{
-			Prompt:         *prompt,
-			NegativePrompt: *negativePrompt,
-			CFGScale:       float32(*cfgScale),
-			Width:          editWidth,
-			Height:         editHeight,
-			Steps:          *steps,
-			Seed:           *seed,
-		}
-
-		var img *mlx.Array
-		img, err = m.EditFromConfig(inputImages, cfg)
-		if err == nil {
-			err = saveImageArray(img, *out)
-		}
-	case *listTensors:
-		err = listModelTensors(*modelPath)
-	default:
-		// llm path
-		m, err := load(*modelPath)
-		if err != nil {
-			log.Fatal(err)
-		}
-
-		// Load image if provided and model supports it
-		var image *mlx.Array
-		if *imagePath != "" {
-			if mm, ok := m.(interface{ ImageSize() int32 }); ok {
-				image, err = gemma3.ProcessImage(*imagePath, mm.ImageSize())
-				if err != nil {
-					log.Fatal("load image:", err)
-				}
-			} else {
-				log.Fatal("model does not support image input")
-			}
-		}
-
-		err = generate(context.Background(), m, input{
-			Prompt:       *prompt,
-			Image:        image,
-			MaxTokens:    *maxTokens,
-			Temperature:  float32(*temperature),
-			TopP:         float32(*topP),
-			TopK:         *topK,
-			WiredLimitGB: *wiredLimitGB,
-		}, func(out output) {
-			if out.Text != "" {
-				fmt.Print(out.Text)
-			}
-			if out.Done {
-				fmt.Printf("\n\n[prefill: %.1f tok/s, gen: %.1f tok/s]\n", out.PrefillTokSec, out.GenTokSec)
-			}
-		})
-	}
-
-	if err != nil {
-		log.Fatal(err)
-	}
-}
-
-func listModelTensors(modelPath string) error {
-	weights, err := safetensors.LoadModelWeights(modelPath)
-	if err != nil {
-		return err
-	}
-	for _, name := range weights.ListTensors() {
-		info, _ := weights.GetTensorInfo(name)
-		fmt.Printf("%s: %v (%s)\n", name, info.Shape, info.Dtype)
-	}
-	return nil
-}
-
-// loadModel builds and evaluates a model using the common load pattern.
-// Release safetensors BEFORE eval - lazy arrays have captured their data,
-// and this reduces peak memory by ~6GB (matches mlx-lm behavior).
-func loadModel[T Model](build func() T, cleanup func()) T {
-	m := build()
-	weights := mlx.Collect(m)
-	cleanup()
-	mlx.Eval(weights...)
-	return m
-}
-
-func load(modelPath string) (Model, error) {
-	kind, err := detectModelKind(modelPath)
-	if err != nil {
-		return nil, fmt.Errorf("detect model kind: %w", err)
-	}
-
-	switch kind {
-	case "gpt_oss":
-		return gpt_oss.Load(modelPath)
-	case "gemma3":
-		return gemma3.Load(modelPath)
-	case "gemma3_text":
-		return gemma3.LoadText(modelPath)
-	default:
-		return llama.Load(modelPath)
-	}
-}
-
-func detectModelKind(modelPath string) (string, error) {
-	indexPath := filepath.Join(modelPath, "model_index.json")
-	if _, err := os.Stat(indexPath); err == nil {
-		data, err := os.ReadFile(indexPath)
-		if err != nil {
-			return "zimage", nil
-		}
-		var index struct {
-			ClassName string `json:"_class_name"`
-		}
-		if err := json.Unmarshal(data, &index); err == nil {
-			switch index.ClassName {
-			case "FluxPipeline", "ZImagePipeline":
-				return "zimage", nil
-			}
-		}
-		return "zimage", nil
-	}
-
-	configPath := filepath.Join(modelPath, "config.json")
-	data, err := os.ReadFile(configPath)
-	if err != nil {
-		return "", fmt.Errorf("no config.json or model_index.json found: %w", err)
-	}
-
-	var cfg struct {
-		ModelType string `json:"model_type"`
-	}
-	if err := json.Unmarshal(data, &cfg); err != nil {
-		return "", fmt.Errorf("parse config.json: %w", err)
-	}
-
-	return cfg.ModelType, nil
-}

x/imagegen/cmd/engine/sample.go

@@ -1,49 +0,0 @@
-//go:build mlx
-
-package main
-
-import "github.com/ollama/ollama/x/imagegen/mlx"
-
-// sampleTopK samples from top-k logits using global random state
-func sampleTopK(scaledLogits *mlx.Array, k int) *mlx.Array {
-	neg := mlx.Neg(scaledLogits)
-	indices := mlx.Argpartition(neg, k-1, -1)
-	topKIdx := mlx.Slice(indices, []int32{0}, []int32{int32(k)})
-	values := mlx.TakeAlongAxis(scaledLogits, topKIdx, -1)
-	sampled := mlx.RandomCategorical(values, -1, 1)
-	return mlx.Take(topKIdx, sampled, -1)
-}
-
-// sampleTopP samples using nucleus sampling with global random state
-func sampleTopP(scaledLogits *mlx.Array, p float32, vocabSize int32) *mlx.Array {
-	sorted := mlx.Argsort(mlx.Neg(scaledLogits), -1)
-	sortedLogits := mlx.TakeAlongAxis(scaledLogits, sorted, -1)
-	probs := mlx.Softmax(sortedLogits, -1)
-	cumProbs := mlx.Cumsum(probs, -1)
-	mask := mlx.LessScalar(cumProbs, p)
-	negInf := mlx.FullDtype(float32(-1e9), scaledLogits.Dtype(), vocabSize)
-	masked := mlx.Where(mask, sortedLogits, negInf)
-	sampled := mlx.RandomCategorical(masked, -1, 1)
-	return mlx.Take(sorted, sampled, -1)
-}
-
-// sample samples from logits at the last position
-func sample(logits *mlx.Array, temp float32, topK int, topP float32, vocab int32) *mlx.Array {
-	// Get last position logits: [1, L, vocab] -> [vocab]
-	shape := logits.Shape()
-	seqLen := shape[1]
-	lastLogits := mlx.Slice(logits, []int32{0, seqLen - 1, 0}, []int32{1, seqLen, vocab})
-	lastLogits = mlx.Reshape(lastLogits, vocab)
-
-	if temp == 0 {
-		return mlx.Argmax(lastLogits, -1, false)
-	}
-	scaled := mlx.DivScalar(lastLogits, temp)
-	if topK > 0 && topK < int(vocab) {
-		return sampleTopK(scaled, topK)
-	}
-	if topP > 0 && topP < 1.0 {
-		return sampleTopP(scaled, topP, vocab)
-	}
-	return mlx.RandomCategorical(scaled, -1, 1)
-}

x/imagegen/create.go

@@ -1,183 +0,0 @@
-package imagegen
-
-import (
-	"bytes"
-	"encoding/json"
-	"fmt"
-	"io"
-	"os"
-	"path/filepath"
-	"strings"
-
-	"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.
-// Stores each tensor as a separate blob for fine-grained deduplication.
-// Layer creation and manifest writing are done via callbacks to avoid import cycles.
-func CreateModel(modelName, modelDir string, createLayer LayerCreator, createTensorLayer TensorLayerCreator, writeManifest ManifestWriter, fn func(status string)) error {
-	var layers []LayerInfo
-	var configLayer LayerInfo
-
-	// Components to process - extract individual tensors from each
-	components := []string{"text_encoder", "transformer", "vae"}
-
-	for _, component := range components {
-		componentDir := filepath.Join(modelDir, component)
-		if _, err := os.Stat(componentDir); os.IsNotExist(err) {
-			continue
-		}
-
-		// Find all safetensors files in this component
-		entries, err := os.ReadDir(componentDir)
-		if err != nil {
-			return fmt.Errorf("failed to read %s: %w", component, err)
-		}
-
-		for _, entry := range entries {
-			if !strings.HasSuffix(entry.Name(), ".safetensors") {
-				continue
-			}
-
-			stPath := filepath.Join(componentDir, 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()
-			fn(fmt.Sprintf("importing %s/%s (%d tensors)", component, entry.Name(), len(tensorNames)))
-
-			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)
-				}
-
-				// Store as minimal safetensors format (88 bytes header overhead)
-				// This enables native mmap loading via mlx_load_safetensors
-				// Use path-style name: "component/tensor_name"
-				fullName := component + "/" + tensorName
-				layer, err := createTensorLayer(td.SafetensorsReader(), fullName, td.Dtype, td.Shape)
-				if err != nil {
-					extractor.Close()
-					return fmt.Errorf("failed to create layer for %s: %w", fullName, err)
-				}
-				layers = append(layers, layer)
-			}
-
-			extractor.Close()
-		}
-	}
-
-	// Import config files
-	configFiles := []string{
-		"model_index.json",
-		"text_encoder/config.json",
-		"text_encoder/generation_config.json",
-		"transformer/config.json",
-		"vae/config.json",
-		"scheduler/scheduler_config.json",
-		"tokenizer/tokenizer.json",
-		"tokenizer/tokenizer_config.json",
-		"tokenizer/vocab.json",
-	}
-
-	for _, cfgPath := range configFiles {
-		fullPath := filepath.Join(modelDir, cfgPath)
-		if _, err := os.Stat(fullPath); os.IsNotExist(err) {
-			continue
-		}
-
-		fn(fmt.Sprintf("importing config %s", cfgPath))
-
-		var r io.Reader
-
-		// For model_index.json, normalize to Ollama format
-		if cfgPath == "model_index.json" {
-			data, err := os.ReadFile(fullPath)
-			if err != nil {
-				return fmt.Errorf("failed to read %s: %w", cfgPath, err)
-			}
-
-			var cfg map[string]any
-			if err := json.Unmarshal(data, &cfg); err != nil {
-				return fmt.Errorf("failed to parse %s: %w", cfgPath, err)
-			}
-
-			// Rename _class_name to architecture, remove diffusers-specific fields
-			if className, ok := cfg["_class_name"]; ok {
-				cfg["architecture"] = className
-				delete(cfg, "_class_name")
-			}
-			delete(cfg, "_diffusers_version")
-
-			data, err = json.MarshalIndent(cfg, "", "    ")
-			if err != nil {
-				return fmt.Errorf("failed to marshal %s: %w", cfgPath, err)
-			}
-			r = bytes.NewReader(data)
-		} else {
-			f, err := os.Open(fullPath)
-			if err != nil {
-				return fmt.Errorf("failed to open %s: %w", cfgPath, err)
-			}
-			defer f.Close()
-			r = f
-		}
-
-		layer, err := createLayer(r, "application/vnd.ollama.image.json", cfgPath)
-		if err != nil {
-			return fmt.Errorf("failed to create layer for %s: %w", cfgPath, err)
-		}
-
-		// Use model_index.json as the config layer
-		if cfgPath == "model_index.json" {
-			configLayer = layer
-		}
-
-		layers = append(layers, layer)
-	}
-
-	if configLayer.Digest == "" {
-		return fmt.Errorf("model_index.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/imagegen/image.go

@@ -1,107 +0,0 @@
-//go:build mlx
-
-package imagegen
-
-import (
-	"bytes"
-	"encoding/base64"
-	"fmt"
-	"image"
-	"image/png"
-	"os"
-	"path/filepath"
-
-	"github.com/ollama/ollama/x/imagegen/mlx"
-)
-
-// SaveImage saves an MLX array as a PNG image file.
-// Expected format: [B, C, H, W] with values in [0, 1] range and C=3 (RGB).
-func SaveImage(arr *mlx.Array, path string) error {
-	img, err := ArrayToImage(arr)
-	if err != nil {
-		return err
-	}
-
-	if filepath.Ext(path) != ".png" {
-		path = path + ".png"
-	}
-
-	f, err := os.Create(path)
-	if err != nil {
-		return err
-	}
-	defer f.Close()
-
-	return png.Encode(f, img)
-}
-
-// EncodeImageBase64 encodes an MLX array as a base64-encoded PNG.
-// Expected format: [B, C, H, W] with values in [0, 1] range and C=3 (RGB).
-func EncodeImageBase64(arr *mlx.Array) (string, error) {
-	img, err := ArrayToImage(arr)
-	if err != nil {
-		return "", err
-	}
-
-	var buf bytes.Buffer
-	if err := png.Encode(&buf, img); err != nil {
-		return "", err
-	}
-
-	return base64.StdEncoding.EncodeToString(buf.Bytes()), nil
-}
-
-// ArrayToImage converts an MLX array to a Go image.RGBA.
-// Expected format: [B, C, H, W] with values in [0, 1] range and C=3 (RGB).
-func ArrayToImage(arr *mlx.Array) (*image.RGBA, error) {
-	shape := arr.Shape()
-	if len(shape) != 4 {
-		return nil, fmt.Errorf("expected 4D array [B, C, H, W], got %v", shape)
-	}
-
-	// Transform to [H, W, C] for image conversion
-	img := mlx.Squeeze(arr, 0)
-	img = mlx.Transpose(img, 1, 2, 0)
-	img = mlx.Contiguous(img)
-	mlx.Eval(img)
-
-	imgShape := img.Shape()
-	H := int(imgShape[0])
-	W := int(imgShape[1])
-	C := int(imgShape[2])
-
-	if C != 3 {
-		img.Free()
-		return nil, fmt.Errorf("expected 3 channels (RGB), got %d", C)
-	}
-
-	// Copy to CPU and free GPU memory
-	data := img.Data()
-	img.Free()
-
-	// Write directly to Pix slice (faster than SetRGBA)
-	goImg := image.NewRGBA(image.Rect(0, 0, W, H))
-	pix := goImg.Pix
-	for y := 0; y < H; y++ {
-		for x := 0; x < W; x++ {
-			srcIdx := (y*W + x) * C
-			dstIdx := (y*W + x) * 4
-			pix[dstIdx+0] = uint8(clampF(data[srcIdx+0]*255+0.5, 0, 255))
-			pix[dstIdx+1] = uint8(clampF(data[srcIdx+1]*255+0.5, 0, 255))
-			pix[dstIdx+2] = uint8(clampF(data[srcIdx+2]*255+0.5, 0, 255))
-			pix[dstIdx+3] = 255
-		}
-	}
-
-	return goImg, nil
-}
-
-func clampF(v, min, max float32) float32 {
-	if v < min {
-		return min
-	}
-	if v > max {
-		return max
-	}
-	return v
-}

x/imagegen/manifest.go

@@ -1,177 +0,0 @@
-package imagegen
-
-import (
-	"encoding/json"
-	"fmt"
-	"io"
-	"os"
-	"path/filepath"
-	"runtime"
-	"strings"
-)
-
-// 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"` // Path-style name: "component/tensor" or "path/to/config.json"
-}
-
-// 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"`
-}
-
-// ModelManifest holds a parsed manifest with helper methods.
-type ModelManifest struct {
-	Manifest *Manifest
-	BlobDir  string
-}
-
-// DefaultBlobDir returns the default blob storage directory.
-func DefaultBlobDir() string {
-	home, err := os.UserHomeDir()
-	if err != nil {
-		home = "."
-	}
-	switch runtime.GOOS {
-	case "darwin":
-		return filepath.Join(home, ".ollama", "models", "blobs")
-	case "linux":
-		return filepath.Join(home, ".ollama", "models", "blobs")
-	case "windows":
-		return filepath.Join(home, ".ollama", "models", "blobs")
-	default:
-		return filepath.Join(home, ".ollama", "models", "blobs")
-	}
-}
-
-// DefaultManifestDir returns the default manifest storage directory.
-func DefaultManifestDir() string {
-	home, err := os.UserHomeDir()
-	if err != nil {
-		home = "."
-	}
-	return filepath.Join(home, ".ollama", "models", "manifests")
-}
-
-// LoadManifest loads a manifest for the given model name.
-// Model name format: "modelname" or "modelname:tag" or "host/namespace/name:tag"
-func LoadManifest(modelName string) (*ModelManifest, error) {
-	manifestPath := resolveManifestPath(modelName)
-
-	data, err := os.ReadFile(manifestPath)
-	if err != nil {
-		return nil, fmt.Errorf("read manifest: %w", err)
-	}
-
-	var manifest Manifest
-	if err := json.Unmarshal(data, &manifest); err != nil {
-		return nil, fmt.Errorf("parse manifest: %w", err)
-	}
-
-	return &ModelManifest{
-		Manifest: &manifest,
-		BlobDir:  DefaultBlobDir(),
-	}, nil
-}
-
-// resolveManifestPath converts a model name to a manifest file path.
-func resolveManifestPath(modelName string) string {
-	// Parse model name into components
-	// Default: registry.ollama.ai/library/<name>/<tag>
-	host := "registry.ollama.ai"
-	namespace := "library"
-	name := modelName
-	tag := "latest"
-
-	// Handle explicit tag
-	if idx := strings.LastIndex(name, ":"); idx != -1 {
-		tag = name[idx+1:]
-		name = name[:idx]
-	}
-
-	// Handle full path like "host/namespace/name"
-	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)
-}
-
-// BlobPath returns the full path to a blob given its digest.
-func (m *ModelManifest) BlobPath(digest string) string {
-	// Convert "sha256:abc123" to "sha256-abc123"
-	blobName := strings.Replace(digest, ":", "-", 1)
-	return filepath.Join(m.BlobDir, blobName)
-}
-
-// GetTensorLayers returns all tensor layers for a given component.
-// Component should be "text_encoder", "transformer", or "vae".
-// Tensor names are path-style: "component/tensor_name" (e.g., "text_encoder/model.embed_tokens.weight").
-func (m *ModelManifest) GetTensorLayers(component string) []ManifestLayer {
-	prefix := component + "/"
-	var layers []ManifestLayer
-	for _, layer := range m.Manifest.Layers {
-		if layer.MediaType == "application/vnd.ollama.image.tensor" && strings.HasPrefix(layer.Name, prefix) {
-			layers = append(layers, layer)
-		}
-	}
-	return layers
-}
-
-// GetConfigLayer returns the config layer for a given path.
-func (m *ModelManifest) GetConfigLayer(configPath string) *ManifestLayer {
-	for _, layer := range m.Manifest.Layers {
-		if layer.MediaType == "application/vnd.ollama.image.json" && layer.Name == configPath {
-			return &layer
-		}
-	}
-	return nil
-}
-
-// ReadConfig reads and returns the content of a config file.
-func (m *ModelManifest) ReadConfig(configPath string) ([]byte, error) {
-	layer := m.GetConfigLayer(configPath)
-	if layer == nil {
-		return nil, fmt.Errorf("config %q not found in manifest", configPath)
-	}
-
-	blobPath := m.BlobPath(layer.Digest)
-	return os.ReadFile(blobPath)
-}
-
-// ReadConfigJSON reads and unmarshals a config file.
-func (m *ModelManifest) ReadConfigJSON(configPath string, v any) error {
-	data, err := m.ReadConfig(configPath)
-	if err != nil {
-		return err
-	}
-	return json.Unmarshal(data, v)
-}
-
-// OpenBlob opens a blob for reading.
-func (m *ModelManifest) OpenBlob(digest string) (io.ReadCloser, error) {
-	return os.Open(m.BlobPath(digest))
-}
-
-// HasTensorLayers returns true if the manifest has any tensor layers.
-func (m *ModelManifest) HasTensorLayers() bool {
-	for _, layer := range m.Manifest.Layers {
-		if layer.MediaType == "application/vnd.ollama.image.tensor" {
-			return true
-		}
-	}
-	return false
-}

x/imagegen/memory.go

@@ -1,102 +0,0 @@
-// Package imagegen provides experimental image generation capabilities for Ollama.
-//
-// This package is in x/ because the tensor model storage format is under development.
-// The goal is to integrate these capabilities into the main Ollama packages once
-// the format is stable.
-//
-// TODO (jmorganca): Integrate into main packages when stable:
-//   - CLI commands → cmd/
-//   - API endpoints → api/
-//   - Model creation → server/
-package imagegen
-
-import (
-	"encoding/json"
-	"fmt"
-	"runtime"
-)
-
-// GB is a convenience constant for gigabytes.
-const GB = 1024 * 1024 * 1024
-
-// SupportedBackends lists the backends that support image generation.
-var SupportedBackends = []string{"metal", "cuda", "cpu"}
-
-// modelVRAMEstimates maps pipeline class names to their estimated VRAM requirements.
-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
-}
-
-// CheckPlatformSupport validates that image generation is supported on the current platform.
-// Returns nil if supported, or an error describing why it's not supported.
-func CheckPlatformSupport() error {
-	switch runtime.GOOS {
-	case "darwin":
-		// macOS: Metal is supported via MLX
-		if runtime.GOARCH != "arm64" {
-			return fmt.Errorf("image generation on macOS requires Apple Silicon (arm64), got %s", runtime.GOARCH)
-		}
-		return nil
-	case "linux", "windows":
-		// Linux/Windows: CUDA support (requires mlx or cuda build)
-		// The actual backend availability is checked at runtime
-		return nil
-	default:
-		return fmt.Errorf("image generation is not supported on %s", runtime.GOOS)
-	}
-}
-
-// CheckMemoryRequirements validates that there's enough memory for image generation.
-// Returns nil if memory is sufficient, or an error if not.
-func CheckMemoryRequirements(modelName string, availableMemory uint64) error {
-	required := EstimateVRAM(modelName)
-	if availableMemory < required {
-		return fmt.Errorf("insufficient memory for image generation: need %d GB, have %d GB",
-			required/GB, availableMemory/GB)
-	}
-	return nil
-}
-
-// ResolveModelName checks if a model name is a known image generation model.
-// Returns the normalized model name if found, empty string otherwise.
-func ResolveModelName(modelName string) string {
-	manifest, err := LoadManifest(modelName)
-	if err == nil && manifest.HasTensorLayers() {
-		return modelName
-	}
-	return ""
-}
-
-// EstimateVRAM returns the estimated VRAM needed for an image generation model.
-// Returns a conservative default of 21GB if the model type cannot be determined.
-func EstimateVRAM(modelName string) uint64 {
-	manifest, err := LoadManifest(modelName)
-	if err != nil {
-		return 21 * GB
-	}
-
-	data, err := manifest.ReadConfig("model_index.json")
-	if err != nil {
-		return 21 * GB
-	}
-
-	// Parse just the class name
-	var index struct {
-		ClassName string `json:"_class_name"`
-	}
-	if err := json.Unmarshal(data, &index); err != nil {
-		return 21 * GB
-	}
-
-	if estimate, ok := modelVRAMEstimates[index.ClassName]; ok {
-		return estimate
-	}
-	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

@@ -1,110 +0,0 @@
-package imagegen
-
-import (
-	"runtime"
-	"testing"
-)
-
-func TestCheckPlatformSupport(t *testing.T) {
-	err := CheckPlatformSupport()
-
-	switch runtime.GOOS {
-	case "darwin":
-		if runtime.GOARCH == "arm64" {
-			if err != nil {
-				t.Errorf("Expected nil error on darwin/arm64, got: %v", err)
-			}
-		} else {
-			if err == nil {
-				t.Error("Expected error on darwin/non-arm64")
-			}
-		}
-	case "linux", "windows":
-		if err != nil {
-			t.Errorf("Expected nil error on %s, got: %v", runtime.GOOS, err)
-		}
-	default:
-		if err == nil {
-			t.Errorf("Expected error on unsupported platform %s", runtime.GOOS)
-		}
-	}
-}
-
-func TestCheckMemoryRequirements(t *testing.T) {
-	tests := []struct {
-		name            string
-		availableMemory uint64
-		wantErr         bool
-	}{
-		{
-			name:            "sufficient memory",
-			availableMemory: 32 * GB,
-			wantErr:         false,
-		},
-		{
-			name:            "exactly enough memory",
-			availableMemory: 21 * GB,
-			wantErr:         false,
-		},
-		{
-			name:            "insufficient memory",
-			availableMemory: 16 * GB,
-			wantErr:         true,
-		},
-		{
-			name:            "zero memory",
-			availableMemory: 0,
-			wantErr:         true,
-		},
-	}
-
-	for _, tt := range tests {
-		t.Run(tt.name, func(t *testing.T) {
-			// Use a non-existent model name which will default to 21GB estimate
-			err := CheckMemoryRequirements("nonexistent-model", tt.availableMemory)
-			if (err != nil) != tt.wantErr {
-				t.Errorf("CheckMemoryRequirements() error = %v, wantErr %v", err, tt.wantErr)
-			}
-		})
-	}
-}
-
-func TestModelVRAMEstimates(t *testing.T) {
-	// Verify the VRAM estimates map has expected entries
-	expected := map[string]uint64{
-		"ZImagePipeline":    21 * GB,
-		"FluxPipeline":      21 * GB,
-		"QwenImagePipeline": 80 * GB,
-	}
-
-	for name, expectedVRAM := range expected {
-		if actual, ok := modelVRAMEstimates[name]; !ok {
-			t.Errorf("Missing VRAM estimate for %s", name)
-		} else if actual != expectedVRAM {
-			t.Errorf("VRAM estimate for %s = %d GB, want %d GB", name, actual/GB, expectedVRAM/GB)
-		}
-	}
-}
-
-func TestEstimateVRAMDefault(t *testing.T) {
-	// Non-existent model should return default 21GB
-	vram := EstimateVRAM("nonexistent-model-that-does-not-exist")
-	if vram != 21*GB {
-		t.Errorf("EstimateVRAM() = %d GB, want 21 GB", vram/GB)
-	}
-}
-
-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")
-	if result != "" {
-		t.Errorf("ResolveModelName() = %q, want empty string", result)
-	}
-}

x/imagegen/mlx/README.md

@@ -1,46 +0,0 @@
-# MLX Memory Management
-
-| This package will get consolidated with `x/ml/backend/mlx` in the future.
-
-## Automatic Tracking
-
-All arrays are automatically tracked when created. On `Eval()`, non-kept arrays are freed.
-
-### API
-
-```go
-result := mlx.Matmul(x, w) // arrays automatically tracked
-mlx.Eval(result)           // free non-kept, eval result (auto-kept)
-```
-
-### Key Functions
-
-- `mlx.Eval(outputs...)` - free non-kept arrays, then evaluate (outputs auto-kept)
-- `mlx.AsyncEval(outputs...)` - async version of Eval (outputs auto-kept)
-- `mlx.Keep(arrays...)` - mark arrays to survive cleanup (for weights, caches)
-- `array.Free()` - mark array for cleanup on next Eval
-
-### Loop Pattern
-
-```go
-for step := 0; step < maxTokens; step++ {
-    logits := model.Forward(token, caches)
-    oldToken := token
-    token = sample(logits)
-
-    // Keep cache state across iterations
-    for _, c := range caches {
-        mlx.Keep(c.State()...)
-    }
-
-    oldToken.Free()       // mark for cleanup
-    mlx.AsyncEval(token)  // frees old, evals new
-}
-```
-
-### Notes
-
-- `Eval()` and `AsyncEval()` auto-keep their outputs
-- `Free()` marks for cleanup - actual free happens during next Eval
-- Use `Keep()` for weights and cache state that must survive multiple Eval cycles
-- Arrays created inside compiled closures are managed by MLX, not tracked

x/imagegen/mlx/compile.go

@@ -1,173 +0,0 @@
-//go:build mlx
-
-package mlx
-
-/*
-#include "mlx/c/mlx.h"
-#include <stdlib.h>
-
-// Forward declaration for Go callback
-extern int goClosureCallback(mlx_vector_array* res, mlx_vector_array input, void* payload);
-
-// Destructor for payload (Go handle)
-extern void goClosureDestructor(void* payload);
-*/
-import "C"
-import (
-	"runtime/cgo"
-	"sync"
-	"unsafe"
-)
-
-// inClosureCallback is set to true during closure callback execution.
-var inClosureCallback bool
-var closureCallbackMu sync.Mutex
-
-// InClosureCallback returns true if we're currently executing inside a closure callback.
-func InClosureCallback() bool {
-	closureCallbackMu.Lock()
-	defer closureCallbackMu.Unlock()
-	return inClosureCallback
-}
-
-// CompiledFunc is a compiled MLX function that can be called efficiently.
-// All intermediate arrays during execution stay inside MLX - only inputs
-// and outputs cross the Go boundary.
-type CompiledFunc struct {
-	closure  C.mlx_closure
-	compiled C.mlx_closure
-}
-
-// ClosureFunc is the signature for functions that can be compiled.
-// It takes a slice of input arrays and returns a slice of output arrays.
-type ClosureFunc func(inputs []*Array) []*Array
-
-// Compile compiles a Go function into an optimized MLX closure.
-// The function is traced once during compilation, then subsequent calls
-// run the optimized graph without creating Go intermediate arrays.
-//
-// Example:
-//
-//	compiled := mlx.Compile(func(inputs []*mlx.Array) []*mlx.Array {
-//	    a, b := inputs[0], inputs[1]
-//	    c := mlx.Add(a, b)
-//	    d := mlx.Mul(c, c)
-//	    return []*mlx.Array{d}
-//	})
-//	defer compiled.Free()
-//
-//	result := compiled.Call(x, y)[0]
-func Compile(fn ClosureFunc) *CompiledFunc {
-	return CompileShapeless(fn, false)
-}
-
-// CompileShapeless compiles with optional shapeless mode.
-// If shapeless=true, the function works for any input shape after tracing.
-func CompileShapeless(fn ClosureFunc, shapeless bool) *CompiledFunc {
-	// Create a cgo.Handle to prevent the Go function from being GC'd
-	handle := cgo.NewHandle(fn)
-
-	// Create the closure from the Go callback
-	closure := C.mlx_closure_new_func_payload(
-		(*[0]byte)(C.goClosureCallback),
-		unsafe.Pointer(handle),
-		(*[0]byte)(C.goClosureDestructor),
-	)
-
-	// Compile the closure
-	compiled := C.mlx_closure_new()
-	C.mlx_compile(&compiled, closure, C.bool(shapeless))
-
-	return &CompiledFunc{
-		closure:  closure,
-		compiled: compiled,
-	}
-}
-
-// Call invokes the compiled function with the given inputs.
-func (cf *CompiledFunc) Call(inputs ...*Array) []*Array {
-	// Pack inputs into vector
-	inputVec := C.mlx_vector_array_new()
-	for _, arr := range inputs {
-		C.mlx_vector_array_append_value(inputVec, arr.c)
-	}
-
-	// Apply compiled closure
-	outputVec := C.mlx_vector_array_new()
-	C.mlx_closure_apply(&outputVec, cf.compiled, inputVec)
-	C.mlx_vector_array_free(inputVec)
-
-	// Unpack outputs
-	numOutputs := int(C.mlx_vector_array_size(outputVec))
-	outputs := make([]*Array, numOutputs)
-	for i := 0; i < numOutputs; i++ {
-		var arr C.mlx_array
-		C.mlx_vector_array_get(&arr, outputVec, C.size_t(i))
-		outputs[i] = newArray(arr)
-	}
-	C.mlx_vector_array_free(outputVec)
-
-	return outputs
-}
-
-// CallEval invokes the compiled function and evaluates the results.
-func (cf *CompiledFunc) CallEval(inputs ...*Array) []*Array {
-	outputs := cf.Call(inputs...)
-	Eval(outputs...)
-	return outputs
-}
-
-// Free releases the compiled function resources.
-func (cf *CompiledFunc) Free() {
-	C.mlx_closure_free(cf.compiled)
-	C.mlx_closure_free(cf.closure)
-}
-
-// borrowArray wraps a C array WITHOUT setting up GC cleanup.
-// Use this for arrays we don't own (e.g., borrowed references in callbacks).
-func borrowArray(array C.mlx_array) *Array {
-	return &Array{c: array}
-}
-
-//export goClosureCallback
-func goClosureCallback(res *C.mlx_vector_array, input C.mlx_vector_array, payload unsafe.Pointer) C.int {
-	// Set flag to disable AddCleanup during callback
-	closureCallbackMu.Lock()
-	inClosureCallback = true
-	closureCallbackMu.Unlock()
-	defer func() {
-		closureCallbackMu.Lock()
-		inClosureCallback = false
-		closureCallbackMu.Unlock()
-	}()
-
-	// Recover the Go function from the handle
-	handle := cgo.Handle(payload)
-	fn := handle.Value().(ClosureFunc)
-
-	// Convert input vector to Go slice - use borrowArray since MLX owns these
-	numInputs := int(C.mlx_vector_array_size(input))
-	inputs := make([]*Array, numInputs)
-	for i := 0; i < numInputs; i++ {
-		var arr C.mlx_array
-		C.mlx_vector_array_get(&arr, input, C.size_t(i))
-		inputs[i] = borrowArray(arr) // Don't set up cleanup - MLX owns these
-	}
-
-	// Call the Go function
-	outputs := fn(inputs)
-
-	// Build output vector
-	*res = C.mlx_vector_array_new()
-	for _, arr := range outputs {
-		C.mlx_vector_array_append_value(*res, arr.c)
-	}
-
-	return 0
-}
-
-//export goClosureDestructor
-func goClosureDestructor(payload unsafe.Pointer) {
-	handle := cgo.Handle(payload)
-	handle.Delete()
-}

x/imagegen/models/gemma3/gemma3.go

@@ -1,614 +0,0 @@
-//go:build mlx
-
-package gemma3
-
-import (
-	"encoding/json"
-	"fmt"
-	"math"
-	"os"
-	"path/filepath"
-
-	"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"
-	"github.com/ollama/ollama/x/imagegen/tokenizer"
-)
-
-// TextConfig holds configuration for the text model
-type 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"`
-	HeadDim               int32   `json:"head_dim"`
-	VocabSize             int32   `json:"vocab_size"`
-	RMSNormEps            float32 `json:"rms_norm_eps"`
-	RopeTheta             float32 `json:"rope_theta"`
-	RopeLocalBaseFreq     float32 `json:"rope_local_base_freq"`
-	MaxPositionEmbeddings int32   `json:"max_position_embeddings"`
-	SlidingWindow         int32   `json:"sliding_window"`
-	SlidingWindowPattern  int32   `json:"sliding_window_pattern"`
-
-	// Computed fields
-	Scale float32 `json:"-"`
-}
-
-// TextModel is the Gemma 3 text-only model
-type TextModel struct {
-	EmbedTokens *nn.Embedding   `weight:"model.embed_tokens"`
-	Layers      []*DecoderLayer `weight:"model.layers"`
-	Norm        *nn.RMSNorm     `weight:"model.norm"`
-	Output      *nn.Linear      `weight:"-"` // Tied to EmbedTokens, set manually
-
-	// Precomputed (1 + weight) for Gemma-style RMSNorm to avoid allocation per forward
-	NormScaled *mlx.Array `weight:"-"`
-
-	tok *tokenizer.Tokenizer
-	*TextConfig
-}
-
-// DecoderLayer is a single transformer block
-type DecoderLayer struct {
-	InputNorm    *nn.RMSNorm `weight:"input_layernorm"`
-	Attention    *Attention
-	PostAttnNorm *nn.RMSNorm `weight:"post_attention_layernorm"`
-	PreFFNorm    *nn.RMSNorm `weight:"pre_feedforward_layernorm"`
-	MLP          *MLP
-	PostFFNorm   *nn.RMSNorm `weight:"post_feedforward_layernorm"`
-
-	// Precomputed (1 + weight) for Gemma-style RMSNorm
-	InputNormScaled    *mlx.Array `weight:"-"`
-	PostAttnNormScaled *mlx.Array `weight:"-"`
-	PreFFNormScaled    *mlx.Array `weight:"-"`
-	PostFFNormScaled   *mlx.Array `weight:"-"`
-
-	// Whether this layer uses sliding window attention
-	IsSliding bool
-	LayerIdx  int32
-}
-
-// Attention implements Gemma 3 attention with Q/K normalization
-type Attention struct {
-	QProj *nn.Linear  `weight:"self_attn.q_proj"`
-	KProj *nn.Linear  `weight:"self_attn.k_proj"`
-	VProj *nn.Linear  `weight:"self_attn.v_proj"`
-	OProj *nn.Linear  `weight:"self_attn.o_proj"`
-	QNorm *nn.RMSNorm `weight:"self_attn.q_norm"`
-	KNorm *nn.RMSNorm `weight:"self_attn.k_norm"`
-
-	// Precomputed (1 + weight) for Gemma-style RMSNorm
-	QNormScaled *mlx.Array `weight:"-"`
-	KNormScaled *mlx.Array `weight:"-"`
-}
-
-// MLP is the feed-forward network with GELU activation
-type MLP struct {
-	GateProj *nn.Linear `weight:"mlp.gate_proj"`
-	UpProj   *nn.Linear `weight:"mlp.up_proj"`
-	DownProj *nn.Linear `weight:"mlp.down_proj"`
-}
-
-// LoadText loads the text-only Gemma 3 model
-func LoadText(modelPath string) (*TextModel, error) {
-	data, err := os.ReadFile(filepath.Join(modelPath, "config.json"))
-	if err != nil {
-		return nil, fmt.Errorf("load config: %w", err)
-	}
-	var cfg TextConfig
-	if err := json.Unmarshal(data, &cfg); err != nil {
-		return nil, fmt.Errorf("parse config: %w", err)
-	}
-
-	// Compute scale
-	cfg.Scale = float32(1.0 / math.Sqrt(float64(cfg.HeadDim)))
-
-	// Set defaults if not specified
-	if cfg.RopeTheta == 0 {
-		cfg.RopeTheta = 1000000
-	}
-	if cfg.RopeLocalBaseFreq == 0 {
-		cfg.RopeLocalBaseFreq = 10000
-	}
-	if cfg.RMSNormEps == 0 {
-		cfg.RMSNormEps = 1e-6
-	}
-
-	weights, err := safetensors.LoadModelWeights(modelPath)
-	if err != nil {
-		return nil, fmt.Errorf("load weights: %w", err)
-	}
-
-	tok, err := tokenizer.Load(filepath.Join(modelPath, "tokenizer.json"))
-	if err != nil {
-		return nil, fmt.Errorf("load tokenizer: %w", err)
-	}
-
-	m := &TextModel{
-		Layers:     make([]*DecoderLayer, cfg.NumHiddenLayers),
-		TextConfig: &cfg,
-		tok:        tok,
-	}
-
-	// Initialize layer metadata
-	for i := range m.Layers {
-		m.Layers[i] = &DecoderLayer{
-			LayerIdx:  int32(i),
-			IsSliding: isLayerSliding(int32(i), cfg.SlidingWindowPattern),
-		}
-	}
-
-	if err := safetensors.LoadModule(m, weights, ""); err != nil {
-		return nil, err
-	}
-
-	// Tied embeddings for output
-	m.Output = nn.NewLinear(m.EmbedTokens.Weight, nil)
-
-	mlx.Eval(mlx.Collect(m)...)
-	weights.ReleaseAll()
-
-	// Precompute (1 + weight) for Gemma-style RMSNorm to avoid per-forward allocation
-	precomputeGemmaScaledWeights(m)
-
-	return m, nil
-}
-
-// precomputeGemmaScaledWeights computes (1 + weight) for all RMSNorm layers
-// This avoids creating temporary arrays on every forward pass
-func precomputeGemmaScaledWeights(m *TextModel) {
-	m.NormScaled = mlx.AddScalar(m.Norm.Weight, 1.0)
-
-	for _, layer := range m.Layers {
-		layer.InputNormScaled = mlx.AddScalar(layer.InputNorm.Weight, 1.0)
-		layer.PostAttnNormScaled = mlx.AddScalar(layer.PostAttnNorm.Weight, 1.0)
-		layer.PreFFNormScaled = mlx.AddScalar(layer.PreFFNorm.Weight, 1.0)
-		layer.PostFFNormScaled = mlx.AddScalar(layer.PostFFNorm.Weight, 1.0)
-
-		layer.Attention.QNormScaled = mlx.AddScalar(layer.Attention.QNorm.Weight, 1.0)
-		layer.Attention.KNormScaled = mlx.AddScalar(layer.Attention.KNorm.Weight, 1.0)
-	}
-
-	// Eval all the precomputed weights
-	var scaled []*mlx.Array
-	scaled = append(scaled, m.NormScaled)
-	for _, layer := range m.Layers {
-		scaled = append(scaled, layer.InputNormScaled, layer.PostAttnNormScaled,
-			layer.PreFFNormScaled, layer.PostFFNormScaled,
-			layer.Attention.QNormScaled, layer.Attention.KNormScaled)
-	}
-	mlx.Eval(scaled...)
-}
-
-// isLayerSliding determines if a layer uses sliding window attention
-// Pattern N means: layers 0 to N-1 sliding, N full, N+1 to 2N-1 sliding, 2N full, etc.
-func isLayerSliding(layerIdx, pattern int32) bool {
-	if pattern <= 0 {
-		return false // No sliding window
-	}
-	// Layer is full attention if (layerIdx + 1) % pattern == 0
-	return (layerIdx+1)%pattern != 0
-}
-
-// Forward runs the text model forward pass
-func (m *TextModel) Forward(tokens *mlx.Array, caches []cache.Cache) *mlx.Array {
-	B, L := tokens.Shape()[0], tokens.Shape()[1]
-
-	// Get embeddings and scale by sqrt(hidden_size)
-	h := m.EmbedTokens.Forward(tokens)
-	h = mlx.MulScalar(h, float32(math.Sqrt(float64(m.HiddenSize))))
-
-	for i, layer := range m.Layers {
-		h = layer.Forward(h, caches[i], B, L, m.TextConfig)
-	}
-
-	// Final norm and output projection
-	return m.Output.Forward(mlx.RMSNorm(h, m.NormScaled, m.RMSNormEps))
-}
-
-// Forward runs a decoder layer
-func (l *DecoderLayer) Forward(x *mlx.Array, c cache.Cache, B, L int32, cfg *TextConfig) *mlx.Array {
-	// Pre-attention norm (use precomputed scaled weight)
-	normed := mlx.RMSNorm(x, l.InputNormScaled, cfg.RMSNormEps)
-
-	// Attention
-	attnOut := l.Attention.Forward(normed, c, B, L, l.IsSliding, cfg)
-
-	// Post-attention norm and residual
-	attnOut = mlx.RMSNorm(attnOut, l.PostAttnNormScaled, cfg.RMSNormEps)
-	h := mlx.Add(x, attnOut)
-
-	// Pre-FFN norm
-	normed = mlx.RMSNorm(h, l.PreFFNormScaled, cfg.RMSNormEps)
-
-	// MLP
-	mlpOut := l.MLP.Forward(normed)
-
-	// Post-FFN norm and residual
-	mlpOut = mlx.RMSNorm(mlpOut, l.PostFFNormScaled, cfg.RMSNormEps)
-	return mlx.Add(h, mlpOut)
-}
-
-// Forward runs attention with Q/K normalization
-func (a *Attention) Forward(x *mlx.Array, c cache.Cache, B, L int32, isSliding bool, cfg *TextConfig) *mlx.Array {
-	q := a.QProj.Forward(x)
-	k := a.KProj.Forward(x)
-	v := a.VProj.Forward(x)
-
-	// Reshape to [B, num_heads, L, head_dim]
-	q = mlx.AsStrided(q, []int32{B, cfg.NumAttentionHeads, L, cfg.HeadDim},
-		[]int64{int64(L * cfg.NumAttentionHeads * cfg.HeadDim), int64(cfg.HeadDim), int64(cfg.NumAttentionHeads * cfg.HeadDim), 1}, 0)
-	k = mlx.AsStrided(k, []int32{B, cfg.NumKeyValueHeads, L, cfg.HeadDim},
-		[]int64{int64(L * cfg.NumKeyValueHeads * cfg.HeadDim), int64(cfg.HeadDim), int64(cfg.NumKeyValueHeads * cfg.HeadDim), 1}, 0)
-	v = mlx.AsStrided(v, []int32{B, cfg.NumKeyValueHeads, L, cfg.HeadDim},
-		[]int64{int64(L * cfg.NumKeyValueHeads * cfg.HeadDim), int64(cfg.HeadDim), int64(cfg.NumKeyValueHeads * cfg.HeadDim), 1}, 0)
-
-	// Q/K normalization after reshaping (use precomputed scaled weight)
-	q = mlx.RMSNorm(q, a.QNormScaled, cfg.RMSNormEps)
-	k = mlx.RMSNorm(k, a.KNormScaled, cfg.RMSNormEps)
-
-	// Apply RoPE with appropriate theta
-	ropeTheta := cfg.RopeTheta
-	if isSliding {
-		ropeTheta = cfg.RopeLocalBaseFreq
-	}
-	q = mlx.RoPE(q, int(cfg.HeadDim), false, ropeTheta, 1.0, c.Offset())
-	k = mlx.RoPE(k, int(cfg.HeadDim), false, ropeTheta, 1.0, c.Offset())
-
-	// Update cache
-	k, v = c.Update(k, v, int(L))
-
-	// Repeat K/V for GQA if needed
-	repeatFactor := cfg.NumAttentionHeads / cfg.NumKeyValueHeads
-	if repeatFactor > 1 {
-		k = nn.RepeatKV(k, repeatFactor)
-		v = nn.RepeatKV(v, repeatFactor)
-	}
-
-	// Attention
-	out := mlx.ScaledDotProductAttention(q, k, v, cfg.Scale, L > 1)
-	out = mlx.Reshape(mlx.Transpose(out, 0, 2, 1, 3), B, L, cfg.NumAttentionHeads*cfg.HeadDim)
-	return a.OProj.Forward(out)
-}
-
-// compiledGeluApprox is a singleton compiled GELU function shared across all layers
-var compiledGeluApprox *mlx.CompiledFunc
-
-// getCompiledGeluApprox returns the compiled GELU function, creating it once if needed
-func getCompiledGeluApprox() *mlx.CompiledFunc {
-	if compiledGeluApprox == nil {
-		compiledGeluApprox = mlx.CompileShapeless(func(inputs []*mlx.Array) []*mlx.Array {
-			return []*mlx.Array{geluApproxImpl(inputs[0])}
-		}, true)
-	}
-	return compiledGeluApprox
-}
-
-// Forward runs the MLP with GELU approximation (tanh variant)
-func (m *MLP) Forward(x *mlx.Array) *mlx.Array {
-	gate := getCompiledGeluApprox().Call(m.GateProj.Forward(x))[0]
-	return m.DownProj.Forward(mlx.Mul(gate, m.UpProj.Forward(x)))
-}
-
-// geluApproxImpl computes GELU using the tanh approximation (gelu_pytorch_tanh):
-// 0.5 * x * (1 + tanh(sqrt(2/pi) * (x + 0.044715 * x^3)))
-func geluApproxImpl(x *mlx.Array) *mlx.Array {
-	// Constants
-	const sqrt2OverPi = 0.7978845608028654 // sqrt(2/pi)
-	const coeff = 0.044715
-
-	// x^3
-	x3 := mlx.Mul(mlx.Mul(x, x), x)
-	// x + 0.044715 * x^3
-	inner := mlx.Add(x, mlx.MulScalar(x3, coeff))
-	// sqrt(2/pi) * (x + 0.044715 * x^3)
-	scaled := mlx.MulScalar(inner, sqrt2OverPi)
-	// tanh(...)
-	tanh := mlx.Tanh(scaled)
-	// 1 + tanh(...)
-	onePlusTanh := mlx.AddScalar(tanh, 1.0)
-	// 0.5 * x * (1 + tanh(...))
-	return mlx.Mul(mlx.MulScalar(x, 0.5), onePlusTanh)
-}
-
-// gemmaRMSNorm applies Gemma-style RMS normalization: x * rsqrt(mean(x^2) + eps) * (1 + weight)
-// Uses mlx.RMSNorm fast kernel with pre-computed (1 + weight)
-func gemmaRMSNorm(x, weight *mlx.Array, eps float32) *mlx.Array {
-	// Gemma uses (1 + weight) instead of weight
-	scaledWeight := mlx.AddScalar(weight, 1.0)
-	return mlx.RMSNorm(x, scaledWeight, eps)
-}
-
-// Interface methods
-func (m *TextModel) NumLayers() int          { return len(m.Layers) }
-func (m *TextModel) MaxContextLength() int32 { return m.MaxPositionEmbeddings }
-func (m *TextModel) VocabSize() int32        { return m.TextConfig.VocabSize }
-
-// Tokenizer returns the tokenizer wrapped to add BOS and apply chat template
-func (m *TextModel) Tokenizer() *tokenizer.Tokenizer {
-	return m.tok
-}
-
-// FormatPrompt applies the Gemma 3 chat template to a prompt
-func (m *TextModel) FormatPrompt(prompt string) string {
-	// Gemma 3 chat format: <start_of_turn>user\n{prompt}<end_of_turn>\n<start_of_turn>model\n
-	return fmt.Sprintf("<start_of_turn>user\n%s<end_of_turn>\n<start_of_turn>model\n", prompt)
-}
-
-func (m *TextModel) NewCache(maxSeqLen int32) []cache.Cache {
-	caches := make([]cache.Cache, len(m.Layers))
-	for i := range caches {
-		if m.Layers[i].IsSliding {
-			// Use rotating cache for sliding window layers
-			caches[i] = cache.NewRotatingKVCache(int(m.SlidingWindow))
-		} else {
-			// Use regular cache for global attention layers
-			caches[i] = cache.NewKVCache()
-		}
-	}
-	return caches
-}
-
-// Config holds config for the full multimodal model
-type Config struct {
-	TextConfig   TextConfig   `json:"text_config"`
-	VisionConfig VisionConfig `json:"vision_config"`
-
-	// Image token config (from config.json)
-	BOITokenIndex     int32 `json:"boi_token_index"`     // <start_of_image> = 255999
-	EOITokenIndex     int32 `json:"eoi_token_index"`     // <end_of_image> = 256000
-	ImageTokenIndex   int32 `json:"image_token_index"`   // <image_soft_token> = 262144
-	MMTokensPerImage  int32 `json:"mm_tokens_per_image"` // 256
-}
-
-// Model is the full Gemma 3 multimodal model
-type Model struct {
-	VisionTower *VisionTower         `weight:"vision_tower"`
-	Projector   *MultiModalProjector `weight:"multi_modal_projector"`
-	TextModel   *TextModel           `weight:"language_model"`
-	Config      *Config
-	tok         *tokenizer.Tokenizer
-}
-
-// Load loads the full multimodal Gemma 3 model
-func Load(modelPath string) (*Model, error) {
-	data, err := os.ReadFile(filepath.Join(modelPath, "config.json"))
-	if err != nil {
-		return nil, fmt.Errorf("load config: %w", err)
-	}
-
-	var cfg Config
-	if err := json.Unmarshal(data, &cfg); err != nil {
-		return nil, fmt.Errorf("parse config: %w", err)
-	}
-
-	// Set defaults for text config (multimodal config often has incomplete text_config)
-	// These defaults match transformers.Gemma3TextConfig defaults
-	tc := &cfg.TextConfig
-	if tc.HeadDim == 0 {
-		tc.HeadDim = 256 // Gemma 3 uses head_dim=256
-	}
-	if tc.NumAttentionHeads == 0 {
-		// Gemma 3 4B uses 8 attention heads (cannot infer from hidden_size/head_dim)
-		tc.NumAttentionHeads = 8
-	}
-	if tc.NumKeyValueHeads == 0 {
-		// Gemma 3 4B uses 4 KV heads (GQA with 2:1 ratio)
-		tc.NumKeyValueHeads = 4
-	}
-	if tc.VocabSize == 0 {
-		tc.VocabSize = 262208 // Gemma 3 vocab size (not 262144!)
-	}
-	if tc.RopeTheta == 0 {
-		tc.RopeTheta = 1000000
-	}
-	if tc.RopeLocalBaseFreq == 0 {
-		tc.RopeLocalBaseFreq = 10000
-	}
-	if tc.RMSNormEps == 0 {
-		tc.RMSNormEps = 1e-6
-	}
-	if tc.SlidingWindowPattern == 0 {
-		tc.SlidingWindowPattern = 6
-	}
-	if tc.MaxPositionEmbeddings == 0 {
-		tc.MaxPositionEmbeddings = 131072 // Gemma 3 4B default
-	}
-
-	// Compute text model scale
-	tc.Scale = float32(1.0 / math.Sqrt(float64(tc.HeadDim)))
-
-	// Set defaults for image token config
-	if cfg.BOITokenIndex == 0 {
-		cfg.BOITokenIndex = 255999 // <start_of_image>
-	}
-	if cfg.EOITokenIndex == 0 {
-		cfg.EOITokenIndex = 256000 // <end_of_image>
-	}
-	if cfg.ImageTokenIndex == 0 {
-		cfg.ImageTokenIndex = 262144 // <image_soft_token>
-	}
-	if cfg.MMTokensPerImage == 0 {
-		cfg.MMTokensPerImage = 256
-	}
-
-	weights, err := safetensors.LoadModelWeights(modelPath)
-	if err != nil {
-		return nil, fmt.Errorf("load weights: %w", err)
-	}
-
-	tok, err := tokenizer.Load(filepath.Join(modelPath, "tokenizer.json"))
-	if err != nil {
-		return nil, fmt.Errorf("load tokenizer: %w", err)
-	}
-
-	m := &Model{
-		VisionTower: &VisionTower{
-			Embeddings: &VisionEmbeddings{},
-			Encoder:    make([]*VisionEncoderLayer, cfg.VisionConfig.NumHiddenLayers),
-			Config:     &cfg.VisionConfig,
-		},
-		Projector: &MultiModalProjector{},
-		TextModel: &TextModel{
-			Layers:     make([]*DecoderLayer, cfg.TextConfig.NumHiddenLayers),
-			TextConfig: &cfg.TextConfig,
-		},
-		Config: &cfg,
-		tok:    tok,
-	}
-
-	// Initialize text layer metadata
-	for i := range m.TextModel.Layers {
-		m.TextModel.Layers[i] = &DecoderLayer{
-			LayerIdx:  int32(i),
-			IsSliding: isLayerSliding(int32(i), cfg.TextConfig.SlidingWindowPattern),
-		}
-	}
-
-	// Initialize vision encoder layers
-	for i := range m.VisionTower.Encoder {
-		m.VisionTower.Encoder[i] = &VisionEncoderLayer{}
-	}
-
-	if err := safetensors.LoadModule(m, weights, ""); err != nil {
-		return nil, err
-	}
-
-	// Tied embeddings for text output
-	m.TextModel.Output = nn.NewLinear(m.TextModel.EmbedTokens.Weight, nil)
-	m.TextModel.tok = tok
-
-	mlx.Eval(mlx.Collect(m)...)
-	weights.ReleaseAll()
-
-	// Precompute (1 + weight) for Gemma-style RMSNorm
-	precomputeGemmaScaledWeights(m.TextModel)
-
-	// Precompute projector's scaled weight
-	m.Projector.SoftEmbNormScaled = mlx.AddScalar(m.Projector.SoftEmbNorm.Weight, 1.0)
-	mlx.Eval(m.Projector.SoftEmbNormScaled)
-
-	return m, nil
-}
-
-// Forward runs the text-only forward pass
-func (m *Model) Forward(tokens *mlx.Array, caches []cache.Cache) *mlx.Array {
-	return m.TextModel.Forward(tokens, caches)
-}
-
-// ForwardWithImage runs the multimodal forward pass
-// tokens: [B, L] input token IDs (with image placeholder tokens)
-// image: [B, H, W, C] preprocessed image tensor
-func (m *Model) ForwardWithImage(tokens *mlx.Array, image *mlx.Array, caches []cache.Cache) *mlx.Array {
-	B, L := tokens.Shape()[0], tokens.Shape()[1]
-	cfg := m.Config.TextConfig
-
-	// Find image token position FIRST before any eval that might free tokens
-	imageStartPos := int32(-1)
-	if image != nil && B == 1 {
-		tokenData := tokens.DataInt32() // This evals tokens
-		for i, t := range tokenData {
-			if t == m.Config.ImageTokenIndex {
-				imageStartPos = int32(i)
-				break
-			}
-		}
-	}
-
-	// Get text embeddings and scale
-	h := m.TextModel.EmbedTokens.Forward(tokens)
-	h = mlx.MulScalar(h, float32(math.Sqrt(float64(cfg.HiddenSize))))
-
-	// Process image if provided
-	if image != nil && imageStartPos >= 0 {
-		// Vision tower: [B, H, W, C] -> [B, num_patches, vision_hidden]
-		visionFeatures := m.VisionTower.Forward(image)
-
-		// Project to text space: [B, num_patches, vision_hidden] -> [B, 256, text_hidden]
-		imageEmbeds := m.Projector.Forward(visionFeatures, cfg.RMSNormEps)
-
-		// Eval h and imageEmbeds together so neither gets freed
-		mlx.Eval(h, imageEmbeds)
-
-		// Cast imageEmbeds to match text embeddings dtype (bf16)
-		if imageEmbeds.Dtype() != h.Dtype() {
-			imageEmbeds = mlx.AsType(imageEmbeds, h.Dtype())
-			mlx.Eval(imageEmbeds)
-		}
-
-		// Insert image embeddings at the known position
-		h = m.insertImageEmbeddingsAt(h, imageEmbeds, imageStartPos)
-	}
-
-	// Run through text model layers
-	for i, layer := range m.TextModel.Layers {
-		h = layer.Forward(h, caches[i], B, L, m.TextModel.TextConfig)
-	}
-
-	// Final norm and output projection
-	return m.TextModel.Output.Forward(mlx.RMSNorm(h, m.TextModel.NormScaled, cfg.RMSNormEps))
-}
-
-// insertImageEmbeddingsAt replaces image placeholder tokens with actual image embeddings
-// at a known position (to avoid re-scanning tokens after eval)
-// textEmbeds: [B, L, hidden_size] text embeddings
-// imageEmbeds: [B, 256, hidden_size] image embeddings from projector
-// startPos: starting position of image tokens in the sequence
-func (m *Model) insertImageEmbeddingsAt(textEmbeds, imageEmbeds *mlx.Array, startPos int32) *mlx.Array {
-	numImageTokens := imageEmbeds.Shape()[1]
-	L := textEmbeds.Shape()[1]
-
-	// Split text embeddings: [0:startPos] + imageEmbeds + [startPos+256:L]
-	afterStart := startPos + numImageTokens
-
-	// Slice before image tokens: textEmbeds[:, 0:startPos, :]
-	before := mlx.SliceAxis(textEmbeds, 1, 0, startPos)
-
-	// Slice after image tokens: textEmbeds[:, startPos+256:L, :]
-	after := mlx.SliceAxis(textEmbeds, 1, afterStart, L)
-
-	// Concatenate: before + imageEmbeds + after along axis 1
-	return mlx.Concatenate([]*mlx.Array{before, imageEmbeds, after}, 1)
-}
-
-// Interface methods for Model
-func (m *Model) NumLayers() int                         { return len(m.TextModel.Layers) }
-func (m *Model) MaxContextLength() int32                { return m.Config.TextConfig.MaxPositionEmbeddings }
-func (m *Model) VocabSize() int32                       { return m.Config.TextConfig.VocabSize }
-func (m *Model) Tokenizer() *tokenizer.Tokenizer     { return m.tok }
-func (m *Model) NewCache(maxSeqLen int32) []cache.Cache { return m.TextModel.NewCache(maxSeqLen) }
-func (m *Model) ImageSize() int32                       { return m.Config.VisionConfig.ImageSize }
-
-// FormatPrompt applies the Gemma 3 multimodal chat template
-func (m *Model) FormatPrompt(prompt string) string {
-	return fmt.Sprintf("<start_of_turn>user\n%s<end_of_turn>\n<start_of_turn>model\n", prompt)
-}
-
-// FormatPromptWithImage applies the Gemma 3 multimodal chat template with image
-func (m *Model) FormatPromptWithImage(prompt string) string {
-	return fmt.Sprintf("<start_of_turn>user\n<start_of_image>%s<end_of_turn>\n<start_of_turn>model\n", prompt)
-}
-
-// ExpandImageTokens expands <start_of_image> into 256 image placeholder tokens
-// Input tokens containing boi_token (255999) are expanded to:
-// boi_token + 256 * image_token + eoi_token
-func (m *Model) ExpandImageTokens(tokens []int32) []int32 {
-	result := make([]int32, 0, len(tokens)+int(m.Config.MMTokensPerImage)+1)
-
-	for _, t := range tokens {
-		if t == m.Config.BOITokenIndex {
-			// Expand: boi + 256 * image_token + eoi
-			result = append(result, m.Config.BOITokenIndex)
-			for i := int32(0); i < m.Config.MMTokensPerImage; i++ {
-				result = append(result, m.Config.ImageTokenIndex)
-			}
-			result = append(result, m.Config.EOITokenIndex)
-		} else {
-			result = append(result, t)
-		}
-	}
-
-	return result
-}

x/imagegen/models/gemma3/image.go

@@ -1,58 +0,0 @@
-//go:build mlx
-
-package gemma3
-
-import (
-	"fmt"
-	"image"
-	_ "image/jpeg"
-	_ "image/png"
-	"os"
-
-	"github.com/ollama/ollama/x/imagegen/mlx"
-	"golang.org/x/image/draw"
-)
-
-// ProcessImage loads and preprocesses an image for the vision tower
-// Returns [1, H, W, C] tensor in NHWC format normalized for SigLIP
-func ProcessImage(path string, imageSize int32) (*mlx.Array, error) {
-	f, err := os.Open(path)
-	if err != nil {
-		return nil, fmt.Errorf("open image: %w", err)
-	}
-	defer f.Close()
-
-	img, _, err := image.Decode(f)
-	if err != nil {
-		return nil, fmt.Errorf("decode image: %w", err)
-	}
-
-	return ProcessImageData(img, imageSize)
-}
-
-// ProcessImageData preprocesses an image.Image for the vision tower
-func ProcessImageData(img image.Image, imageSize int32) (*mlx.Array, error) {
-	// Resize to target size using bilinear interpolation
-	resized := image.NewRGBA(image.Rect(0, 0, int(imageSize), int(imageSize)))
-	draw.BiLinear.Scale(resized, resized.Bounds(), img, img.Bounds(), draw.Over, nil)
-
-	// Convert to float32 array [H, W, C] and normalize
-	// SigLIP normalization: (pixel / 255.0 - 0.5) / 0.5 = pixel / 127.5 - 1.0
-	data := make([]float32, imageSize*imageSize*3)
-	idx := 0
-	for y := int32(0); y < imageSize; y++ {
-		for x := int32(0); x < imageSize; x++ {
-			r, g, b, _ := resized.At(int(x), int(y)).RGBA()
-			// RGBA returns 16-bit values, convert to 8-bit
-			data[idx] = float32(r>>8)/127.5 - 1.0
-			data[idx+1] = float32(g>>8)/127.5 - 1.0
-			data[idx+2] = float32(b>>8)/127.5 - 1.0
-			idx += 3
-		}
-	}
-
-	// Create MLX array [1, H, W, C] for NHWC layout
-	arr := mlx.NewArrayFloat32(data, []int32{1, imageSize, imageSize, 3})
-	mlx.Eval(arr) // Materialize to prevent use-after-free
-	return arr, nil
-}

x/imagegen/models/gemma3/projector.go

@@ -1,50 +0,0 @@
-//go:build mlx
-
-package gemma3
-
-import (
-	"github.com/ollama/ollama/x/imagegen/mlx"
-	"github.com/ollama/ollama/x/imagegen/nn"
-)
-
-// MultiModalProjector projects vision features to text embedding space
-type MultiModalProjector struct {
-	// mm_input_projection_weight: [vision_hidden, text_hidden]
-	InputProjection *mlx.Array  `weight:"mm_input_projection_weight"`
-	SoftEmbNorm     *nn.RMSNorm `weight:"mm_soft_emb_norm"`
-
-	// Precomputed (1 + weight) for Gemma-style RMSNorm
-	SoftEmbNormScaled *mlx.Array `weight:"-"`
-}
-
-// Forward projects vision features to text space
-// Input: [B, num_patches, vision_hidden] (e.g., [1, 4096, 1152])
-// Output: [B, num_image_tokens, text_hidden] (e.g., [1, 256, 2560])
-func (p *MultiModalProjector) Forward(visionFeatures *mlx.Array, eps float32) *mlx.Array {
-	// Average pool 4x4: [B, 4096, 1152] -> [B, 256, 1152]
-	// 4096 patches = 64x64 grid, pool to 16x16 = 256 tokens
-	B := visionFeatures.Shape()[0]
-	visionHidden := visionFeatures.Shape()[2]
-
-	// Reshape to [B, 64, 64, hidden]
-	gridSize := int32(64) // sqrt(4096)
-	pooledSize := int32(16) // 64/4
-	h := mlx.Reshape(visionFeatures, B, gridSize, gridSize, visionHidden)
-
-	// Reshape to [B, 16, 4, 16, 4, hidden] for 4x4 pooling
-	h = mlx.Reshape(h, B, pooledSize, 4, pooledSize, 4, visionHidden)
-
-	// Average over pooling dimensions (axes 2 and 4)
-	h = mlx.Mean(h, 4, false)
-	h = mlx.Mean(h, 2, false)
-
-	// h is now [B, 16, 16, hidden], reshape to [B, 256, hidden]
-	numTokens := pooledSize * pooledSize
-	h = mlx.Reshape(h, B, numTokens, visionHidden)
-
-	// Apply Gemma-style RMS norm (use precomputed 1 + weight)
-	h = mlx.RMSNorm(h, p.SoftEmbNormScaled, eps)
-
-	// Project to text space: [B, 256, vision_hidden] @ [vision_hidden, text_hidden]
-	return mlx.Linear(h, p.InputProjection)
-}

x/imagegen/models/gemma3/vision.go

@@ -1,138 +0,0 @@
-//go:build mlx
-
-package gemma3
-
-import (
-	"math"
-
-	"github.com/ollama/ollama/x/imagegen/mlx"
-	"github.com/ollama/ollama/x/imagegen/nn"
-)
-
-// VisionConfig holds configuration for the SigLIP vision tower
-type VisionConfig struct {
-	HiddenSize        int32 `json:"hidden_size"`
-	ImageSize         int32 `json:"image_size"`
-	IntermediateSize  int32 `json:"intermediate_size"`
-	NumAttentionHeads int32 `json:"num_attention_heads"`
-	NumHiddenLayers   int32 `json:"num_hidden_layers"`
-	PatchSize         int32 `json:"patch_size"`
-}
-
-// VisionTower is the SigLIP vision encoder
-type VisionTower struct {
-	Embeddings    *VisionEmbeddings     `weight:"vision_model.embeddings"`
-	Encoder       []*VisionEncoderLayer `weight:"vision_model.encoder.layers"`
-	PostLayerNorm *nn.LayerNorm         `weight:"vision_model.post_layernorm"`
-	Config        *VisionConfig
-}
-
-// VisionEmbeddings handles patch and position embeddings
-type VisionEmbeddings struct {
-	// PatchWeight: [O, C, kH, kW] from PyTorch, transposed to [O, kH, kW, C] for MLX
-	PatchWeight *mlx.Array    `weight:"patch_embedding.weight"`
-	PatchBias   *mlx.Array    `weight:"patch_embedding.bias"`
-	PosEmbed    *nn.Embedding `weight:"position_embedding"`
-}
-
-// VisionEncoderLayer is a single transformer encoder layer
-type VisionEncoderLayer struct {
-	LayerNorm1 *nn.LayerNorm     `weight:"layer_norm1"`
-	Attention  *VisionAttention  `weight:"self_attn"`
-	LayerNorm2 *nn.LayerNorm     `weight:"layer_norm2"`
-	MLP        *VisionMLP        `weight:"mlp"`
-}
-
-// VisionAttention implements multi-head self-attention
-type VisionAttention struct {
-	QProj   *nn.Linear `weight:"q_proj"`
-	KProj   *nn.Linear `weight:"k_proj"`
-	VProj   *nn.Linear `weight:"v_proj"`
-	OutProj *nn.Linear `weight:"out_proj"`
-}
-
-// VisionMLP is the feed-forward network
-type VisionMLP struct {
-	FC1 *nn.Linear `weight:"fc1"`
-	FC2 *nn.Linear `weight:"fc2"`
-}
-
-// Forward runs the vision tower on preprocessed images
-// Input: [B, H, W, C] normalized image tensor (NHWC layout for MLX)
-// Output: [B, num_patches, hidden_size]
-func (v *VisionTower) Forward(x *mlx.Array) *mlx.Array {
-	// Patch embedding conv: input [B, H, W, C], weight [O, kH, kW, C] -> [B, grid, grid, O]
-	// Weight comes as [O, C, kH, kW] from PyTorch, transpose to [O, kH, kW, C]
-	weight := mlx.Transpose(v.Embeddings.PatchWeight, 0, 2, 3, 1)
-	h := mlx.Conv2d(x, weight, v.Config.PatchSize, 0) // stride=patch_size, no padding
-
-	// Add bias: [O] -> [1, 1, 1, O] for broadcasting
-	bias := mlx.Reshape(v.Embeddings.PatchBias, 1, 1, 1, v.Embeddings.PatchBias.Shape()[0])
-	h = mlx.Add(h, bias)
-
-	// h is [B, grid, grid, hidden], flatten to [B, num_patches, hidden]
-	B := h.Shape()[0]
-	gridH, gridW := h.Shape()[1], h.Shape()[2]
-	hidden := h.Shape()[3]
-	numPatches := gridH * gridW
-	h = mlx.Reshape(h, B, numPatches, hidden)
-
-	// Add position embeddings
-	posIds := mlx.ArangeInt(0, numPatches, 1, mlx.DtypeInt32)
-	posEmbed := v.Embeddings.PosEmbed.Forward(posIds)
-	h = mlx.Add(h, posEmbed)
-
-	// Encoder layers
-	headDim := float32(v.Config.HiddenSize / v.Config.NumAttentionHeads)
-	scale := float32(1.0 / math.Sqrt(float64(headDim)))
-	for _, layer := range v.Encoder {
-		h = layer.Forward(h, v.Config, scale)
-	}
-
-	// Final layer norm
-	h = v.PostLayerNorm.Forward(h)
-
-	return h
-}
-
-// Forward runs a vision encoder layer
-func (l *VisionEncoderLayer) Forward(x *mlx.Array, cfg *VisionConfig, scale float32) *mlx.Array {
-	// Pre-norm attention
-	h := l.LayerNorm1.Forward(x)
-	h = l.Attention.Forward(h, cfg, scale)
-	x = mlx.Add(x, h)
-
-	// Pre-norm MLP
-	h = l.LayerNorm2.Forward(x)
-	h = l.MLP.Forward(h)
-	return mlx.Add(x, h)
-}
-
-// Forward runs multi-head self-attention
-func (a *VisionAttention) Forward(x *mlx.Array, cfg *VisionConfig, scale float32) *mlx.Array {
-	B, L := x.Shape()[0], x.Shape()[1]
-	headDim := cfg.HiddenSize / cfg.NumAttentionHeads
-
-	q := a.QProj.Forward(x)
-	k := a.KProj.Forward(x)
-	v := a.VProj.Forward(x)
-
-	// Reshape to [B, num_heads, L, head_dim]
-	q = mlx.Transpose(mlx.Reshape(q, B, L, cfg.NumAttentionHeads, headDim), 0, 2, 1, 3)
-	k = mlx.Transpose(mlx.Reshape(k, B, L, cfg.NumAttentionHeads, headDim), 0, 2, 1, 3)
-	v = mlx.Transpose(mlx.Reshape(v, B, L, cfg.NumAttentionHeads, headDim), 0, 2, 1, 3)
-
-	// Scaled dot-product attention (no causal mask for vision)
-	out := mlx.ScaledDotProductAttention(q, k, v, scale, false)
-
-	// Reshape back: [B, num_heads, L, head_dim] -> [B, L, hidden]
-	out = mlx.Reshape(mlx.Transpose(out, 0, 2, 1, 3), B, L, cfg.HiddenSize)
-
-	return a.OutProj.Forward(out)
-}
-
-// Forward runs the MLP with GELU activation
-func (m *VisionMLP) Forward(x *mlx.Array) *mlx.Array {
-	h := mlx.GELU(m.FC1.Forward(x))
-	return m.FC2.Forward(h)
-}

x/imagegen/models/gpt_oss/gpt_oss.go

@@ -1,487 +0,0 @@
-//go:build mlx
-
-package gpt_oss
-
-import (
-	"encoding/json"
-	"fmt"
-	"math"
-	"os"
-	"path/filepath"
-
-	"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"
-	"github.com/ollama/ollama/x/imagegen/tokenizer"
-)
-
-// RopeScaling holds YaRN or other RoPE scaling configuration
-type RopeScaling struct {
-	RopeType                      string  `json:"rope_type"`
-	Factor                        float32 `json:"factor"`
-	OriginalMaxPositionEmbeddings int32   `json:"original_max_position_embeddings"`
-	BetaFast                      float32 `json:"beta_fast"`
-	BetaSlow                      float32 `json:"beta_slow"`
-}
-
-type Config 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"`
-	RopeTheta         float32      `json:"rope_theta"`
-	HeadDim           int32        `json:"head_dim"`
-	SlidingWindow     int32        `json:"sliding_window"`
-	NumLocalExperts   int32        `json:"num_local_experts"`
-	NumExpertsPerTok  int32        `json:"num_experts_per_tok"`
-	LayerTypes        []string     `json:"layer_types"`
-	SwiGLULimit       float32      `json:"swiglu_limit"`
-	RopeScaling       *RopeScaling `json:"rope_scaling"`
-	Scale             float32      `json:"-"` // computed: 1/sqrt(HeadDim)
-}
-
-type Attention struct {
-	QProj      *nn.Linear `weight:"self_attn.q_proj"`
-	KProj      *nn.Linear `weight:"self_attn.k_proj"`
-	VProj      *nn.Linear `weight:"self_attn.v_proj"`
-	OProj      *nn.Linear `weight:"self_attn.o_proj"`
-	Sinks      *mlx.Array `weight:"self_attn.sinks,optional"`
-	YarnFreqs  *mlx.Array // computed
-	YarnMscale float32
-}
-
-// swiGLU applies the GPT-OSS custom SwiGLU activation.
-// Formula: (gate * sigmoid(alpha * gate)) * (up + 1)
-// with clipping: gate to [None, limit], up to [-limit, limit]
-func swiGLU(gate, up *mlx.Array, alpha, limit float32) *mlx.Array {
-	// Clip gate to [None, limit]
-	gateClipped := mlx.ClipScalar(gate, 0, limit, false, true)
-
-	// Clip up to [-limit, limit]
-	upClipped := mlx.ClipScalar(up, -limit, limit, true, true)
-
-	// glu_scaled = alpha * gate_clipped
-	gluScaled := mlx.MulScalar(gateClipped, alpha)
-
-	// sig = sigmoid(glu_scaled)
-	sig := mlx.Sigmoid(gluScaled)
-
-	// out_glu = gate_clipped * sig
-	outGlu := mlx.Mul(gateClipped, sig)
-
-	// result = out_glu * (up_clipped + 1)
-	return mlx.Mul(outGlu, mlx.AddScalar(upClipped, 1.0))
-}
-
-// compiledSwiGLU is a singleton compiled SwiGLU function shared across all layers
-var compiledSwiGLU *mlx.CompiledFunc
-
-// getCompiledSwiGLU returns the compiled SwiGLU function, creating it once if needed
-func getCompiledSwiGLU() *mlx.CompiledFunc {
-	if compiledSwiGLU == nil {
-		const alpha float32 = 1.702
-		const limit float32 = 7.0
-		compiledSwiGLU = mlx.CompileShapeless(func(inputs []*mlx.Array) []*mlx.Array {
-			return []*mlx.Array{swiGLU(inputs[0], inputs[1], alpha, limit)}
-		}, true) // shapeless=true so it works for any input size
-	}
-	return compiledSwiGLU
-}
-
-// ComputeYarnFreqs computes YaRN-modified RoPE frequencies
-// Based on mlx-lm's YarnRoPE implementation
-func ComputeYarnFreqs(dims int32, base, scalingFactor float32, origMaxPos int32, betaFast, betaSlow float32) (*mlx.Array, float32) {
-	// yarn_find_correction_dim
-	yarnFindCorrectionDim := func(numRotations float64) float64 {
-		return float64(dims) * math.Log(float64(origMaxPos)/(numRotations*2*math.Pi)) / (2 * math.Log(float64(base)))
-	}
-
-	// yarn_find_correction_range
-	low := int(math.Floor(yarnFindCorrectionDim(float64(betaFast))))
-	high := int(math.Ceil(yarnFindCorrectionDim(float64(betaSlow))))
-	if low < 0 {
-		low = 0
-	}
-	if high > int(dims)-1 {
-		high = int(dims) - 1
-	}
-
-	// yarn_get_mscale
-	yarnGetMscale := func(scale, mscale float64) float64 {
-		if scale <= 1 {
-			return 1.0
-		}
-		return 0.1*mscale*math.Log(scale) + 1.0
-	}
-	mscale := float32(yarnGetMscale(float64(scalingFactor), 1.0) / yarnGetMscale(float64(scalingFactor), 0.0))
-
-	// Compute frequencies
-	// freq_extra = base ** (arange(0, dims, 2) / dims)
-	// freq_inter = scaling_factor * freq_extra
-	halfDims := dims / 2
-	freqData := make([]float32, halfDims)
-	for i := int32(0); i < halfDims; i++ {
-		exp := float64(2*i) / float64(dims)
-		freqExtra := math.Pow(float64(base), exp)
-		freqInter := float64(scalingFactor) * freqExtra
-
-		// linear ramp mask
-		var freqMask float64
-		if low == high {
-			freqMask = 0.0
-		} else {
-			t := (float64(i) - float64(low)) / float64(high-low)
-			if t < 0 {
-				t = 0
-			}
-			if t > 1 {
-				t = 1
-			}
-			freqMask = 1.0 - t
-		}
-
-		// Combined frequency: (inter * extra) / (inter * mask + extra * (1 - mask))
-		freqData[i] = float32((freqInter * freqExtra) / (freqInter*freqMask + freqExtra*(1-freqMask)))
-	}
-
-	return mlx.NewArray(freqData, []int32{halfDims}), mscale
-}
-
-// initYarn initializes YaRN RoPE if configured
-func (a *Attention) initYarn(cfg *Config) {
-	a.YarnMscale = 1.0
-	if cfg.RopeScaling != nil && cfg.RopeScaling.RopeType == "yarn" {
-		a.YarnFreqs, a.YarnMscale = ComputeYarnFreqs(
-			cfg.HeadDim,
-			cfg.RopeTheta,
-			cfg.RopeScaling.Factor,
-			cfg.RopeScaling.OriginalMaxPositionEmbeddings,
-			cfg.RopeScaling.BetaFast,
-			cfg.RopeScaling.BetaSlow,
-		)
-	}
-}
-
-func (a *Attention) Forward(x *mlx.Array, c cache.Cache, B, L int32, mask *mlx.Array, maskMode string, cfg *Config) *mlx.Array {
-	q := a.QProj.Forward(x)
-	k := a.KProj.Forward(x)
-	v := a.VProj.Forward(x)
-
-	// Reshape via AsStrided: [B, L, n_heads * head_dim] -> [B, n_heads, L, head_dim]
-	q = mlx.AsStrided(q, []int32{B, cfg.NumAttentionHeads, L, cfg.HeadDim},
-		[]int64{int64(L * cfg.NumAttentionHeads * cfg.HeadDim), int64(cfg.HeadDim), int64(cfg.NumAttentionHeads * cfg.HeadDim), 1}, 0)
-	k = mlx.AsStrided(k, []int32{B, cfg.NumKeyValueHeads, L, cfg.HeadDim},
-		[]int64{int64(L * cfg.NumKeyValueHeads * cfg.HeadDim), int64(cfg.HeadDim), int64(cfg.NumKeyValueHeads * cfg.HeadDim), 1}, 0)
-	v = mlx.AsStrided(v, []int32{B, cfg.NumKeyValueHeads, L, cfg.HeadDim},
-		[]int64{int64(L * cfg.NumKeyValueHeads * cfg.HeadDim), int64(cfg.HeadDim), int64(cfg.NumKeyValueHeads * cfg.HeadDim), 1}, 0)
-
-	offset := 0
-	if c != nil {
-		offset = c.Offset()
-	}
-	if a.YarnFreqs != nil {
-		if a.YarnMscale != 1.0 {
-			q = mlx.MulScalar(q, a.YarnMscale)
-		}
-		q = mlx.RoPEWithFreqs(q, a.YarnFreqs, int(cfg.HeadDim), false, 1.0, offset)
-		k = mlx.RoPEWithFreqs(k, a.YarnFreqs, int(cfg.HeadDim), false, 1.0, offset)
-	} else {
-		q = mlx.RoPE(q, int(cfg.HeadDim), false, cfg.RopeTheta, 1.0, offset)
-		k = mlx.RoPE(k, int(cfg.HeadDim), false, cfg.RopeTheta, 1.0, offset)
-	}
-
-	if c != nil {
-		k, v = c.Update(k, v, int(L))
-	}
-
-	out := mlx.ScaledDotProductAttentionWithSinks(q, k, v, cfg.Scale, maskMode, mask, a.Sinks)
-	out = mlx.Reshape(mlx.Transpose(out, 0, 2, 1, 3), B, L, cfg.NumAttentionHeads*cfg.HeadDim)
-	return a.OProj.Forward(out)
-}
-
-// CreateSlidingWindowMask creates a causal mask with sliding window
-// Mirrors mlx-lm's create_causal_mask with window_size
-func CreateSlidingWindowMask(seqLen, queryStart, keyStart, keyLen, windowSize int) *mlx.Array {
-	// Build mask aligned to actual cache length (may be rotated)
-	// rinds covers existing keys: [keyStart, keyStart+keyLen)
-	// linds covers new queries: [queryStart, queryStart+seqLen)
-	rinds := mlx.Arange(float32(keyStart), float32(keyStart+keyLen), 1)     // [keyLen]
-	linds := mlx.Arange(float32(queryStart), float32(queryStart+seqLen), 1) // [seqLen]
-
-	linds = mlx.ExpandDims(linds, 1) // [seqLen, 1]
-	rinds = mlx.ExpandDims(rinds, 0) // [1, keyLen]
-
-	causalMask := mlx.GreaterEqual(linds, rinds) // [seqLen, keyLen]
-	windowLimit := mlx.AddScalar(rinds, float32(windowSize))
-	windowMask := mlx.LessArray(linds, windowLimit) // [seqLen, keyLen]
-
-	return mlx.LogicalAnd(causalMask, windowMask)
-}
-
-// MoE represents the Mixture of Experts SwiGLU layer with quantized experts.
-type MoE struct {
-	Router     *nn.Linear `weight:"mlp.router"`
-	TopK       int32
-	HiddenSize int32
-	GroupSize  int
-	Bits       int
-	// Expert weights (loaded manually via sanitizeExpertWeights)
-	GateBlocks, GateScales, GateBias *mlx.Array
-	UpBlocks, UpScales, UpBias       *mlx.Array
-	DownBlocks, DownScales, DownBias *mlx.Array
-}
-
-func (moe *MoE) Forward(x *mlx.Array, B, L int32) *mlx.Array {
-	logits := moe.Router.Forward(x)
-	neg := mlx.Neg(logits)
-	part := mlx.Argpartition(neg, int(moe.TopK)-1, -1)
-	topKIdx := mlx.Slice(part, []int32{0, 0, 0}, []int32{B, L, moe.TopK})
-	topKVal := mlx.TakeAlongAxis(logits, topKIdx, -1)
-	weights := mlx.Softmax(topKVal, -1)
-
-	xFlat := mlx.Reshape(x, B*L, 1, 1, moe.HiddenSize)
-	idxFlat := mlx.Reshape(topKIdx, B*L, moe.TopK)
-
-	doSort := B*L >= 64
-	var invOrder *mlx.Array
-	sorted := false
-	n := B * L * moe.TopK
-
-	if doSort {
-		idxAll := mlx.Flatten(idxFlat)
-		order := mlx.Argsort(idxAll, 0)
-		invOrder = mlx.Argsort(order, 0)
-		xFlat = mlx.ExpandDims(mlx.Take(mlx.Squeeze(xFlat, 1), mlx.FloorDivideScalar(order, moe.TopK), 0), 1)
-		idxFlat = mlx.Reshape(mlx.Take(idxAll, order, 0), n, 1)
-		sorted = true
-	}
-
-	gate := mlx.GatherQMM(xFlat, moe.GateBlocks, moe.GateScales, nil, nil, idxFlat, true, moe.GroupSize, moe.Bits, "mxfp4", sorted)
-	up := mlx.GatherQMM(xFlat, moe.UpBlocks, moe.UpScales, nil, nil, idxFlat, true, moe.GroupSize, moe.Bits, "mxfp4", sorted)
-
-	if moe.GateBias != nil {
-		gate = mlx.Add(gate, mlx.ExpandDims(mlx.Take(moe.GateBias, idxFlat, 0), 2))
-	}
-	if moe.UpBias != nil {
-		up = mlx.Add(up, mlx.ExpandDims(mlx.Take(moe.UpBias, idxFlat, 0), 2))
-	}
-
-	hidden := getCompiledSwiGLU().Call(gate, up)[0]
-
-	down := mlx.GatherQMM(hidden, moe.DownBlocks, moe.DownScales, nil, nil, idxFlat, true, moe.GroupSize, moe.Bits, "mxfp4", sorted)
-	if moe.DownBias != nil {
-		down = mlx.Add(down, mlx.ExpandDims(mlx.Take(moe.DownBias, idxFlat, 0), 2))
-	}
-
-	if doSort {
-		down = mlx.Reshape(mlx.Take(mlx.Squeeze(mlx.Squeeze(down, 2), 1), invOrder, 0), B*L, moe.TopK, moe.HiddenSize)
-	} else {
-		down = mlx.Squeeze(down, 2)
-	}
-
-	ewFlat := mlx.Reshape(weights, B*L, moe.TopK, 1)
-	return mlx.Reshape(mlx.Sum(mlx.Mul(down, ewFlat), 1, false), B, L, moe.HiddenSize)
-}
-
-type Block struct {
-	Attention    *Attention
-	MLP          *MoE
-	InputNorm    *nn.RMSNorm `weight:"input_layernorm"`
-	PostAttnNorm *nn.RMSNorm `weight:"post_attention_layernorm"`
-	LayerType    string      // "sliding_attention" or "full_attention"
-}
-
-func (b *Block) Forward(x *mlx.Array, c cache.Cache, B, L int32, mask *mlx.Array, maskMode string, cfg *Config) *mlx.Array {
-	h := mlx.Add(x, b.Attention.Forward(b.InputNorm.Forward(x, cfg.RMSNormEps), c, B, L, mask, maskMode, cfg))
-	return mlx.Add(h, b.MLP.Forward(b.PostAttnNorm.Forward(h, cfg.RMSNormEps), B, L))
-}
-
-type Model struct {
-	EmbedTokens *nn.Embedding `weight:"model.embed_tokens"`
-	Layers      []*Block      `weight:"-"` // loaded manually due to MoE sanitization
-	Norm        *nn.RMSNorm   `weight:"model.norm"`
-	LMHead      *nn.Linear    `weight:"lm_head"`
-
-	tok *tokenizer.Tokenizer
-	*Config
-}
-
-func (m *Model) Tokenizer() *tokenizer.Tokenizer { return m.tok }
-func (m *Model) NumLayers() int                     { return len(m.Layers) }
-func (m *Model) VocabSize() int32                   { return m.Config.VocabSize }
-
-func (m *Model) NewCache(int32) []cache.Cache {
-	caches := make([]cache.Cache, len(m.Layers))
-	for i, layer := range m.Layers {
-		if layer.LayerType == "sliding_attention" && m.SlidingWindow > 0 {
-			caches[i] = cache.NewRotatingKVCache(int(m.SlidingWindow))
-		} else {
-			caches[i] = cache.NewKVCache()
-		}
-	}
-	return caches
-}
-
-func (m *Model) Forward(tokens *mlx.Array, caches []cache.Cache) *mlx.Array {
-	B, L := tokens.Shape()[0], tokens.Shape()[1]
-	x := m.EmbedTokens.Forward(tokens)
-
-	// Find representative cache indices for sliding window attention
-	var swaIdx int = -1
-	for i, layer := range m.Layers {
-		if layer.LayerType == "sliding_attention" {
-			swaIdx = i
-			break
-		}
-	}
-
-	// Create masks once at model level
-	var fullMask, swaMask *mlx.Array
-	var fullMaskMode, swaMaskMode string
-
-	if L > 1 {
-		fullMaskMode = "causal"
-		if swaIdx >= 0 && m.SlidingWindow > 0 && caches != nil {
-			c := caches[swaIdx]
-			offset := c.Offset()
-			windowSize := int(m.SlidingWindow)
-			cacheLen := min(int(L), windowSize)
-			if offset > 0 {
-				cacheLen = min(c.Len()+int(L), windowSize)
-			}
-			if int(L) > windowSize {
-				swaMask = CreateSlidingWindowMask(int(L), offset, offset+int(L)-cacheLen, cacheLen, windowSize)
-			} else {
-				swaMaskMode = "causal"
-			}
-		} else {
-			swaMaskMode = "causal"
-		}
-	}
-
-	for i, layer := range m.Layers {
-		var c cache.Cache
-		if caches != nil {
-			c = caches[i]
-		}
-		mask, maskMode := fullMask, fullMaskMode
-		if layer.LayerType == "sliding_attention" {
-			mask, maskMode = swaMask, swaMaskMode
-		}
-		x = layer.Forward(x, c, B, L, mask, maskMode, m.Config)
-	}
-
-	return m.LMHead.Forward(m.Norm.Forward(x, m.RMSNormEps))
-}
-
-// sanitizeExpertWeights splits merged gate_up weights into separate gate/up arrays.
-// MXFP4 quantized weights require contiguous memory - strided views give wrong results.
-func sanitizeExpertWeights(weights *safetensors.ModelWeights, prefix string) (moe *MoE) {
-	gateUpBlocks, _ := weights.GetTensor(prefix + ".mlp.experts.gate_up_proj_blocks")
-	gateUpScales, _ := weights.GetTensor(prefix + ".mlp.experts.gate_up_proj_scales")
-	gateUpBias, _ := weights.GetTensor(prefix + ".mlp.experts.gate_up_proj_bias")
-	downBlocks, _ := weights.GetTensor(prefix + ".mlp.experts.down_proj_blocks")
-	downScales, _ := weights.GetTensor(prefix + ".mlp.experts.down_proj_scales")
-	downBias, _ := weights.GetTensor(prefix + ".mlp.experts.down_proj_bias")
-
-	moe = &MoE{GroupSize: 32, Bits: 4, DownScales: downScales, DownBias: downBias}
-
-	if gateUpBlocks != nil {
-		gub := mlx.FlattenRange(mlx.View(gateUpBlocks, int(mlx.DtypeUint32)), -2, -1)
-		s := gub.Shape()
-		moe.GateBlocks = mlx.Contiguous(mlx.SliceStride(gub, []int32{0, 0, 0}, []int32{s[0], s[1], s[2]}, []int32{1, 2, 1}))
-		moe.UpBlocks = mlx.Contiguous(mlx.SliceStride(gub, []int32{0, 1, 0}, []int32{s[0], s[1], s[2]}, []int32{1, 2, 1}))
-	}
-	if gateUpScales != nil {
-		s := gateUpScales.Shape()
-		moe.GateScales = mlx.Contiguous(mlx.SliceStride(gateUpScales, []int32{0, 0, 0}, []int32{s[0], s[1], s[2]}, []int32{1, 2, 1}))
-		moe.UpScales = mlx.Contiguous(mlx.SliceStride(gateUpScales, []int32{0, 1, 0}, []int32{s[0], s[1], s[2]}, []int32{1, 2, 1}))
-	}
-	if gateUpBias != nil {
-		s := gateUpBias.Shape()
-		moe.GateBias = mlx.Contiguous(mlx.SliceStride(gateUpBias, []int32{0, 0}, []int32{s[0], s[1]}, []int32{1, 2}))
-		moe.UpBias = mlx.Contiguous(mlx.SliceStride(gateUpBias, []int32{0, 1}, []int32{s[0], s[1]}, []int32{1, 2}))
-	}
-	if downBlocks != nil {
-		moe.DownBlocks = mlx.FlattenRange(mlx.View(downBlocks, int(mlx.DtypeUint32)), -2, -1)
-	}
-	return moe
-}
-
-func Load(modelPath string) (*Model, error) {
-	data, err := os.ReadFile(filepath.Join(modelPath, "config.json"))
-	if err != nil {
-		return nil, fmt.Errorf("load config: %w", err)
-	}
-	var cfg Config
-	if err := json.Unmarshal(data, &cfg); err != nil {
-		return nil, fmt.Errorf("parse config: %w", err)
-	}
-	cfg.Scale = float32(1.0 / math.Sqrt(float64(cfg.HeadDim)))
-
-	weights, err := safetensors.LoadModelWeights(modelPath)
-	if err != nil {
-		return nil, fmt.Errorf("load weights: %w", err)
-	}
-
-	tok, err := tokenizer.Load(filepath.Join(modelPath, "tokenizer.json"))
-	if err != nil {
-		return nil, fmt.Errorf("load tokenizer: %w", err)
-	}
-
-	m := &Model{
-		Layers: make([]*Block, cfg.NumHiddenLayers),
-		Config: &cfg,
-		tok:    tok,
-	}
-
-	// Load simple weights via struct tags
-	if err := safetensors.LoadModule(m, weights, ""); err != nil {
-		return nil, err
-	}
-
-	// Load layers with custom MoE handling
-	for i := int32(0); i < cfg.NumHiddenLayers; i++ {
-		prefix := fmt.Sprintf("model.layers.%d", i)
-		layer := &Block{}
-		if err := safetensors.LoadModule(layer, weights, prefix); err != nil {
-			return nil, fmt.Errorf("layer %d: %w", i, err)
-		}
-
-		// Initialize attention YaRN
-		layer.Attention.initYarn(&cfg)
-
-		// Load MoE with weight sanitization
-		moe := sanitizeExpertWeights(weights, prefix)
-		moe.Router = layer.MLP.Router // Router was loaded by LoadModule
-		moe.TopK = cfg.NumExpertsPerTok
-		moe.HiddenSize = cfg.HiddenSize
-		layer.MLP = moe
-
-		// Set layer type
-		layer.LayerType = "full_attention"
-		if int(i) < len(cfg.LayerTypes) {
-			layer.LayerType = cfg.LayerTypes[i]
-		}
-
-		m.Layers[i] = layer
-	}
-
-	// Release safetensors BEFORE eval - lazy arrays have captured data,
-	// this reduces peak memory by freeing mmap during materialization
-	weights.ReleaseAll()
-	mlx.Eval(mlx.Collect(m)...)
-
-	return m, nil
-}
-
-func (m *Model) MaxContextLength() int32 {
-	if m.RopeScaling != nil && m.RopeScaling.OriginalMaxPositionEmbeddings > 0 {
-		return m.RopeScaling.OriginalMaxPositionEmbeddings
-	}
-	return 131072
-}

x/imagegen/models/llama/llama.go

@@ -1,152 +0,0 @@
-//go:build mlx
-
-package llama
-
-import (
-	"encoding/json"
-	"fmt"
-	"math"
-	"os"
-	"path/filepath"
-
-	"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"
-	"github.com/ollama/ollama/x/imagegen/tokenizer"
-)
-
-type Config 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"`
-	RopeTheta             float32 `json:"rope_theta"`
-	MaxPositionEmbeddings int32   `json:"max_position_embeddings"`
-	HeadDim               int32   `json:"-"`
-	Scale                 float32 `json:"-"`
-}
-
-type Model struct {
-	EmbedTokens *nn.Embedding `weight:"model.embed_tokens"`
-	Layers      []*Layer      `weight:"model.layers"`
-	Norm        *nn.RMSNorm   `weight:"model.norm"`
-	Output      *nn.Linear    `weight:"lm_head,optional"`
-
-	tok *tokenizer.Tokenizer
-	*Config
-}
-
-type Layer struct {
-	Attention     *Attention
-	MLP           *MLP
-	AttentionNorm *nn.RMSNorm `weight:"input_layernorm"`
-	MLPNorm       *nn.RMSNorm `weight:"post_attention_layernorm"`
-}
-
-type Attention struct {
-	QProj *nn.Linear `weight:"self_attn.q_proj"`
-	KProj *nn.Linear `weight:"self_attn.k_proj"`
-	VProj *nn.Linear `weight:"self_attn.v_proj"`
-	OProj *nn.Linear `weight:"self_attn.o_proj"`
-}
-
-type MLP struct {
-	GateProj *nn.Linear `weight:"mlp.gate_proj"`
-	UpProj   *nn.Linear `weight:"mlp.up_proj"`
-	DownProj *nn.Linear `weight:"mlp.down_proj"`
-}
-
-func Load(modelPath string) (*Model, error) {
-	data, err := os.ReadFile(filepath.Join(modelPath, "config.json"))
-	if err != nil {
-		return nil, fmt.Errorf("load config: %w", err)
-	}
-	var cfg Config
-	if err := json.Unmarshal(data, &cfg); err != nil {
-		return nil, fmt.Errorf("parse config: %w", err)
-	}
-	cfg.HeadDim = cfg.HiddenSize / cfg.NumAttentionHeads
-	cfg.Scale = float32(1.0 / math.Sqrt(float64(cfg.HeadDim)))
-
-	weights, err := safetensors.LoadModelWeights(modelPath)
-	if err != nil {
-		return nil, fmt.Errorf("load weights: %w", err)
-	}
-
-	tok, err := tokenizer.Load(filepath.Join(modelPath, "tokenizer.json"))
-	if err != nil {
-		return nil, fmt.Errorf("load tokenizer: %w", err)
-	}
-
-	m := &Model{
-		Layers: make([]*Layer, cfg.NumHiddenLayers),
-		Config: &cfg,
-		tok:    tok,
-	}
-	if err := safetensors.LoadModule(m, weights, ""); err != nil {
-		return nil, err
-	}
-	m.Output = nn.NewLinear(m.EmbedTokens.Weight, nil)
-
-	mlx.Eval(mlx.Collect(m)...)
-	weights.ReleaseAll()
-
-	return m, nil
-}
-
-func (m *Model) Forward(tokens *mlx.Array, caches []cache.Cache) *mlx.Array {
-	B, L := tokens.Shape()[0], tokens.Shape()[1]
-	h := m.EmbedTokens.Forward(tokens)
-	for i, layer := range m.Layers {
-		h = layer.Forward(h, caches[i], B, L, m.Config)
-	}
-	return m.Output.Forward(m.Norm.Forward(h, m.RMSNormEps))
-}
-
-func (l *Layer) Forward(x *mlx.Array, c cache.Cache, B, L int32, cfg *Config) *mlx.Array {
-	h := mlx.Add(x, l.Attention.Forward(l.AttentionNorm.Forward(x, cfg.RMSNormEps), c, B, L, cfg))
-	return mlx.Add(h, l.MLP.Forward(l.MLPNorm.Forward(h, cfg.RMSNormEps)))
-}
-
-func (a *Attention) Forward(x *mlx.Array, c cache.Cache, B, L int32, cfg *Config) *mlx.Array {
-	q := a.QProj.Forward(x)
-	k := a.KProj.Forward(x)
-	v := a.VProj.Forward(x)
-
-	q = mlx.AsStrided(q, []int32{B, cfg.NumAttentionHeads, L, cfg.HeadDim},
-		[]int64{int64(L * cfg.NumAttentionHeads * cfg.HeadDim), int64(cfg.HeadDim), int64(cfg.NumAttentionHeads * cfg.HeadDim), 1}, 0)
-	k = mlx.AsStrided(k, []int32{B, cfg.NumKeyValueHeads, L, cfg.HeadDim},
-		[]int64{int64(L * cfg.NumKeyValueHeads * cfg.HeadDim), int64(cfg.HeadDim), int64(cfg.NumKeyValueHeads * cfg.HeadDim), 1}, 0)
-	v = mlx.AsStrided(v, []int32{B, cfg.NumKeyValueHeads, L, cfg.HeadDim},
-		[]int64{int64(L * cfg.NumKeyValueHeads * cfg.HeadDim), int64(cfg.HeadDim), int64(cfg.NumKeyValueHeads * cfg.HeadDim), 1}, 0)
-
-	q = mlx.RoPE(q, int(cfg.HeadDim), false, cfg.RopeTheta, 1.0, c.Offset())
-	k = mlx.RoPE(k, int(cfg.HeadDim), false, cfg.RopeTheta, 1.0, c.Offset())
-
-	k, v = c.Update(k, v, int(L))
-	out := mlx.ScaledDotProductAttention(q, k, v, cfg.Scale, L > 1)
-	out = mlx.Reshape(mlx.Transpose(out, 0, 2, 1, 3), B, L, cfg.NumAttentionHeads*cfg.HeadDim)
-	return a.OProj.Forward(out)
-}
-
-func (m *MLP) Forward(x *mlx.Array) *mlx.Array {
-	return m.DownProj.Forward(mlx.Mul(mlx.SiLU(m.GateProj.Forward(x)), m.UpProj.Forward(x)))
-}
-
-// Interface methods
-func (m *Model) NumLayers() int                     { return len(m.Layers) }
-func (m *Model) MaxContextLength() int32            { return m.MaxPositionEmbeddings }
-func (m *Model) VocabSize() int32                   { return m.Config.VocabSize }
-func (m *Model) Tokenizer() *tokenizer.Tokenizer { return m.tok }
-
-func (m *Model) NewCache(maxSeqLen int32) []cache.Cache {
-	caches := make([]cache.Cache, len(m.Layers))
-	for i := range caches {
-		caches[i] = cache.NewKVCache()
-	}
-	return caches
-}

x/imagegen/models/qwen_image/pipeline_test.go

@@ -1,66 +0,0 @@
-//go:build mlx
-
-package qwen_image
-
-import (
-	"os"
-	"testing"
-
-	"github.com/ollama/ollama/x/imagegen/mlx"
-)
-
-// TestPipelineOutput runs the full pipeline (integration test).
-// Skips if model weights not found. Requires ~50GB VRAM.
-func TestPipelineOutput(t *testing.T) {
-	modelPath := "../../../weights/Qwen-Image-2512"
-	if _, err := os.Stat(modelPath); os.IsNotExist(err) {
-		t.Skip("Skipping: model weights not found at " + modelPath)
-	}
-
-	// Load model
-	pm, err := LoadPersistent(modelPath)
-	if err != nil {
-		t.Skipf("Skipping: failed to load model: %v", err)
-	}
-
-	// Run 2-step pipeline (minimum for stable scheduler)
-	cfg := &GenerateConfig{
-		Prompt: "a cat",
-		Width:  256,
-		Height: 256,
-		Steps:  2,
-		Seed:   42,
-	}
-
-	output, err := pm.GenerateFromConfig(cfg)
-	if err != nil {
-		t.Fatalf("Pipeline failed: %v", err)
-	}
-	mlx.Eval(output)
-
-	// Verify output shape [1, C, H, W]
-	shape := output.Shape()
-	if len(shape) != 4 {
-		t.Errorf("Expected 4D output, got %v", shape)
-	}
-	if shape[0] != 1 || shape[1] != 3 || shape[2] != cfg.Height || shape[3] != cfg.Width {
-		t.Errorf("Shape mismatch: got %v, expected [1, 3, %d, %d]", shape, cfg.Height, cfg.Width)
-	}
-
-	// Verify values in expected range [0, 1]
-	data := output.Data()
-	minVal, maxVal := float32(1.0), float32(0.0)
-	for _, v := range data {
-		if v < minVal {
-			minVal = v
-		}
-		if v > maxVal {
-			maxVal = v
-		}
-	}
-	t.Logf("Output range: [%.4f, %.4f]", minVal, maxVal)
-
-	if minVal < -0.1 || maxVal > 1.1 {
-		t.Errorf("Output values out of range: [%.4f, %.4f]", minVal, maxVal)
-	}
-}

x/imagegen/models/qwen_image/qwen_image.go

@@ -1,350 +0,0 @@
-//go:build mlx
-
-// Package qwen_image implements the Qwen-Image diffusion transformer model.
-package qwen_image
-
-import (
-	"context"
-	"fmt"
-	"path/filepath"
-	"time"
-
-	"github.com/ollama/ollama/x/imagegen/cache"
-	"github.com/ollama/ollama/x/imagegen/mlx"
-	"github.com/ollama/ollama/x/imagegen/tokenizer"
-)
-
-// GenerateConfig holds all options for image generation.
-type GenerateConfig struct {
-	Prompt         string
-	NegativePrompt string       // Empty = no CFG
-	CFGScale       float32      // Only used if NegativePrompt is set (default: 4.0)
-	Width          int32        // Image width (default: 1024)
-	Height         int32        // Image height (default: 1024)
-	Steps          int          // Denoising steps (default: 30)
-	Seed           int64        // Random seed
-	Progress       ProgressFunc // Optional progress callback
-
-	// Layer caching (DeepCache/Learning-to-Cache speedup)
-	LayerCache    bool // Enable layer caching (default: false)
-	CacheInterval int  // Refresh cache every N steps (default: 3)
-	CacheLayers   int  // Number of shallow layers to cache (default: 25)
-}
-
-// ProgressFunc is called during generation with step progress.
-type ProgressFunc func(step, totalSteps int)
-
-// Model represents a Qwen-Image diffusion model.
-type Model struct {
-	ModelPath   string
-	Tokenizer   *tokenizer.Tokenizer
-	TextEncoder *Qwen25VL
-	Transformer *Transformer
-	VAEDecoder  *VAEDecoder
-}
-
-// Load loads the Qwen-Image model from a directory.
-func (m *Model) Load(modelPath string) error {
-	fmt.Println("Loading Qwen-Image model...")
-	start := time.Now()
-
-	if mlx.GPUIsAvailable() {
-		mlx.SetDefaultDeviceGPU()
-		mlx.EnableCompile()
-	}
-
-	m.ModelPath = modelPath
-
-	// Load tokenizer
-	fmt.Print("  Loading tokenizer... ")
-	tokenizerPath := filepath.Join(modelPath, "tokenizer")
-	tok, err := tokenizer.Load(tokenizerPath)
-	if err != nil {
-		return fmt.Errorf("tokenizer: %w", err)
-	}
-	m.Tokenizer = tok
-	fmt.Println("✓")
-
-	// Load text encoder (Qwen2.5-VL in text-only mode - skip vision tower for efficiency)
-	m.TextEncoder = &Qwen25VL{}
-	if err := m.TextEncoder.LoadTextOnly(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 transformer
-	m.Transformer = &Transformer{}
-	if err := m.Transformer.Load(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.Load(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()
-	peak := mlx.MetalGetPeakMemory()
-	fmt.Printf("  Loaded in %.2fs (%.1f GB active, %.1f GB peak)\n",
-		time.Since(start).Seconds(),
-		float64(mem)/(1024*1024*1024),
-		float64(peak)/(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(&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(&GenerateConfig{
-		Prompt:   prompt,
-		Width:    width,
-		Height:   height,
-		Steps:    steps,
-		Seed:     seed,
-		Progress: progress,
-	})
-}
-
-// GenerateWithCFG creates an image with classifier-free guidance.
-func (m *Model) GenerateWithCFG(prompt, negativePrompt string, width, height int32, steps int, seed int64, cfgScale float32, progress ProgressFunc) (*mlx.Array, error) {
-	return m.GenerateFromConfig(&GenerateConfig{
-		Prompt:         prompt,
-		NegativePrompt: negativePrompt,
-		CFGScale:       cfgScale,
-		Width:          width,
-		Height:         height,
-		Steps:          steps,
-		Seed:           seed,
-		Progress:       progress,
-	})
-}
-
-// GenerateFromConfig generates an image using the unified config struct.
-func (m *Model) GenerateFromConfig(cfg *GenerateConfig) (*mlx.Array, error) {
-	start := time.Now()
-	result, err := m.generate(cfg)
-	if err != nil {
-		return nil, err
-	}
-	if cfg.NegativePrompt != "" {
-		fmt.Printf("Generated with CFG (scale=%.1f) in %.2fs (%d steps)\n", cfg.CFGScale, time.Since(start).Seconds(), cfg.Steps)
-	} else {
-		fmt.Printf("Generated in %.2fs (%d 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 denoising pipeline.
-func (m *Model) generate(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 = 30
-	}
-	if cfg.CFGScale <= 0 {
-		cfg.CFGScale = 4.0
-	}
-	if cfg.CacheInterval <= 0 {
-		cfg.CacheInterval = 3
-	}
-	if cfg.CacheLayers <= 0 {
-		cfg.CacheLayers = 25 // ~42% of 60 layers (similar ratio to Z-Image's 15/38)
-	}
-
-	useCFG := cfg.NegativePrompt != ""
-	tcfg := m.Transformer.Config
-	latentH := cfg.Height / 8
-	latentW := cfg.Width / 8
-	pH := latentH / tcfg.PatchSize
-	pW := latentW / tcfg.PatchSize
-	imgSeqLen := pH * pW
-
-	// Text encoding
-	var posEmb, negEmb *mlx.Array
-	{
-		posEmb = m.TextEncoder.EncodePrompt(m.Tokenizer, cfg.Prompt)
-		if useCFG {
-			negEmb = m.TextEncoder.EncodePrompt(m.Tokenizer, cfg.NegativePrompt)
-			mlx.Keep(posEmb, negEmb)
-			mlx.Eval(posEmb, negEmb)
-		} else {
-			mlx.Keep(posEmb)
-			mlx.Eval(posEmb)
-		}
-	}
-
-	// Pad sequences to same length for CFG
-	txtLen := posEmb.Shape()[1]
-	if useCFG {
-		negLen := negEmb.Shape()[1]
-		if negLen > txtLen {
-			txtLen = negLen
-		}
-		if posEmb.Shape()[1] < txtLen {
-			posEmb = padSequence(posEmb, txtLen)
-		}
-		if negEmb.Shape()[1] < txtLen {
-			negEmb = padSequence(negEmb, txtLen)
-		}
-		mlx.Keep(posEmb, negEmb)
-	}
-
-	// Scheduler
-	scheduler := NewFlowMatchScheduler(DefaultSchedulerConfig())
-	scheduler.SetTimesteps(cfg.Steps, imgSeqLen)
-
-	// Init latents [B, C, T, H, W]
-	var latents *mlx.Array
-	{
-		latents = scheduler.InitNoise([]int32{1, tcfg.OutChannels, 1, latentH, latentW}, cfg.Seed)
-		mlx.Eval(latents)
-	}
-
-	// RoPE cache
-	var ropeCache *RoPECache
-	{
-		ropeCache = PrepareRoPE(pH, pW, txtLen, tcfg.AxesDimsRope)
-		mlx.Keep(ropeCache.ImgFreqs, ropeCache.TxtFreqs)
-		mlx.Eval(ropeCache.ImgFreqs)
-	}
-
-	// Layer cache for DeepCache/Learning-to-Cache speedup
-	var stepCache *cache.StepCache
-	if cfg.LayerCache {
-		stepCache = cache.NewStepCache(cfg.CacheLayers)
-		fmt.Printf("  Layer caching: %d layers, refresh every %d steps\n", cfg.CacheLayers, cfg.CacheInterval)
-	}
-
-	// Denoising loop
-	for i := 0; i < cfg.Steps; i++ {
-		stepStart := time.Now()
-		if cfg.Progress != nil {
-			cfg.Progress(i+1, cfg.Steps)
-		}
-
-		t := scheduler.Timesteps[i]
-		timestep := mlx.ToBFloat16(mlx.NewArray([]float32{t}, []int32{1}))
-
-		// Squeeze temporal dim: [B, C, T, H, W] -> [B, C, H, W]
-		latents2D := mlx.Squeeze(latents, 2)
-		patches := PackLatents(latents2D, tcfg.PatchSize)
-
-		var output *mlx.Array
-		if useCFG {
-			// True CFG: run twice and combine with norm rescaling
-			// Note: layer caching with CFG is not supported yet (would need 2 caches)
-			posOutput := m.Transformer.Forward(patches, posEmb, timestep, ropeCache.ImgFreqs, ropeCache.TxtFreqs)
-			negOutput := m.Transformer.Forward(patches, negEmb, timestep, ropeCache.ImgFreqs, ropeCache.TxtFreqs)
-
-			diff := mlx.Sub(posOutput, negOutput)
-			scaledDiff := mlx.MulScalar(diff, cfg.CFGScale)
-			combPred := mlx.Add(negOutput, scaledDiff)
-
-			// Norm rescaling: rescale combined prediction to match conditional prediction's norm
-			condNorm := mlx.Sqrt(mlx.Sum(mlx.Square(posOutput), -1, true))
-			combNorm := mlx.Sqrt(mlx.Sum(mlx.Square(combPred), -1, true))
-			output = mlx.Mul(combPred, mlx.Div(condNorm, combNorm))
-		} else if stepCache != nil {
-			output = m.Transformer.ForwardWithCache(patches, posEmb, timestep, ropeCache.ImgFreqs, ropeCache.TxtFreqs,
-				stepCache, i, cfg.CacheInterval, cfg.CacheLayers)
-		} else {
-			output = m.Transformer.Forward(patches, posEmb, timestep, ropeCache.ImgFreqs, ropeCache.TxtFreqs)
-		}
-
-		noisePred := UnpackLatents(output, latentH, latentW, tcfg.PatchSize)
-		oldLatents := latents
-		latents = scheduler.Step(noisePred, latents, i)
-
-		// Keep cached arrays alive across cleanup
-		if stepCache != nil {
-			mlx.Keep(stepCache.Arrays()...)
-		}
-		mlx.Eval(latents)
-		oldLatents.Free()
-
-		activeMem := float64(mlx.MetalGetActiveMemory()) / (1024 * 1024 * 1024)
-		peakMem := float64(mlx.MetalGetPeakMemory()) / (1024 * 1024 * 1024)
-		fmt.Printf("  Step %d/%d: t=%.4f (%.2fs) [%.1f GB active, %.1f GB peak]\n", i+1, cfg.Steps, t, time.Since(stepStart).Seconds(), activeMem, peakMem)
-	}
-
-	// Free denoising temporaries before VAE decode
-	posEmb.Free()
-	if negEmb != nil {
-		negEmb.Free()
-	}
-	ropeCache.ImgFreqs.Free()
-	ropeCache.TxtFreqs.Free()
-	if stepCache != nil {
-		stepCache.Free()
-	}
-
-	// VAE decode (Decode manages its own pools for staged memory)
-	decoded := m.VAEDecoder.Decode(latents)
-	latents.Free()
-	// Post-process: squeeze temporal dim and rescale to [0, 1]
-	{
-		decoded = mlx.Squeeze(decoded, 2)
-		decoded = mlx.AddScalar(decoded, 1.0)
-		decoded = mlx.DivScalar(decoded, 2.0)
-		mlx.Eval(decoded)
-	}
-
-	fmt.Printf("  Peak memory: %.2f GB\n", float64(mlx.MetalGetPeakMemory())/(1024*1024*1024))
-
-	return decoded, nil
-}
-
-// padSequence pads a sequence tensor to the target length with zeros
-func padSequence(x *mlx.Array, targetLen int32) *mlx.Array {
-	shape := x.Shape()
-	currentLen := shape[1]
-	if currentLen >= targetLen {
-		return x
-	}
-	padLen := targetLen - currentLen
-	// Pad on sequence dimension (axis 1)
-	return mlx.Pad(x, []int32{0, 0, 0, padLen, 0, 0})
-}
-
-// LoadPersistent is an alias for backward compatibility.
-// Use m := &Model{}; m.Load(path) instead.
-func LoadPersistent(modelPath string) (*Model, error) {
-	m := &Model{}
-	if err := m.Load(modelPath); err != nil {
-		return nil, err
-	}
-	return m, nil
-}

x/imagegen/models/qwen_image/scheduler.go

@@ -1,218 +0,0 @@
-//go:build mlx
-
-package qwen_image
-
-import (
-	"math"
-
-	"github.com/ollama/ollama/x/imagegen/mlx"
-)
-
-// SchedulerConfig holds FlowMatchEulerDiscreteScheduler configuration
-type SchedulerConfig struct {
-	NumTrainTimesteps int32   `json:"num_train_timesteps"` // 1000
-	BaseShift         float32 `json:"base_shift"`          // 0.5
-	MaxShift          float32 `json:"max_shift"`           // 0.9
-	BaseImageSeqLen   int32   `json:"base_image_seq_len"`  // 256
-	MaxImageSeqLen    int32   `json:"max_image_seq_len"`   // 8192
-	ShiftTerminal     float32 `json:"shift_terminal"`      // 0.02
-	UseDynamicShift   bool    `json:"use_dynamic_shifting"` // true
-}
-
-// DefaultSchedulerConfig returns config for FlowMatchEulerDiscreteScheduler
-func DefaultSchedulerConfig() *SchedulerConfig {
-	return &SchedulerConfig{
-		NumTrainTimesteps: 1000,
-		BaseShift:         0.5,
-		MaxShift:          0.9, // Matches scheduler_config.json
-		BaseImageSeqLen:   256,
-		MaxImageSeqLen:    8192,
-		ShiftTerminal:     0.02,
-		UseDynamicShift:   true,
-	}
-}
-
-// FlowMatchScheduler implements the Flow Match Euler discrete scheduler
-type FlowMatchScheduler struct {
-	Config    *SchedulerConfig
-	Timesteps []float32
-	Sigmas    []float32
-	NumSteps  int
-}
-
-// NewFlowMatchScheduler creates a new scheduler
-func NewFlowMatchScheduler(cfg *SchedulerConfig) *FlowMatchScheduler {
-	return &FlowMatchScheduler{
-		Config: cfg,
-	}
-}
-
-// CalculateShift computes the dynamic shift based on image sequence length
-// This matches Python's calculate_shift function
-func CalculateShift(imageSeqLen int32, baseSeqLen int32, maxSeqLen int32, baseShift float32, maxShift float32) float32 {
-	m := (maxShift - baseShift) / float32(maxSeqLen-baseSeqLen)
-	b := baseShift - m*float32(baseSeqLen)
-	mu := float32(imageSeqLen)*m + b
-	return mu
-}
-
-// SetTimesteps sets up the scheduler for the given number of inference steps
-// Matches Python diffusers FlowMatchEulerDiscreteScheduler behavior:
-// 1. Create sigmas from sigma_max to sigma_min (linspace)
-// 2. Apply time_shift with mu (if dynamic shifting)
-// 3. Apply stretch_shift_to_terminal to make final value = shift_terminal
-func (s *FlowMatchScheduler) SetTimesteps(numSteps int, imageSeqLen int32) {
-	s.NumSteps = numSteps
-
-	// Calculate mu for dynamic shifting
-	var mu float32
-	if s.Config.UseDynamicShift {
-		mu = CalculateShift(
-			imageSeqLen,
-			s.Config.BaseImageSeqLen,
-			s.Config.MaxImageSeqLen,
-			s.Config.BaseShift,
-			s.Config.MaxShift,
-		)
-	}
-
-	// Step 1: Create sigmas from 1.0 to 1/num_steps
-	// Python (pipeline_qwenimage.py:639):
-	//   sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
-	// This gives sigmas from 1.0 to 1/30 = 0.033 for 30 steps
-	sigmas := make([]float32, numSteps)
-	sigmaMax := float32(1.0)
-	sigmaMin := 1.0 / float32(numSteps) // 1/30 = 0.033 for 30 steps
-	if numSteps == 1 {
-		sigmas[0] = sigmaMax
-	} else {
-		for i := 0; i < numSteps; i++ {
-			sigmas[i] = sigmaMax + float32(i)*(sigmaMin-sigmaMax)/float32(numSteps-1)
-		}
-	}
-
-	// Step 2: Apply time shift if using dynamic shifting
-	if s.Config.UseDynamicShift && mu != 0 {
-		for i := range sigmas {
-			sigmas[i] = s.timeShift(mu, sigmas[i])
-		}
-	}
-
-	// Step 3: Apply stretch_shift_to_terminal
-	if s.Config.ShiftTerminal > 0 {
-		sigmas = s.stretchShiftToTerminal(sigmas)
-	}
-
-	// Step 4: Append terminal sigma (0) and store
-	// Note: Python's scheduler.timesteps are sigmas*1000, but the pipeline divides by 1000
-	// before passing to transformer. We skip both steps and just use sigmas directly.
-	s.Sigmas = make([]float32, numSteps+1)
-	s.Timesteps = make([]float32, numSteps+1)
-	for i := 0; i < numSteps; i++ {
-		s.Sigmas[i] = sigmas[i]
-		s.Timesteps[i] = sigmas[i]
-	}
-	s.Sigmas[numSteps] = 0.0
-	s.Timesteps[numSteps] = 0.0
-}
-
-// stretchShiftToTerminal stretches and shifts the timestep schedule
-// so the final value equals shift_terminal (matches Python behavior)
-func (s *FlowMatchScheduler) stretchShiftToTerminal(sigmas []float32) []float32 {
-	if len(sigmas) == 0 {
-		return sigmas
-	}
-
-	// one_minus_z = 1 - t
-	// scale_factor = one_minus_z[-1] / (1 - shift_terminal)
-	// stretched_t = 1 - (one_minus_z / scale_factor)
-	lastSigma := sigmas[len(sigmas)-1]
-	scaleFactor := (1.0 - lastSigma) / (1.0 - s.Config.ShiftTerminal)
-
-	// Handle edge case: if scaleFactor is 0 or near 0, skip stretch
-	// This happens when lastSigma ≈ 1.0 (e.g., single step with timeshift)
-	if scaleFactor < 1e-6 {
-		return sigmas
-	}
-
-	result := make([]float32, len(sigmas))
-	for i, t := range sigmas {
-		oneMinusZ := 1.0 - t
-		result[i] = 1.0 - (oneMinusZ / scaleFactor)
-	}
-	return result
-}
-
-// timeShift applies the dynamic time shift (exponential)
-// exp(mu) / (exp(mu) + (1/t - 1))
-func (s *FlowMatchScheduler) timeShift(mu float32, t float32) float32 {
-	if t <= 0 {
-		return 0
-	}
-	expMu := float32(math.Exp(float64(mu)))
-	return expMu / (expMu + (1.0/t - 1.0))
-}
-
-// Step performs one denoising step
-// modelOutput: predicted velocity from the transformer
-// sample: current noisy sample
-// timestepIdx: current timestep index
-func (s *FlowMatchScheduler) Step(modelOutput, sample *mlx.Array, timestepIdx int) *mlx.Array {
-	// Get current and next sigma
-	sigma := s.Sigmas[timestepIdx]
-	sigmaNext := s.Sigmas[timestepIdx+1]
-
-	// Euler step: x_{t-dt} = x_t + (sigma_next - sigma) * v_t
-	dt := sigmaNext - sigma
-
-	// Upcast to float32 to avoid precision issues (matches Python diffusers)
-	sampleF32 := mlx.AsType(sample, mlx.DtypeFloat32)
-	modelOutputF32 := mlx.AsType(modelOutput, mlx.DtypeFloat32)
-
-	scaledOutput := mlx.MulScalar(modelOutputF32, dt)
-	result := mlx.Add(sampleF32, scaledOutput)
-
-	// Cast back to original dtype
-	return mlx.ToBFloat16(result)
-}
-
-// GetTimestep returns the timestep value at the given index
-func (s *FlowMatchScheduler) GetTimestep(idx int) float32 {
-	if idx < len(s.Timesteps) {
-		return s.Timesteps[idx]
-	}
-	return 0.0
-}
-
-// InitNoise creates initial noise for sampling in unpacked format [B, C, T, H, W]
-func (s *FlowMatchScheduler) InitNoise(shape []int32, seed int64) *mlx.Array {
-	return mlx.RandomNormal(shape, uint64(seed))
-}
-
-// InitNoisePacked creates initial noise directly in packed format [B, L, C*4]
-// This matches how Python diffusers generates noise - directly in packed space.
-// Generating in unpacked format and then packing produces different spatial
-// correlation structure, which affects model output quality.
-func (s *FlowMatchScheduler) InitNoisePacked(batchSize, seqLen, channels int32, seed int64) *mlx.Array {
-	shape := []int32{batchSize, seqLen, channels}
-	return mlx.RandomNormal(shape, uint64(seed))
-}
-
-// GetLatentShape returns the latent shape for a given image size
-// For qwen_image: VAE downscale is 8x (spatial), latent has 16 channels
-func GetLatentShape(batchSize, height, width int32) []int32 {
-	latentH := height / 8
-	latentW := width / 8
-	return []int32{batchSize, 16, 1, latentH, latentW} // [B, C, T, H, W]
-}
-
-// GetPatchedLatentShape returns the patchified latent shape
-// After patchification: [B, L, C*patch_size^2] where L = H/2 * W/2
-func GetPatchedLatentShape(batchSize, height, width, patchSize int32) []int32 {
-	latentH := height / 8
-	latentW := width / 8
-	pH := latentH / patchSize
-	pW := latentW / patchSize
-	inChannels := int32(64) // 16 * patch_size^2
-	return []int32{batchSize, pH * pW, inChannels}
-}

x/imagegen/models/qwen_image/scheduler_test.go

@@ -1,135 +0,0 @@
-//go:build mlx
-
-package qwen_image
-
-import (
-	"math"
-	"testing"
-)
-
-// TestSchedulerSetTimesteps verifies scheduler sigmas match Python diffusers reference.
-// Golden values generated via:
-//
-//	python3 -c "
-//	from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
-//	import numpy as np
-//	s = FlowMatchEulerDiscreteScheduler(num_train_timesteps=1000, base_shift=0.5, max_shift=0.9,
-//	    base_image_seq_len=256, max_image_seq_len=8192, shift_terminal=0.02, use_dynamic_shifting=True)
-//	mu = 4096 * (0.9-0.5)/(8192-256) + 0.5 - (0.9-0.5)/(8192-256)*256
-//	sigmas = np.linspace(1.0, 1.0/30, 30)
-//	s.set_timesteps(sigmas=sigmas, mu=mu)
-//	print(s.sigmas.numpy())"
-func TestSchedulerSetTimesteps(t *testing.T) {
-	cfg := DefaultSchedulerConfig()
-	scheduler := NewFlowMatchScheduler(cfg)
-	scheduler.SetTimesteps(30, 4096)
-
-	// Golden values from Python diffusers (first 3, last 3 before terminal)
-	wantFirst := []float32{1.000000, 0.982251, 0.963889}
-	wantLast := []float32{0.142924, 0.083384, 0.020000}
-
-	// Check first 3
-	for i, want := range wantFirst {
-		got := scheduler.Sigmas[i]
-		if abs32(got-want) > 1e-4 {
-			t.Errorf("sigma[%d]: got %v, want %v", i, got, want)
-		}
-	}
-
-	// Check last 3 (indices 27, 28, 29)
-	for i, want := range wantLast {
-		idx := 27 + i
-		got := scheduler.Sigmas[idx]
-		if abs32(got-want) > 1e-4 {
-			t.Errorf("sigma[%d]: got %v, want %v", idx, got, want)
-		}
-	}
-
-	// Check terminal is 0
-	if scheduler.Sigmas[30] != 0.0 {
-		t.Errorf("terminal sigma: got %v, want 0", scheduler.Sigmas[30])
-	}
-
-	// Check length
-	if len(scheduler.Sigmas) != 31 {
-		t.Errorf("sigmas length: got %d, want 31", len(scheduler.Sigmas))
-	}
-}
-
-// TestSchedulerProperties tests mathematical invariants of the scheduler.
-func TestSchedulerProperties(t *testing.T) {
-	cfg := DefaultSchedulerConfig()
-	scheduler := NewFlowMatchScheduler(cfg)
-	scheduler.SetTimesteps(30, 4096)
-
-	// Property: sigmas monotonically decreasing
-	for i := 1; i < len(scheduler.Sigmas); i++ {
-		if scheduler.Sigmas[i] > scheduler.Sigmas[i-1] {
-			t.Errorf("sigmas not monotonically decreasing at %d: %v > %v",
-				i, scheduler.Sigmas[i], scheduler.Sigmas[i-1])
-		}
-	}
-
-	// Property: first sigma should be ~1.0 (with time shift)
-	if scheduler.Sigmas[0] < 0.9 || scheduler.Sigmas[0] > 1.01 {
-		t.Errorf("first sigma out of expected range [0.9, 1.01]: %v", scheduler.Sigmas[0])
-	}
-
-	// Property: terminal sigma should be exactly 0
-	if scheduler.Sigmas[len(scheduler.Sigmas)-1] != 0.0 {
-		t.Errorf("terminal sigma should be 0, got %v", scheduler.Sigmas[len(scheduler.Sigmas)-1])
-	}
-
-	// Property: last non-terminal sigma should be shift_terminal (0.02)
-	lastNonTerminal := scheduler.Sigmas[len(scheduler.Sigmas)-2]
-	if abs32(lastNonTerminal-0.02) > 1e-5 {
-		t.Errorf("last non-terminal sigma should be 0.02, got %v", lastNonTerminal)
-	}
-
-	// Property: length = steps + 1
-	if len(scheduler.Sigmas) != scheduler.NumSteps+1 {
-		t.Errorf("sigmas length should be steps+1: got %d, want %d",
-			len(scheduler.Sigmas), scheduler.NumSteps+1)
-	}
-}
-
-// TestCalculateShift verifies the mu calculation against Python reference.
-// Golden values from: mu = img_seq_len * m + b where m = (max_shift - base_shift) / (max_seq_len - base_seq_len)
-func TestCalculateShift(t *testing.T) {
-	cases := []struct {
-		imgSeqLen int32
-		want      float32
-	}{
-		{256, 0.5},     // base case
-		{8192, 0.9},    // max case
-		{4096, 0.6935}, // middle case (rounded)
-	}
-
-	for _, c := range cases {
-		got := CalculateShift(c.imgSeqLen, 256, 8192, 0.5, 0.9)
-		if abs32(got-c.want) > 0.001 {
-			t.Errorf("CalculateShift(%d): got %v, want %v", c.imgSeqLen, got, c.want)
-		}
-	}
-}
-
-// TestSchedulerStep verifies the Euler step formula.
-func TestSchedulerStep(t *testing.T) {
-	cfg := DefaultSchedulerConfig()
-	scheduler := NewFlowMatchScheduler(cfg)
-	scheduler.SetTimesteps(30, 4096)
-
-	// Verify dt calculation for first step
-	sigma0 := scheduler.Sigmas[0]
-	sigma1 := scheduler.Sigmas[1]
-	expectedDt := sigma1 - sigma0
-
-	// dt should be negative (sigmas decrease)
-	if expectedDt >= 0 {
-		t.Errorf("expected negative dt, got %v (sigma0=%v, sigma1=%v)", expectedDt, sigma0, sigma1)
-	}
-}
-
-func abs32(x float32) float32 {
-	return float32(math.Abs(float64(x)))
-}