integration: fix tools_test.go for ToolCallFunctionArguments API change (#13731)

* openai: tweak v1/responses to conform better

* openai: provide better error for image URLs

* lint

README.md

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

cmd/cmd.go

@@ -46,9 +46,8 @@ import (
 	"github.com/ollama/ollama/types/syncmap"
 	"github.com/ollama/ollama/version"
 	xcmd "github.com/ollama/ollama/x/cmd"
-	"github.com/ollama/ollama/x/create"
-	xcreateclient "github.com/ollama/ollama/x/create/client"
 	"github.com/ollama/ollama/x/imagegen"
+	imagegenclient "github.com/ollama/ollama/x/imagegen/client"
 )
 
 const ConnectInstructions = "To sign in, navigate to:\n    %s\n\n"
@@ -94,82 +93,15 @@ func CreateHandler(cmd *cobra.Command, args []string) error {
 	p := progress.NewProgress(os.Stderr)
 	defer p.Stop()
 
-	// Check for --experimental flag for safetensors model creation
-	experimental, _ := cmd.Flags().GetBool("experimental")
-	if experimental {
-		modelName := args[0]
-
-		// Get Modelfile content - either from -f flag or default to "FROM ."
-		var reader io.Reader
-		filename, err := getModelfileName(cmd)
-		if os.IsNotExist(err) || filename == "" {
-			// No Modelfile specified or found - use default
-			reader = strings.NewReader("FROM .\n")
-		} else if err != nil {
-			return err
-		} else {
-			f, err := os.Open(filename)
-			if err != nil {
-				return err
-			}
-			defer f.Close()
-			reader = f
-		}
-
-		// Parse the Modelfile
-		modelfile, err := parser.ParseFile(reader)
-		if err != nil {
-			return fmt.Errorf("failed to parse Modelfile: %w", err)
-		}
-
-		// Extract FROM path and configuration
-		var modelDir string
-		mfConfig := &xcreateclient.ModelfileConfig{}
-
-		for _, cmd := range modelfile.Commands {
-			switch cmd.Name {
-			case "model":
-				modelDir = cmd.Args
-			case "template":
-				mfConfig.Template = cmd.Args
-			case "system":
-				mfConfig.System = cmd.Args
-			case "license":
-				mfConfig.License = cmd.Args
-			}
-		}
-
-		if modelDir == "" {
-			modelDir = "."
-		}
-
-		// Resolve relative paths based on Modelfile location
-		if !filepath.IsAbs(modelDir) && filename != "" {
-			modelDir = filepath.Join(filepath.Dir(filename), modelDir)
-		}
-
-		quantize, _ := cmd.Flags().GetString("quantize")
-		return xcreateclient.CreateModel(xcreateclient.CreateOptions{
-			ModelName: modelName,
-			ModelDir:  modelDir,
-			Quantize:  quantize,
-			Modelfile: mfConfig,
-		}, p)
-	}
-
 	var reader io.Reader
 
 	filename, err := getModelfileName(cmd)
 	if os.IsNotExist(err) {
 		if filename == "" {
 			// No Modelfile found - check if current directory is an image gen model
-			if create.IsTensorModelDir(".") {
+			if imagegen.IsTensorModelDir(".") {
 				quantize, _ := cmd.Flags().GetString("quantize")
-				return xcreateclient.CreateModel(xcreateclient.CreateOptions{
-					ModelName: args[0],
-					ModelDir:  ".",
-					Quantize:  quantize,
-				}, p)
+				return imagegenclient.CreateModel(args[0], ".", quantize, p)
 			}
 			reader = strings.NewReader("FROM .\n")
 		} else {
@@ -1810,22 +1742,15 @@ func NewCLI() *cobra.Command {
 	rootCmd.Flags().BoolP("version", "v", false, "Show version information")
 
 	createCmd := &cobra.Command{
-		Use:   "create MODEL",
-		Short: "Create a model",
-		Args:  cobra.ExactArgs(1),
-		PreRunE: func(cmd *cobra.Command, args []string) error {
-			// Skip server check for experimental mode (writes directly to disk)
-			if experimental, _ := cmd.Flags().GetBool("experimental"); experimental {
-				return nil
-			}
-			return checkServerHeartbeat(cmd, args)
-		},
-		RunE: CreateHandler,
+		Use:     "create MODEL",
+		Short:   "Create a model",
+		Args:    cobra.ExactArgs(1),
+		PreRunE: checkServerHeartbeat,
+		RunE:    CreateHandler,
 	}
 
 	createCmd.Flags().StringP("file", "f", "", "Name of the Modelfile (default \"Modelfile\")")
 	createCmd.Flags().StringP("quantize", "q", "", "Quantize model to this level (e.g. q4_K_M)")
-	createCmd.Flags().Bool("experimental", false, "Enable experimental safetensors model creation")
 
 	showCmd := &cobra.Command{
 		Use:     "show MODEL",

cmd/interactive.go

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

docs/api/anthropic-compatibility.mdx

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

docs/docs.json

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

docs/integrations/claude-code.mdx

@@ -25,6 +25,7 @@ Claude Code connects to Ollama using the Anthropic-compatible API.
 1. Set the environment variables:
 
 ```shell
+export ANTHROPIC_AUTH_TOKEN=ollama
 export ANTHROPIC_BASE_URL=http://localhost:11434
 export ANTHROPIC_API_KEY=ollama
 ```
@@ -38,7 +39,7 @@ 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
+ANTHROPIC_AUTH_TOKEN=ollama ANTHROPIC_BASE_URL=http://localhost:11434 ANTHROPIC_API_KEY=ollama claude --model qwen3-coder
 ```
 
 ## Connecting to ollama.com

docs/integrations/marimo.mdx

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

docs/integrations/onyx.mdx

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

integration/tools_test.go

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

middleware/openai.go

@@ -8,6 +8,7 @@ import (
 	"math/rand"
 	"net/http"
 	"strings"
+	"time"
 
 	"github.com/gin-gonic/gin"
 
@@ -441,6 +442,7 @@ type ResponsesWriter struct {
 	stream     bool
 	responseID string
 	itemID     string
+	request    openai.ResponsesRequest
 }
 
 func (w *ResponsesWriter) writeEvent(eventType string, data any) error {
@@ -478,7 +480,9 @@ func (w *ResponsesWriter) writeResponse(data []byte) (int, error) {
 
 	// Non-streaming response
 	w.ResponseWriter.Header().Set("Content-Type", "application/json")
-	response := openai.ToResponse(w.model, w.responseID, w.itemID, chatResponse)
+	response := openai.ToResponse(w.model, w.responseID, w.itemID, chatResponse, w.request)
+	completedAt := time.Now().Unix()
+	response.CompletedAt = &completedAt
 	return len(data), json.NewEncoder(w.ResponseWriter).Encode(response)
 }
 
@@ -523,11 +527,12 @@ func ResponsesMiddleware() gin.HandlerFunc {
 
 		w := &ResponsesWriter{
 			BaseWriter: BaseWriter{ResponseWriter: c.Writer},
-			converter:  openai.NewResponsesStreamConverter(responseID, itemID, req.Model),
+			converter:  openai.NewResponsesStreamConverter(responseID, itemID, req.Model, req),
 			model:      req.Model,
 			stream:     streamRequested,
 			responseID: responseID,
 			itemID:     itemID,
+			request:    req,
 		}
 
 		// Set headers based on streaming mode

openai/openai.go

@@ -630,6 +630,10 @@ func nameFromToolCallID(messages []Message, toolCallID string) string {
 
 // decodeImageURL decodes a base64 data URI into raw image bytes.
 func decodeImageURL(url string) (api.ImageData, error) {
+	if strings.HasPrefix(url, "http://") || strings.HasPrefix(url, "https://") {
+		return nil, errors.New("image URLs are not currently supported, please use base64 encoded data instead")
+	}
+
 	types := []string{"jpeg", "jpg", "png", "webp"}
 
 	// Support blank mime type to match /api/chat's behavior of taking just unadorned base64

openai/responses.go

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

openai/responses_test.go

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

readline/readline.go

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

server/routes.go

@@ -52,7 +52,6 @@ import (
 	"github.com/ollama/ollama/version"
 	"github.com/ollama/ollama/x/imagegen"
 	imagegenapi "github.com/ollama/ollama/x/imagegen/api"
-	xserver "github.com/ollama/ollama/x/server"
 )
 
 const signinURLStr = "https://ollama.com/connect?name=%s&key=%s"
@@ -1134,22 +1133,6 @@ func GetModelInfo(req api.ShowRequest) (*api.ShowResponse, error) {
 		}
 	}
 
-	// For safetensors LLM models (experimental), populate details from config.json
-	if m.Config.ModelFormat == "safetensors" && slices.Contains(m.Config.Capabilities, "completion") {
-		if info, err := xserver.GetSafetensorsLLMInfo(name.String()); err == nil {
-			if arch, ok := info["general.architecture"].(string); ok && arch != "" {
-				modelDetails.Family = arch
-			}
-			if paramCount, ok := info["general.parameter_count"].(int64); ok && paramCount > 0 {
-				modelDetails.ParameterSize = format.HumanNumber(uint64(paramCount))
-			}
-		}
-		// Get torch_dtype directly from config.json for quantization level
-		if dtype, err := xserver.GetSafetensorsDtype(name.String()); err == nil && dtype != "" {
-			modelDetails.QuantizationLevel = dtype
-		}
-	}
-
 	if req.System != "" {
 		m.System = req.System
 	}
@@ -1236,20 +1219,6 @@ func GetModelInfo(req api.ShowRequest) (*api.ShowResponse, error) {
 		return resp, nil
 	}
 
-	// For safetensors LLM models (experimental), populate ModelInfo from config.json
-	if m.Config.ModelFormat == "safetensors" && slices.Contains(m.Config.Capabilities, "completion") {
-		if info, err := xserver.GetSafetensorsLLMInfo(name.String()); err == nil {
-			resp.ModelInfo = info
-		}
-		// Populate tensor info if verbose
-		if req.Verbose {
-			if tensors, err := xserver.GetSafetensorsTensorInfo(name.String()); err == nil {
-				resp.Tensors = tensors
-			}
-		}
-		return resp, nil
-	}
-
 	kvData, tensors, err := getModelData(m.ModelPath, req.Verbose)
 	if err != nil {
 		return nil, err

x/README.md

@@ -1,50 +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.
-
-### Building ollama-mlx
-
-The `ollama-mlx` binary is a separate build of Ollama with MLX support enabled. This enables experimental features like image generation.
-
-#### macOS (Apple Silicon and Intel)
-
-```bash
-# Build MLX backend libraries
-cmake --preset MLX
-cmake --build --preset MLX --parallel
-cmake --install build --component MLX
-
-# Build ollama-mlx binary
-go build -tags mlx -o ollama-mlx .
-```
-
-#### Linux (CUDA)
-
-On Linux, use the preset "MLX CUDA 13" or "MLX CUDA 12" to enable CUDA with the default Ollama NVIDIA GPU architectures enabled:
-
-```bash
-# Build MLX backend libraries with CUDA support
-cmake --preset 'MLX CUDA 13'
-cmake --build --preset 'MLX CUDA 13' --parallel
-cmake --install build --component MLX
-
-# Build ollama-mlx binary
-CGO_CFLAGS="-O3 -I$(pwd)/build/_deps/mlx-c-src" \
-CGO_LDFLAGS="-L$(pwd)/build/lib/ollama -lmlxc -lmlx" \
-go build -tags mlx -o ollama-mlx .
-```
-
-#### Using build scripts
-
-The build scripts automatically create the `ollama-mlx` binary:
-
-- **macOS**: `./scripts/build_darwin.sh` produces `dist/darwin/ollama-mlx`
-- **Linux**: `./scripts/build_linux.sh` produces `ollama-mlx` in the output archives
-
-## Image Generation
-
-Image generation is built into the `ollama-mlx` binary. Run `ollama-mlx serve` to start the server with image generation support enabled.

x/cmd/run.go

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

x/create/client/create.go

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

x/create/client/create_test.go

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

x/create/create.go

@@ -1,391 +0,0 @@
-package create
-
-import (
-	"encoding/json"
-	"fmt"
-	"io"
-	"os"
-	"path/filepath"
-	"slices"
-	"strings"
-
-	"github.com/ollama/ollama/envconfig"
-	"github.com/ollama/ollama/x/imagegen/safetensors"
-)
-
-// ModelConfig represents the config blob stored with a model.
-type ModelConfig struct {
-	ModelFormat  string   `json:"model_format"`
-	Capabilities []string `json:"capabilities"`
-}
-
-// Manifest represents the manifest JSON structure.
-type Manifest struct {
-	SchemaVersion int             `json:"schemaVersion"`
-	MediaType     string          `json:"mediaType"`
-	Config        ManifestLayer   `json:"config"`
-	Layers        []ManifestLayer `json:"layers"`
-}
-
-// ManifestLayer represents a layer in the manifest.
-type ManifestLayer struct {
-	MediaType string `json:"mediaType"`
-	Digest    string `json:"digest"`
-	Size      int64  `json:"size"`
-	Name      string `json:"name,omitempty"`
-}
-
-// defaultManifestDir returns the manifest storage directory.
-func defaultManifestDir() string {
-	return filepath.Join(envconfig.Models(), "manifests")
-}
-
-// defaultBlobDir returns the blob storage directory.
-func defaultBlobDir() string {
-	return filepath.Join(envconfig.Models(), "blobs")
-}
-
-// resolveManifestPath converts a model name to a manifest file path.
-func resolveManifestPath(modelName string) string {
-	host := "registry.ollama.ai"
-	namespace := "library"
-	name := modelName
-	tag := "latest"
-
-	if idx := strings.LastIndex(name, ":"); idx != -1 {
-		tag = name[idx+1:]
-		name = name[:idx]
-	}
-
-	parts := strings.Split(name, "/")
-	switch len(parts) {
-	case 3:
-		host = parts[0]
-		namespace = parts[1]
-		name = parts[2]
-	case 2:
-		namespace = parts[0]
-		name = parts[1]
-	}
-
-	return filepath.Join(defaultManifestDir(), host, namespace, name, tag)
-}
-
-// loadManifest loads a manifest for the given model name.
-func loadManifest(modelName string) (*Manifest, error) {
-	manifestPath := resolveManifestPath(modelName)
-
-	data, err := os.ReadFile(manifestPath)
-	if err != nil {
-		return nil, err
-	}
-
-	var manifest Manifest
-	if err := json.Unmarshal(data, &manifest); err != nil {
-		return nil, err
-	}
-
-	return &manifest, nil
-}
-
-// loadModelConfig loads the config blob for a model.
-func loadModelConfig(modelName string) (*ModelConfig, error) {
-	manifest, err := loadManifest(modelName)
-	if err != nil {
-		return nil, err
-	}
-
-	// Read the config blob
-	blobName := strings.Replace(manifest.Config.Digest, ":", "-", 1)
-	blobPath := filepath.Join(defaultBlobDir(), blobName)
-
-	data, err := os.ReadFile(blobPath)
-	if err != nil {
-		return nil, err
-	}
-
-	var config ModelConfig
-	if err := json.Unmarshal(data, &config); err != nil {
-		return nil, err
-	}
-
-	return &config, nil
-}
-
-// IsSafetensorsModel checks if a model was created with the experimental
-// safetensors builder by checking the model format in the config.
-func IsSafetensorsModel(modelName string) bool {
-	config, err := loadModelConfig(modelName)
-	if err != nil {
-		return false
-	}
-	return config.ModelFormat == "safetensors"
-}
-
-// IsSafetensorsLLMModel checks if a model is a safetensors LLM model
-// (has completion capability, not image generation).
-func IsSafetensorsLLMModel(modelName string) bool {
-	config, err := loadModelConfig(modelName)
-	if err != nil {
-		return false
-	}
-	return config.ModelFormat == "safetensors" && slices.Contains(config.Capabilities, "completion")
-}
-
-// IsImageGenModel checks if a model is an image generation model
-// (has image capability).
-func IsImageGenModel(modelName string) bool {
-	config, err := loadModelConfig(modelName)
-	if err != nil {
-		return false
-	}
-	return config.ModelFormat == "safetensors" && slices.Contains(config.Capabilities, "image")
-}
-
-// GetModelArchitecture returns the architecture from the model's config.json layer.
-func GetModelArchitecture(modelName string) (string, error) {
-	manifest, err := loadManifest(modelName)
-	if err != nil {
-		return "", err
-	}
-
-	// Find the config.json layer
-	for _, layer := range manifest.Layers {
-		if layer.Name == "config.json" && layer.MediaType == "application/vnd.ollama.image.json" {
-			blobName := strings.Replace(layer.Digest, ":", "-", 1)
-			blobPath := filepath.Join(defaultBlobDir(), blobName)
-
-			data, err := os.ReadFile(blobPath)
-			if err != nil {
-				return "", err
-			}
-
-			var cfg struct {
-				Architectures []string `json:"architectures"`
-				ModelType     string   `json:"model_type"`
-			}
-			if err := json.Unmarshal(data, &cfg); err != nil {
-				return "", err
-			}
-
-			// Prefer model_type, fall back to first architecture
-			if cfg.ModelType != "" {
-				return cfg.ModelType, nil
-			}
-			if len(cfg.Architectures) > 0 {
-				return cfg.Architectures[0], nil
-			}
-		}
-	}
-
-	return "", fmt.Errorf("architecture not found in model config")
-}
-
-// IsTensorModelDir checks if the directory contains a diffusers-style tensor model
-// by looking for model_index.json, which is the standard diffusers pipeline config.
-func IsTensorModelDir(dir string) bool {
-	_, err := os.Stat(filepath.Join(dir, "model_index.json"))
-	return err == nil
-}
-
-// IsSafetensorsModelDir checks if the directory contains a standard safetensors model
-// by looking for config.json and at least one .safetensors file.
-func IsSafetensorsModelDir(dir string) bool {
-	// Must have config.json
-	if _, err := os.Stat(filepath.Join(dir, "config.json")); err != nil {
-		return false
-	}
-
-	// Must have at least one .safetensors file
-	entries, err := os.ReadDir(dir)
-	if err != nil {
-		return false
-	}
-
-	for _, entry := range entries {
-		if strings.HasSuffix(entry.Name(), ".safetensors") {
-			return true
-		}
-	}
-
-	return false
-}
-
-// LayerInfo holds metadata for a created layer.
-type LayerInfo struct {
-	Digest    string
-	Size      int64
-	MediaType string
-	Name      string // Path-style name: "component/tensor" or "path/to/config.json"
-}
-
-// LayerCreator is called to create a blob layer.
-// name is the path-style name (e.g., "tokenizer/tokenizer.json")
-type LayerCreator func(r io.Reader, mediaType, name string) (LayerInfo, error)
-
-// TensorLayerCreator creates a tensor blob layer with metadata.
-// name is the path-style name including component (e.g., "text_encoder/model.embed_tokens.weight")
-type TensorLayerCreator func(r io.Reader, name, dtype string, shape []int32) (LayerInfo, error)
-
-// QuantizingTensorLayerCreator creates tensor layers with optional quantization.
-// When quantize is true, returns multiple layers (weight + scales + biases).
-type QuantizingTensorLayerCreator func(r io.Reader, name, dtype string, shape []int32, quantize bool) ([]LayerInfo, error)
-
-// ManifestWriter writes the manifest file.
-type ManifestWriter func(modelName string, config LayerInfo, layers []LayerInfo) error
-
-// ShouldQuantize returns true if a tensor should be quantized.
-// For image gen models (component non-empty): quantizes linear weights, skipping VAE, embeddings, norms.
-// For LLM models (component empty): quantizes linear weights, skipping embeddings, norms, and small tensors.
-func ShouldQuantize(name, component string) bool {
-	// Image gen specific: skip VAE entirely
-	if component == "vae" {
-		return false
-	}
-
-	// Skip embeddings
-	if strings.Contains(name, "embed") {
-		return false
-	}
-
-	// Skip layer norms and RMS norms
-	if strings.Contains(name, "norm") || strings.Contains(name, "ln_") || strings.Contains(name, "layernorm") {
-		return false
-	}
-
-	// Skip biases
-	if strings.HasSuffix(name, ".bias") {
-		return false
-	}
-
-	// Only quantize weights
-	return strings.HasSuffix(name, ".weight")
-}
-
-// ShouldQuantizeTensor returns true if a tensor should be quantized based on name and shape.
-// This is a more detailed check that also considers tensor dimensions.
-func ShouldQuantizeTensor(name string, shape []int32) bool {
-	// Use basic name-based check first
-	if !ShouldQuantize(name, "") {
-		return false
-	}
-
-	// Only quantize 2D tensors (linear layers) - skip 1D (biases, norms) and higher-D (convolutions if any)
-	if len(shape) != 2 {
-		return false
-	}
-
-	// Skip small tensors (less than 1024 elements) - not worth quantizing
-	if len(shape) >= 2 && int64(shape[0])*int64(shape[1]) < 1024 {
-		return false
-	}
-
-	return true
-}
-
-// CreateSafetensorsModel imports a standard safetensors model from a directory.
-// This handles Hugging Face style models with config.json and *.safetensors files.
-// Stores each tensor as a separate blob for fine-grained deduplication.
-// If quantize is non-empty (e.g., "fp8"), eligible tensors will be quantized.
-func CreateSafetensorsModel(modelName, modelDir, quantize string, createLayer LayerCreator, createTensorLayer QuantizingTensorLayerCreator, writeManifest ManifestWriter, fn func(status string)) error {
-	var layers []LayerInfo
-	var configLayer LayerInfo
-
-	entries, err := os.ReadDir(modelDir)
-	if err != nil {
-		return fmt.Errorf("failed to read directory: %w", err)
-	}
-
-	// Process all safetensors files
-	for _, entry := range entries {
-		if entry.IsDir() || !strings.HasSuffix(entry.Name(), ".safetensors") {
-			continue
-		}
-
-		stPath := filepath.Join(modelDir, entry.Name())
-
-		// Extract individual tensors from safetensors file
-		extractor, err := safetensors.OpenForExtraction(stPath)
-		if err != nil {
-			return fmt.Errorf("failed to open %s: %w", stPath, err)
-		}
-
-		tensorNames := extractor.ListTensors()
-		quantizeMsg := ""
-		if quantize != "" {
-			quantizeMsg = fmt.Sprintf(", quantizing to %s", quantize)
-		}
-		fn(fmt.Sprintf("importing %s (%d tensors%s)", entry.Name(), len(tensorNames), quantizeMsg))
-
-		for _, tensorName := range tensorNames {
-			td, err := extractor.GetTensor(tensorName)
-			if err != nil {
-				extractor.Close()
-				return fmt.Errorf("failed to get tensor %s: %w", tensorName, err)
-			}
-
-			// Determine if this tensor should be quantized
-			doQuantize := quantize != "" && ShouldQuantizeTensor(tensorName, td.Shape)
-
-			// Store as minimal safetensors format (88 bytes header overhead)
-			// This enables native mmap loading via mlx_load_safetensors
-			// createTensorLayer returns multiple layers if quantizing (weight + scales)
-			newLayers, err := createTensorLayer(td.SafetensorsReader(), tensorName, td.Dtype, td.Shape, doQuantize)
-			if err != nil {
-				extractor.Close()
-				return fmt.Errorf("failed to create layer for %s: %w", tensorName, err)
-			}
-			layers = append(layers, newLayers...)
-		}
-
-		extractor.Close()
-	}
-
-	// Process all JSON config files
-	for _, entry := range entries {
-		if entry.IsDir() || !strings.HasSuffix(entry.Name(), ".json") {
-			continue
-		}
-
-		// Skip the index file as we don't need it after extraction
-		if entry.Name() == "model.safetensors.index.json" {
-			continue
-		}
-
-		cfgPath := entry.Name()
-		fullPath := filepath.Join(modelDir, cfgPath)
-
-		fn(fmt.Sprintf("importing config %s", cfgPath))
-
-		f, err := os.Open(fullPath)
-		if err != nil {
-			return fmt.Errorf("failed to open %s: %w", cfgPath, err)
-		}
-
-		layer, err := createLayer(f, "application/vnd.ollama.image.json", cfgPath)
-		f.Close()
-		if err != nil {
-			return fmt.Errorf("failed to create layer for %s: %w", cfgPath, err)
-		}
-
-		// Use config.json as the config layer
-		if cfgPath == "config.json" {
-			configLayer = layer
-		}
-
-		layers = append(layers, layer)
-	}
-
-	if configLayer.Digest == "" {
-		return fmt.Errorf("config.json not found in %s", modelDir)
-	}
-
-	fn(fmt.Sprintf("writing manifest for %s", modelName))
-
-	if err := writeManifest(modelName, configLayer, layers); err != nil {
-		return fmt.Errorf("failed to write manifest: %w", err)
-	}
-
-	fn(fmt.Sprintf("successfully imported %s with %d layers", modelName, len(layers)))
-	return nil
-}

x/create/create_test.go

@@ -1,752 +0,0 @@
-package create
-
-import (
-	"bytes"
-	"encoding/binary"
-	"encoding/json"
-	"io"
-	"os"
-	"path/filepath"
-	"strings"
-	"testing"
-)
-
-func TestIsTensorModelDir(t *testing.T) {
-	tests := []struct {
-		name     string
-		setup    func(dir string) error
-		expected bool
-	}{
-		{
-			name: "valid diffusers model with model_index.json",
-			setup: func(dir string) error {
-				return os.WriteFile(filepath.Join(dir, "model_index.json"), []byte(`{"_class_name": "FluxPipeline"}`), 0644)
-			},
-			expected: true,
-		},
-		{
-			name: "empty directory",
-			setup: func(dir string) error {
-				return nil
-			},
-			expected: false,
-		},
-		{
-			name: "directory with other files but no model_index.json",
-			setup: func(dir string) error {
-				return os.WriteFile(filepath.Join(dir, "config.json"), []byte(`{}`), 0644)
-			},
-			expected: false,
-		},
-	}
-
-	for _, tt := range tests {
-		t.Run(tt.name, func(t *testing.T) {
-			dir := t.TempDir()
-			if err := tt.setup(dir); err != nil {
-				t.Fatalf("setup failed: %v", err)
-			}
-
-			got := IsTensorModelDir(dir)
-			if got != tt.expected {
-				t.Errorf("IsTensorModelDir() = %v, want %v", got, tt.expected)
-			}
-		})
-	}
-}
-
-func TestIsSafetensorsModelDir(t *testing.T) {
-	tests := []struct {
-		name     string
-		setup    func(dir string) error
-		expected bool
-	}{
-		{
-			name: "valid safetensors model with config.json and .safetensors file",
-			setup: func(dir string) error {
-				if err := os.WriteFile(filepath.Join(dir, "config.json"), []byte(`{"model_type": "gemma3"}`), 0644); err != nil {
-					return err
-				}
-				return os.WriteFile(filepath.Join(dir, "model.safetensors"), []byte("dummy"), 0644)
-			},
-			expected: true,
-		},
-		{
-			name: "config.json only, no safetensors files",
-			setup: func(dir string) error {
-				return os.WriteFile(filepath.Join(dir, "config.json"), []byte(`{}`), 0644)
-			},
-			expected: false,
-		},
-		{
-			name: "safetensors file only, no config.json",
-			setup: func(dir string) error {
-				return os.WriteFile(filepath.Join(dir, "model.safetensors"), []byte("dummy"), 0644)
-			},
-			expected: false,
-		},
-		{
-			name: "empty directory",
-			setup: func(dir string) error {
-				return nil
-			},
-			expected: false,
-		},
-		{
-			name: "multiple safetensors files with config.json",
-			setup: func(dir string) error {
-				if err := os.WriteFile(filepath.Join(dir, "config.json"), []byte(`{}`), 0644); err != nil {
-					return err
-				}
-				if err := os.WriteFile(filepath.Join(dir, "model-00001-of-00002.safetensors"), []byte("dummy"), 0644); err != nil {
-					return err
-				}
-				return os.WriteFile(filepath.Join(dir, "model-00002-of-00002.safetensors"), []byte("dummy"), 0644)
-			},
-			expected: true,
-		},
-	}
-
-	for _, tt := range tests {
-		t.Run(tt.name, func(t *testing.T) {
-			dir := t.TempDir()
-			if err := tt.setup(dir); err != nil {
-				t.Fatalf("setup failed: %v", err)
-			}
-
-			got := IsSafetensorsModelDir(dir)
-			if got != tt.expected {
-				t.Errorf("IsSafetensorsModelDir() = %v, want %v", got, tt.expected)
-			}
-		})
-	}
-}
-
-func TestIsSafetensorsModelDir_NonexistentDir(t *testing.T) {
-	got := IsSafetensorsModelDir("/nonexistent/path/that/does/not/exist")
-	if got != false {
-		t.Errorf("IsSafetensorsModelDir() = %v for nonexistent dir, want false", got)
-	}
-}
-
-// createMinimalSafetensors creates a minimal valid safetensors file with one tensor
-func createMinimalSafetensors(t *testing.T, path string) {
-	t.Helper()
-
-	// Create a minimal safetensors file with a single float32 tensor
-	header := map[string]interface{}{
-		"test_tensor": map[string]interface{}{
-			"dtype":        "F32",
-			"shape":        []int{2, 2},
-			"data_offsets": []int{0, 16}, // 4 float32 values = 16 bytes
-		},
-	}
-	headerJSON, err := json.Marshal(header)
-	if err != nil {
-		t.Fatalf("failed to marshal header: %v", err)
-	}
-
-	// Pad header to 8-byte alignment
-	padding := (8 - len(headerJSON)%8) % 8
-	headerJSON = append(headerJSON, bytes.Repeat([]byte(" "), padding)...)
-
-	// Write file
-	f, err := os.Create(path)
-	if err != nil {
-		t.Fatalf("failed to create file: %v", err)
-	}
-	defer f.Close()
-
-	// Write header size (8 bytes, little endian)
-	if err := binary.Write(f, binary.LittleEndian, uint64(len(headerJSON))); err != nil {
-		t.Fatalf("failed to write header size: %v", err)
-	}
-
-	// Write header
-	if _, err := f.Write(headerJSON); err != nil {
-		t.Fatalf("failed to write header: %v", err)
-	}
-
-	// Write tensor data (16 bytes of zeros for 4 float32 values)
-	if _, err := f.Write(make([]byte, 16)); err != nil {
-		t.Fatalf("failed to write tensor data: %v", err)
-	}
-}
-
-func TestCreateSafetensorsModel(t *testing.T) {
-	dir := t.TempDir()
-
-	// Create config.json
-	configJSON := `{"model_type": "test", "architectures": ["TestModel"]}`
-	if err := os.WriteFile(filepath.Join(dir, "config.json"), []byte(configJSON), 0644); err != nil {
-		t.Fatalf("failed to write config.json: %v", err)
-	}
-
-	// Create a minimal safetensors file
-	createMinimalSafetensors(t, filepath.Join(dir, "model.safetensors"))
-
-	// Track what was created
-	var createdLayers []LayerInfo
-	var manifestWritten bool
-	var manifestModelName string
-	var manifestConfigLayer LayerInfo
-	var manifestLayers []LayerInfo
-	var statusMessages []string
-
-	// Mock callbacks
-	createLayer := func(r io.Reader, mediaType, name string) (LayerInfo, error) {
-		data, err := io.ReadAll(r)
-		if err != nil {
-			return LayerInfo{}, err
-		}
-		layer := LayerInfo{
-			Digest:    "sha256:test",
-			Size:      int64(len(data)),
-			MediaType: mediaType,
-			Name:      name,
-		}
-		createdLayers = append(createdLayers, layer)
-		return layer, nil
-	}
-
-	createTensorLayer := func(r io.Reader, name, dtype string, shape []int32, quantize bool) ([]LayerInfo, error) {
-		data, err := io.ReadAll(r)
-		if err != nil {
-			return nil, err
-		}
-		layer := LayerInfo{
-			Digest:    "sha256:tensor_" + name,
-			Size:      int64(len(data)),
-			MediaType: "application/vnd.ollama.image.tensor",
-			Name:      name,
-		}
-		createdLayers = append(createdLayers, layer)
-		return []LayerInfo{layer}, nil
-	}
-
-	writeManifest := func(modelName string, config LayerInfo, layers []LayerInfo) error {
-		manifestWritten = true
-		manifestModelName = modelName
-		manifestConfigLayer = config
-		manifestLayers = layers
-		return nil
-	}
-
-	progressFn := func(status string) {
-		statusMessages = append(statusMessages, status)
-	}
-
-	// Run CreateSafetensorsModel
-	err := CreateSafetensorsModel("test-model", dir, "", createLayer, createTensorLayer, writeManifest, progressFn)
-	if err != nil {
-		t.Fatalf("CreateSafetensorsModel failed: %v", err)
-	}
-
-	// Verify manifest was written
-	if !manifestWritten {
-		t.Error("manifest was not written")
-	}
-
-	if manifestModelName != "test-model" {
-		t.Errorf("manifest model name = %q, want %q", manifestModelName, "test-model")
-	}
-
-	// Verify config layer was set
-	if manifestConfigLayer.Name != "config.json" {
-		t.Errorf("config layer name = %q, want %q", manifestConfigLayer.Name, "config.json")
-	}
-
-	// Verify we have at least one tensor and one config layer
-	hasTensor := false
-	hasConfig := false
-	for _, layer := range manifestLayers {
-		if layer.Name == "test_tensor" {
-			hasTensor = true
-		}
-		if layer.Name == "config.json" {
-			hasConfig = true
-		}
-	}
-
-	if !hasTensor {
-		t.Error("no tensor layer found in manifest")
-	}
-	if !hasConfig {
-		t.Error("no config layer found in manifest")
-	}
-
-	// Verify status messages were sent
-	if len(statusMessages) == 0 {
-		t.Error("no status messages received")
-	}
-}
-
-func TestCreateSafetensorsModel_NoConfigJson(t *testing.T) {
-	dir := t.TempDir()
-
-	// Create only a safetensors file, no config.json
-	createMinimalSafetensors(t, filepath.Join(dir, "model.safetensors"))
-
-	// Mock callbacks (minimal)
-	createLayer := func(r io.Reader, mediaType, name string) (LayerInfo, error) {
-		io.ReadAll(r)
-		return LayerInfo{Name: name}, nil
-	}
-	createTensorLayer := func(r io.Reader, name, dtype string, shape []int32, quantize bool) ([]LayerInfo, error) {
-		io.ReadAll(r)
-		return []LayerInfo{{Name: name}}, nil
-	}
-	writeManifest := func(modelName string, config LayerInfo, layers []LayerInfo) error {
-		return nil
-	}
-	progressFn := func(status string) {}
-
-	err := CreateSafetensorsModel("test-model", dir, "", createLayer, createTensorLayer, writeManifest, progressFn)
-	if err == nil {
-		t.Error("expected error for missing config.json, got nil")
-	}
-}
-
-func TestCreateSafetensorsModel_EmptyDir(t *testing.T) {
-	dir := t.TempDir()
-
-	// Mock callbacks
-	createLayer := func(r io.Reader, mediaType, name string) (LayerInfo, error) {
-		return LayerInfo{}, nil
-	}
-	createTensorLayer := func(r io.Reader, name, dtype string, shape []int32, quantize bool) ([]LayerInfo, error) {
-		return []LayerInfo{{}}, nil
-	}
-	writeManifest := func(modelName string, config LayerInfo, layers []LayerInfo) error {
-		return nil
-	}
-	progressFn := func(status string) {}
-
-	err := CreateSafetensorsModel("test-model", dir, "", createLayer, createTensorLayer, writeManifest, progressFn)
-	if err == nil {
-		t.Error("expected error for empty directory, got nil")
-	}
-}
-
-func TestCreateSafetensorsModel_SkipsIndexJson(t *testing.T) {
-	dir := t.TempDir()
-
-	// Create config.json
-	if err := os.WriteFile(filepath.Join(dir, "config.json"), []byte(`{}`), 0644); err != nil {
-		t.Fatalf("failed to write config.json: %v", err)
-	}
-
-	// Create model.safetensors.index.json (should be skipped)
-	indexJSON := `{"metadata": {"total_size": 100}, "weight_map": {}}`
-	if err := os.WriteFile(filepath.Join(dir, "model.safetensors.index.json"), []byte(indexJSON), 0644); err != nil {
-		t.Fatalf("failed to write index.json: %v", err)
-	}
-
-	// Create a minimal safetensors file
-	createMinimalSafetensors(t, filepath.Join(dir, "model.safetensors"))
-
-	var configNames []string
-
-	createLayer := func(r io.Reader, mediaType, name string) (LayerInfo, error) {
-		io.ReadAll(r)
-		configNames = append(configNames, name)
-		return LayerInfo{Name: name, Digest: "sha256:test"}, nil
-	}
-	createTensorLayer := func(r io.Reader, name, dtype string, shape []int32, quantize bool) ([]LayerInfo, error) {
-		io.ReadAll(r)
-		return []LayerInfo{{Name: name}}, nil
-	}
-	writeManifest := func(modelName string, config LayerInfo, layers []LayerInfo) error {
-		return nil
-	}
-	progressFn := func(status string) {}
-
-	err := CreateSafetensorsModel("test-model", dir, "", createLayer, createTensorLayer, writeManifest, progressFn)
-	if err != nil {
-		t.Fatalf("CreateSafetensorsModel failed: %v", err)
-	}
-
-	// Verify model.safetensors.index.json was not included
-	for _, name := range configNames {
-		if name == "model.safetensors.index.json" {
-			t.Error("model.safetensors.index.json should have been skipped")
-		}
-	}
-}
-
-func TestResolveManifestPath(t *testing.T) {
-	tests := []struct {
-		name      string
-		modelName string
-		wantParts []string // Parts that should appear in the path
-	}{
-		{
-			name:      "simple model name",
-			modelName: "llama2",
-			wantParts: []string{"registry.ollama.ai", "library", "llama2", "latest"},
-		},
-		{
-			name:      "model name with tag",
-			modelName: "llama2:7b",
-			wantParts: []string{"registry.ollama.ai", "library", "llama2", "7b"},
-		},
-		{
-			name:      "model name with namespace",
-			modelName: "myuser/mymodel",
-			wantParts: []string{"registry.ollama.ai", "myuser", "mymodel", "latest"},
-		},
-		{
-			name:      "model name with namespace and tag",
-			modelName: "myuser/mymodel:v1",
-			wantParts: []string{"registry.ollama.ai", "myuser", "mymodel", "v1"},
-		},
-		{
-			name:      "fully qualified model name",
-			modelName: "registry.example.com/namespace/model:tag",
-			wantParts: []string{"registry.example.com", "namespace", "model", "tag"},
-		},
-	}
-
-	for _, tt := range tests {
-		t.Run(tt.name, func(t *testing.T) {
-			got := resolveManifestPath(tt.modelName)
-
-			for _, part := range tt.wantParts {
-				if !strings.Contains(got, part) {
-					t.Errorf("resolveManifestPath(%q) = %q, missing part %q", tt.modelName, got, part)
-				}
-			}
-		})
-	}
-}
-
-func TestLayerInfo(t *testing.T) {
-	layer := LayerInfo{
-		Digest:    "sha256:abc123",
-		Size:      1024,
-		MediaType: "application/vnd.ollama.image.tensor",
-		Name:      "model.weight",
-	}
-
-	if layer.Digest != "sha256:abc123" {
-		t.Errorf("Digest = %q, want %q", layer.Digest, "sha256:abc123")
-	}
-	if layer.Size != 1024 {
-		t.Errorf("Size = %d, want %d", layer.Size, 1024)
-	}
-	if layer.MediaType != "application/vnd.ollama.image.tensor" {
-		t.Errorf("MediaType = %q, want %q", layer.MediaType, "application/vnd.ollama.image.tensor")
-	}
-	if layer.Name != "model.weight" {
-		t.Errorf("Name = %q, want %q", layer.Name, "model.weight")
-	}
-}
-
-func TestModelConfig(t *testing.T) {
-	config := ModelConfig{
-		ModelFormat:  "safetensors",
-		Capabilities: []string{"completion", "chat"},
-	}
-
-	if config.ModelFormat != "safetensors" {
-		t.Errorf("ModelFormat = %q, want %q", config.ModelFormat, "safetensors")
-	}
-	if len(config.Capabilities) != 2 {
-		t.Errorf("Capabilities length = %d, want %d", len(config.Capabilities), 2)
-	}
-}
-
-func TestManifest(t *testing.T) {
-	manifest := Manifest{
-		SchemaVersion: 2,
-		MediaType:     "application/vnd.oci.image.manifest.v1+json",
-		Config: ManifestLayer{
-			MediaType: "application/vnd.docker.container.image.v1+json",
-			Digest:    "sha256:config",
-			Size:      100,
-		},
-		Layers: []ManifestLayer{
-			{
-				MediaType: "application/vnd.ollama.image.tensor",
-				Digest:    "sha256:layer1",
-				Size:      1000,
-				Name:      "weight.bin",
-			},
-		},
-	}
-
-	if manifest.SchemaVersion != 2 {
-		t.Errorf("SchemaVersion = %d, want %d", manifest.SchemaVersion, 2)
-	}
-	if manifest.Config.Digest != "sha256:config" {
-		t.Errorf("Config.Digest = %q, want %q", manifest.Config.Digest, "sha256:config")
-	}
-	if len(manifest.Layers) != 1 {
-		t.Errorf("Layers length = %d, want %d", len(manifest.Layers), 1)
-	}
-	if manifest.Layers[0].Name != "weight.bin" {
-		t.Errorf("Layers[0].Name = %q, want %q", manifest.Layers[0].Name, "weight.bin")
-	}
-}
-
-func TestShouldQuantize(t *testing.T) {
-	tests := []struct {
-		name      string
-		tensor    string
-		component string
-		want      bool
-	}{
-		// VAE component should never be quantized
-		{"vae weight", "decoder.weight", "vae", false},
-		{"vae bias", "decoder.bias", "vae", false},
-
-		// Embeddings should not be quantized
-		{"embedding weight", "embed_tokens.weight", "", false},
-		{"embedding in name", "token_embedding.weight", "", false},
-
-		// Norms should not be quantized
-		{"layer norm", "layer_norm.weight", "", false},
-		{"rms norm", "rms_norm.weight", "", false},
-		{"ln prefix", "ln_1.weight", "", false},
-		{"layernorm in name", "input_layernorm.weight", "", false},
-
-		// Biases should not be quantized
-		{"bias tensor", "attention.bias", "", false},
-		{"proj bias", "o_proj.bias", "", false},
-
-		// Linear weights should be quantized
-		{"linear weight", "q_proj.weight", "", true},
-		{"attention weight", "self_attn.weight", "", true},
-		{"mlp weight", "mlp.gate_proj.weight", "", true},
-
-		// Transformer component weights should be quantized
-		{"transformer weight", "layers.0.weight", "transformer", true},
-		{"text_encoder weight", "encoder.weight", "text_encoder", true},
-	}
-
-	for _, tt := range tests {
-		t.Run(tt.name, func(t *testing.T) {
-			got := ShouldQuantize(tt.tensor, tt.component)
-			if got != tt.want {
-				t.Errorf("ShouldQuantize(%q, %q) = %v, want %v", tt.tensor, tt.component, got, tt.want)
-			}
-		})
-	}
-}
-
-func TestShouldQuantizeTensor(t *testing.T) {
-	tests := []struct {
-		name   string
-		tensor string
-		shape  []int32
-		want   bool
-	}{
-		// 2D tensors with sufficient size should be quantized
-		{"large 2D weight", "q_proj.weight", []int32{4096, 4096}, true},
-		{"medium 2D weight", "small_proj.weight", []int32{128, 128}, true},
-
-		// Small tensors should not be quantized (< 1024 elements)
-		{"tiny 2D weight", "tiny.weight", []int32{16, 16}, false},
-		{"small 2D weight", "small.weight", []int32{31, 31}, false},
-
-		// 1D tensors should not be quantized
-		{"1D tensor", "layer_norm.weight", []int32{4096}, false},
-
-		// 3D+ tensors should not be quantized
-		{"3D tensor", "conv.weight", []int32{64, 64, 3}, false},
-		{"4D tensor", "conv2d.weight", []int32{64, 64, 3, 3}, false},
-
-		// Embeddings should not be quantized regardless of shape
-		{"embedding 2D", "embed_tokens.weight", []int32{32000, 4096}, false},
-
-		// Norms should not be quantized regardless of shape
-		{"norm 2D", "layer_norm.weight", []int32{4096, 1}, false},
-
-		// Biases should not be quantized
-		{"bias 2D", "proj.bias", []int32{4096, 1}, false},
-	}
-
-	for _, tt := range tests {
-		t.Run(tt.name, func(t *testing.T) {
-			got := ShouldQuantizeTensor(tt.tensor, tt.shape)
-			if got != tt.want {
-				t.Errorf("ShouldQuantizeTensor(%q, %v) = %v, want %v", tt.tensor, tt.shape, got, tt.want)
-			}
-		})
-	}
-}
-
-func TestCreateSafetensorsModel_WithQuantize(t *testing.T) {
-	dir := t.TempDir()
-
-	// Create config.json
-	configJSON := `{"model_type": "test", "architectures": ["TestModel"]}`
-	if err := os.WriteFile(filepath.Join(dir, "config.json"), []byte(configJSON), 0644); err != nil {
-		t.Fatalf("failed to write config.json: %v", err)
-	}
-
-	// Create a minimal safetensors file
-	createMinimalSafetensors(t, filepath.Join(dir, "model.safetensors"))
-
-	var quantizeRequested []bool
-
-	createLayer := func(r io.Reader, mediaType, name string) (LayerInfo, error) {
-		io.ReadAll(r)
-		return LayerInfo{Name: name, Digest: "sha256:test"}, nil
-	}
-
-	createTensorLayer := func(r io.Reader, name, dtype string, shape []int32, quantize bool) ([]LayerInfo, error) {
-		io.ReadAll(r)
-		quantizeRequested = append(quantizeRequested, quantize)
-		return []LayerInfo{{Name: name}}, nil
-	}
-
-	writeManifest := func(modelName string, config LayerInfo, layers []LayerInfo) error {
-		return nil
-	}
-
-	progressFn := func(status string) {}
-
-	// Run with quantize enabled
-	err := CreateSafetensorsModel("test-model", dir, "fp8", createLayer, createTensorLayer, writeManifest, progressFn)
-	if err != nil {
-		t.Fatalf("CreateSafetensorsModel failed: %v", err)
-	}
-
-	// Verify quantize was passed to callback (will be false for small test tensor)
-	if len(quantizeRequested) == 0 {
-		t.Error("no tensors processed")
-	}
-}
-
-// createMinimalImageGenModel creates a minimal diffusers-style model directory
-func createMinimalImageGenModel(t *testing.T, dir string) {
-	t.Helper()
-
-	// Create model_index.json
-	modelIndex := `{"_class_name": "FluxPipeline", "_diffusers_version": "0.30.0"}`
-	if err := os.WriteFile(filepath.Join(dir, "model_index.json"), []byte(modelIndex), 0644); err != nil {
-		t.Fatalf("failed to write model_index.json: %v", err)
-	}
-
-	// Create transformer directory with a safetensors file
-	transformerDir := filepath.Join(dir, "transformer")
-	if err := os.MkdirAll(transformerDir, 0755); err != nil {
-		t.Fatalf("failed to create transformer dir: %v", err)
-	}
-	createMinimalSafetensors(t, filepath.Join(transformerDir, "model.safetensors"))
-
-	// Create transformer config
-	transformerConfig := `{"hidden_size": 3072}`
-	if err := os.WriteFile(filepath.Join(transformerDir, "config.json"), []byte(transformerConfig), 0644); err != nil {
-		t.Fatalf("failed to write transformer config: %v", err)
-	}
-}
-
-func TestCreateImageGenModel(t *testing.T) {
-	dir := t.TempDir()
-	createMinimalImageGenModel(t, dir)
-
-	var manifestWritten bool
-	var manifestModelName string
-	var statusMessages []string
-
-	createLayer := func(r io.Reader, mediaType, name string) (LayerInfo, error) {
-		io.ReadAll(r)
-		return LayerInfo{Name: name, Digest: "sha256:test"}, nil
-	}
-
-	createTensorLayer := func(r io.Reader, name, dtype string, shape []int32, quantize bool) ([]LayerInfo, error) {
-		io.ReadAll(r)
-		return []LayerInfo{{Name: name, Digest: "sha256:tensor"}}, nil
-	}
-
-	writeManifest := func(modelName string, config LayerInfo, layers []LayerInfo) error {
-		manifestWritten = true
-		manifestModelName = modelName
-		return nil
-	}
-
-	progressFn := func(status string) {
-		statusMessages = append(statusMessages, status)
-	}
-
-	err := CreateImageGenModel("test-imagegen", dir, "", createLayer, createTensorLayer, writeManifest, progressFn)
-	if err != nil {
-		t.Fatalf("CreateImageGenModel failed: %v", err)
-	}
-
-	if !manifestWritten {
-		t.Error("manifest was not written")
-	}
-
-	if manifestModelName != "test-imagegen" {
-		t.Errorf("manifest model name = %q, want %q", manifestModelName, "test-imagegen")
-	}
-
-	if len(statusMessages) == 0 {
-		t.Error("no status messages received")
-	}
-}
-
-func TestCreateImageGenModel_NoModelIndex(t *testing.T) {
-	dir := t.TempDir()
-
-	// Create only transformer without model_index.json
-	transformerDir := filepath.Join(dir, "transformer")
-	if err := os.MkdirAll(transformerDir, 0755); err != nil {
-		t.Fatalf("failed to create transformer dir: %v", err)
-	}
-	createMinimalSafetensors(t, filepath.Join(transformerDir, "model.safetensors"))
-
-	createLayer := func(r io.Reader, mediaType, name string) (LayerInfo, error) {
-		io.ReadAll(r)
-		return LayerInfo{Name: name}, nil
-	}
-	createTensorLayer := func(r io.Reader, name, dtype string, shape []int32, quantize bool) ([]LayerInfo, error) {
-		io.ReadAll(r)
-		return []LayerInfo{{Name: name}}, nil
-	}
-	writeManifest := func(modelName string, config LayerInfo, layers []LayerInfo) error {
-		return nil
-	}
-	progressFn := func(status string) {}
-
-	err := CreateImageGenModel("test-imagegen", dir, "", createLayer, createTensorLayer, writeManifest, progressFn)
-	if err == nil {
-		t.Error("expected error for missing model_index.json, got nil")
-	}
-}
-
-func TestCreateImageGenModel_WithQuantize(t *testing.T) {
-	dir := t.TempDir()
-	createMinimalImageGenModel(t, dir)
-
-	var quantizeRequested []bool
-
-	createLayer := func(r io.Reader, mediaType, name string) (LayerInfo, error) {
-		io.ReadAll(r)
-		return LayerInfo{Name: name, Digest: "sha256:test"}, nil
-	}
-
-	createTensorLayer := func(r io.Reader, name, dtype string, shape []int32, quantize bool) ([]LayerInfo, error) {
-		io.ReadAll(r)
-		quantizeRequested = append(quantizeRequested, quantize)
-		return []LayerInfo{{Name: name}}, nil
-	}
-
-	writeManifest := func(modelName string, config LayerInfo, layers []LayerInfo) error {
-		return nil
-	}
-
-	progressFn := func(status string) {}
-
-	err := CreateImageGenModel("test-imagegen", dir, "fp8", createLayer, createTensorLayer, writeManifest, progressFn)
-	if err != nil {
-		t.Fatalf("CreateImageGenModel failed: %v", err)
-	}
-
-	if len(quantizeRequested) == 0 {
-		t.Error("no tensors processed")
-	}
-}

x/imagegen/client/create.go

@@ -0,0 +1,190 @@
+// Package client provides client-side model creation for tensor-based models.
+//
+// This package is in x/ because the tensor model storage format is under development.
+// It also exists to break an import cycle: server imports x/imagegen, so x/imagegen
+// cannot import server. This sub-package can import server because server doesn't
+// import it.
+//
+// TODO (jmorganca): This is temporary. When tensor models are promoted to production:
+//  1. Add proper API endpoints for tensor model creation
+//  2. Move tensor extraction to server-side
+//  3. Remove this package
+//  4. Follow the same client→server pattern as regular model creation
+package client
+
+import (
+	"bytes"
+	"encoding/json"
+	"fmt"
+	"io"
+
+	"github.com/ollama/ollama/progress"
+	"github.com/ollama/ollama/server"
+	"github.com/ollama/ollama/types/model"
+	"github.com/ollama/ollama/x/imagegen"
+)
+
+// MinOllamaVersion is the minimum Ollama version required for image generation models.
+const MinOllamaVersion = "0.14.0"
+
+// CreateModel imports a tensor-based model from a local directory.
+// This creates blobs and manifest directly on disk, bypassing the HTTP API.
+// If quantize is "fp8", weights will be quantized to mxfp8 format during import.
+//
+// TODO (jmorganca): Replace with API-based creation when promoted to production.
+func CreateModel(modelName, modelDir, quantize string, p *progress.Progress) error {
+	if !imagegen.IsTensorModelDir(modelDir) {
+		return fmt.Errorf("%s is not an image generation model directory (model_index.json not found)", modelDir)
+	}
+
+	status := "importing image generation model"
+	spinner := progress.NewSpinner(status)
+	p.Add("imagegen", spinner)
+
+	// Create layer callback for config files
+	createLayer := func(r io.Reader, mediaType, name string) (imagegen.LayerInfo, error) {
+		layer, err := server.NewLayer(r, mediaType)
+		if err != nil {
+			return imagegen.LayerInfo{}, err
+		}
+		layer.Name = name
+
+		return imagegen.LayerInfo{
+			Digest:    layer.Digest,
+			Size:      layer.Size,
+			MediaType: layer.MediaType,
+			Name:      name,
+		}, nil
+	}
+
+	// Create tensor layer callback for individual tensors
+	// name is path-style: "component/tensor_name"
+	// When quantize is true, returns multiple layers (weight + scales)
+	createTensorLayer := func(r io.Reader, name, dtype string, shape []int32, doQuantize bool) ([]imagegen.LayerInfo, error) {
+		if doQuantize {
+			// Check if quantization is supported
+			if !QuantizeSupported() {
+				return nil, fmt.Errorf("quantization requires MLX support")
+			}
+
+			// Quantize the tensor (affine mode returns weight, scales, qbiases)
+			qweightData, scalesData, qbiasData, _, _, _, err := quantizeTensor(r, name, dtype, shape)
+			if err != nil {
+				return nil, fmt.Errorf("failed to quantize %s: %w", name, err)
+			}
+
+			// Create layer for quantized weight
+			weightLayer, err := server.NewLayer(bytes.NewReader(qweightData), server.MediaTypeImageTensor)
+			if err != nil {
+				return nil, err
+			}
+
+			// Create layer for scales (use _scale suffix convention)
+			scalesLayer, err := server.NewLayer(bytes.NewReader(scalesData), server.MediaTypeImageTensor)
+			if err != nil {
+				return nil, err
+			}
+
+			layers := []imagegen.LayerInfo{
+				{
+					Digest:    weightLayer.Digest,
+					Size:      weightLayer.Size,
+					MediaType: weightLayer.MediaType,
+					Name:      name, // Keep original name for weight
+				},
+				{
+					Digest:    scalesLayer.Digest,
+					Size:      scalesLayer.Size,
+					MediaType: scalesLayer.MediaType,
+					Name:      name + "_scale", // Add _scale suffix
+				},
+			}
+
+			// Add qbiases layer if present (affine mode)
+			if qbiasData != nil {
+				qbiasLayer, err := server.NewLayer(bytes.NewReader(qbiasData), server.MediaTypeImageTensor)
+				if err != nil {
+					return nil, err
+				}
+				layers = append(layers, imagegen.LayerInfo{
+					Digest:    qbiasLayer.Digest,
+					Size:      qbiasLayer.Size,
+					MediaType: qbiasLayer.MediaType,
+					Name:      name + "_qbias", // Add _qbias suffix
+				})
+			}
+
+			return layers, nil
+		}
+
+		// Non-quantized path: just create a single layer
+		layer, err := server.NewLayer(r, server.MediaTypeImageTensor)
+		if err != nil {
+			return nil, err
+		}
+
+		return []imagegen.LayerInfo{
+			{
+				Digest:    layer.Digest,
+				Size:      layer.Size,
+				MediaType: layer.MediaType,
+				Name:      name,
+			},
+		}, nil
+	}
+
+	// Create manifest writer callback
+	writeManifest := func(modelName string, config imagegen.LayerInfo, layers []imagegen.LayerInfo) error {
+		name := model.ParseName(modelName)
+		if !name.IsValid() {
+			return fmt.Errorf("invalid model name: %s", modelName)
+		}
+
+		// Create a proper config blob with version requirement
+		configData := model.ConfigV2{
+			ModelFormat:  "safetensors",
+			Capabilities: []string{"image"},
+			Requires:     MinOllamaVersion,
+		}
+		configJSON, err := json.Marshal(configData)
+		if err != nil {
+			return fmt.Errorf("failed to marshal config: %w", err)
+		}
+
+		// Create config layer blob
+		configLayer, err := server.NewLayer(bytes.NewReader(configJSON), "application/vnd.docker.container.image.v1+json")
+		if err != nil {
+			return fmt.Errorf("failed to create config layer: %w", err)
+		}
+
+		// Convert LayerInfo to server.Layer (include the original model_index.json in layers)
+		serverLayers := make([]server.Layer, len(layers))
+		for i, l := range layers {
+			serverLayers[i] = server.Layer{
+				MediaType: l.MediaType,
+				Digest:    l.Digest,
+				Size:      l.Size,
+				Name:      l.Name,
+			}
+		}
+
+		return server.WriteManifest(name, configLayer, serverLayers)
+	}
+
+	// Progress callback
+	progressFn := func(msg string) {
+		spinner.Stop()
+		status = msg
+		spinner = progress.NewSpinner(status)
+		p.Add("imagegen", spinner)
+	}
+
+	err := imagegen.CreateModel(modelName, modelDir, quantize, createLayer, createTensorLayer, writeManifest, progressFn)
+	spinner.Stop()
+	if err != nil {
+		return err
+	}
+
+	fmt.Printf("Created image generation model '%s'\n", modelName)
+	return nil
+}

x/imagegen/create.go

@@ -1,4 +1,4 @@
-package create
+package imagegen
 
 import (
 	"bytes"
@@ -12,11 +12,37 @@ import (
 	"github.com/ollama/ollama/x/imagegen/safetensors"
 )
 
-// CreateImageGenModel imports an image generation model from a directory.
+// 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.
 // If quantize is "fp8", linear weights in transformer/text_encoder are quantized to mxfp8 format.
 // Layer creation and manifest writing are done via callbacks to avoid import cycles.
-func CreateImageGenModel(modelName, modelDir, quantize string, createLayer LayerCreator, createTensorLayer QuantizingTensorLayerCreator, writeManifest ManifestWriter, fn func(status string)) error {
+func CreateModel(modelName, modelDir, quantize string, createLayer LayerCreator, createTensorLayer QuantizingTensorLayerCreator, writeManifest ManifestWriter, fn func(status string)) error {
 	var layers []LayerInfo
 	var configLayer LayerInfo
 	var totalParams int64 // Count parameters from original tensor shapes

x/imagegen/quantize.go

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

x/server/show.go

@@ -1,271 +0,0 @@
-package server
-
-import (
-	"encoding/binary"
-	"encoding/json"
-	"fmt"
-	"io"
-	"os"
-	"strings"
-
-	"github.com/ollama/ollama/api"
-	"github.com/ollama/ollama/x/imagegen"
-)
-
-// modelConfig represents the HuggingFace config.json structure
-type modelConfig struct {
-	Architectures         []string `json:"architectures"`
-	ModelType             string   `json:"model_type"`
-	HiddenSize            int      `json:"hidden_size"`
-	NumHiddenLayers       int      `json:"num_hidden_layers"`
-	MaxPositionEmbeddings int      `json:"max_position_embeddings"`
-	IntermediateSize      int      `json:"intermediate_size"`
-	NumAttentionHeads     int      `json:"num_attention_heads"`
-	NumKeyValueHeads      int      `json:"num_key_value_heads"`
-	VocabSize             int      `json:"vocab_size"`
-	RMSNormEps            float64  `json:"rms_norm_eps"`
-	RopeTheta             float64  `json:"rope_theta"`
-	TorchDtype            string   `json:"torch_dtype"`
-	TextConfig            *struct {
-		HiddenSize            int `json:"hidden_size"`
-		MaxPositionEmbeddings int `json:"max_position_embeddings"`
-		NumHiddenLayers       int `json:"num_hidden_layers"`
-	} `json:"text_config"`
-}
-
-// GetSafetensorsLLMInfo extracts model information from safetensors LLM models.
-// It reads the config.json layer and returns a map compatible with GGML's KV format.
-func GetSafetensorsLLMInfo(modelName string) (map[string]any, error) {
-	manifest, err := imagegen.LoadManifest(modelName)
-	if err != nil {
-		return nil, fmt.Errorf("failed to load manifest: %w", err)
-	}
-
-	var config modelConfig
-	if err := manifest.ReadConfigJSON("config.json", &config); err != nil {
-		return nil, fmt.Errorf("failed to read config.json: %w", err)
-	}
-
-	// Calculate total tensor bytes from manifest layers
-	var totalBytes int64
-	var tensorCount int64
-	for _, layer := range manifest.Manifest.Layers {
-		if layer.MediaType == "application/vnd.ollama.image.tensor" {
-			totalBytes += layer.Size
-			tensorCount++
-		}
-	}
-
-	return buildModelInfo(config, totalBytes, tensorCount), nil
-}
-
-// buildModelInfo constructs the model info map from config and tensor stats.
-// This is separated for testability.
-func buildModelInfo(config modelConfig, totalTensorBytes, tensorCount int64) map[string]any {
-	// Determine architecture
-	arch := config.ModelType
-	if arch == "" && len(config.Architectures) > 0 {
-		// Convert HuggingFace architecture name to Ollama format
-		// e.g., "Gemma3ForCausalLM" -> "gemma3"
-		hfArch := config.Architectures[0]
-		arch = strings.ToLower(hfArch)
-		arch = strings.TrimSuffix(arch, "forcausallm")
-		arch = strings.TrimSuffix(arch, "forconditionalgeneration")
-	}
-
-	// Use text_config values if they exist (for multimodal models)
-	hiddenSize := config.HiddenSize
-	maxPosEmbed := config.MaxPositionEmbeddings
-	numLayers := config.NumHiddenLayers
-
-	if config.TextConfig != nil {
-		if config.TextConfig.HiddenSize > 0 {
-			hiddenSize = config.TextConfig.HiddenSize
-		}
-		if config.TextConfig.MaxPositionEmbeddings > 0 {
-			maxPosEmbed = config.TextConfig.MaxPositionEmbeddings
-		}
-		if config.TextConfig.NumHiddenLayers > 0 {
-			numLayers = config.TextConfig.NumHiddenLayers
-		}
-	}
-
-	// Get dtype to determine bytes per parameter for count calculation
-	dtype := config.TorchDtype
-
-	// Determine bytes per parameter based on dtype
-	var bytesPerParam int64 = 2 // default to float16/bfloat16
-	switch strings.ToLower(dtype) {
-	case "float32":
-		bytesPerParam = 4
-	case "float16", "bfloat16":
-		bytesPerParam = 2
-	case "int8", "uint8":
-		bytesPerParam = 1
-	}
-
-	// Subtract safetensors header overhead (88 bytes per tensor file)
-	// Each tensor is stored as a minimal safetensors file
-	totalBytes := totalTensorBytes - tensorCount*88
-
-	paramCount := totalBytes / bytesPerParam
-
-	info := map[string]any{
-		"general.architecture": arch,
-	}
-
-	if maxPosEmbed > 0 {
-		info[fmt.Sprintf("%s.context_length", arch)] = maxPosEmbed
-	}
-
-	if hiddenSize > 0 {
-		info[fmt.Sprintf("%s.embedding_length", arch)] = hiddenSize
-	}
-
-	if numLayers > 0 {
-		info[fmt.Sprintf("%s.block_count", arch)] = numLayers
-	}
-
-	if config.NumAttentionHeads > 0 {
-		info[fmt.Sprintf("%s.attention.head_count", arch)] = config.NumAttentionHeads
-	}
-
-	if config.NumKeyValueHeads > 0 {
-		info[fmt.Sprintf("%s.attention.head_count_kv", arch)] = config.NumKeyValueHeads
-	}
-
-	if config.IntermediateSize > 0 {
-		info[fmt.Sprintf("%s.feed_forward_length", arch)] = config.IntermediateSize
-	}
-
-	if config.VocabSize > 0 {
-		info[fmt.Sprintf("%s.vocab_size", arch)] = config.VocabSize
-	}
-
-	if paramCount > 0 {
-		info["general.parameter_count"] = paramCount
-	}
-
-	return info
-}
-
-// GetSafetensorsTensorInfo extracts tensor information from safetensors model layers.
-// Each tensor is stored as a minimal safetensors file with an 88-byte header containing metadata.
-func GetSafetensorsTensorInfo(modelName string) ([]api.Tensor, error) {
-	manifest, err := imagegen.LoadManifest(modelName)
-	if err != nil {
-		return nil, fmt.Errorf("failed to load manifest: %w", err)
-	}
-
-	return getTensorInfoFromManifest(manifest)
-}
-
-// getTensorInfoFromManifest extracts tensor info from a manifest.
-// This is separated for testability.
-func getTensorInfoFromManifest(manifest *imagegen.ModelManifest) ([]api.Tensor, error) {
-	var tensors []api.Tensor
-
-	for _, layer := range manifest.Manifest.Layers {
-		if layer.MediaType != "application/vnd.ollama.image.tensor" {
-			continue
-		}
-
-		// Read the safetensors header from the blob
-		blobPath := manifest.BlobPath(layer.Digest)
-		info, err := readSafetensorsHeader(blobPath)
-		if err != nil {
-			// Skip tensors we can't read
-			continue
-		}
-
-		// Convert shape from int to uint64
-		shape := make([]uint64, len(info.Shape))
-		for i, s := range info.Shape {
-			shape[i] = uint64(s)
-		}
-
-		tensors = append(tensors, api.Tensor{
-			Name:  layer.Name,
-			Type:  info.Dtype,
-			Shape: shape,
-		})
-	}
-
-	return tensors, nil
-}
-
-// GetSafetensorsDtype returns the torch_dtype from config.json for a safetensors model.
-func GetSafetensorsDtype(modelName string) (string, error) {
-	manifest, err := imagegen.LoadManifest(modelName)
-	if err != nil {
-		return "", fmt.Errorf("failed to load manifest: %w", err)
-	}
-
-	var cfg struct {
-		TorchDtype string `json:"torch_dtype"`
-	}
-	if err := manifest.ReadConfigJSON("config.json", &cfg); err != nil {
-		return "", fmt.Errorf("failed to read config.json: %w", err)
-	}
-
-	return cfg.TorchDtype, nil
-}
-
-// safetensorsTensorInfo holds metadata about a tensor from a safetensors header
-type safetensorsTensorInfo struct {
-	Dtype string  `json:"dtype"`
-	Shape []int64 `json:"shape"`
-}
-
-// readSafetensorsHeader reads the JSON header from a safetensors file to get tensor metadata.
-// Safetensors format: 8-byte header size (little endian) + JSON header + tensor data
-func readSafetensorsHeader(path string) (*safetensorsTensorInfo, error) {
-	f, err := os.Open(path)
-	if err != nil {
-		return nil, err
-	}
-	defer f.Close()
-
-	return parseSafetensorsHeader(f)
-}
-
-// parseSafetensorsHeader parses a safetensors header from a reader.
-// This is separated for testability.
-func parseSafetensorsHeader(r io.Reader) (*safetensorsTensorInfo, error) {
-	// Read header size (8 bytes, little endian)
-	var headerSize uint64
-	if err := binary.Read(r, binary.LittleEndian, &headerSize); err != nil {
-		return nil, fmt.Errorf("failed to read header size: %w", err)
-	}
-
-	// Sanity check - header shouldn't be too large
-	if headerSize > 1024*1024 {
-		return nil, fmt.Errorf("header size too large: %d", headerSize)
-	}
-
-	// Read header JSON
-	headerBytes := make([]byte, headerSize)
-	if _, err := io.ReadFull(r, headerBytes); err != nil {
-		return nil, fmt.Errorf("failed to read header: %w", err)
-	}
-
-	// Parse as map of tensor name -> info
-	var header map[string]json.RawMessage
-	if err := json.Unmarshal(headerBytes, &header); err != nil {
-		return nil, fmt.Errorf("failed to parse header: %w", err)
-	}
-
-	// Find the first (and should be only) tensor entry
-	for name, raw := range header {
-		if name == "__metadata__" {
-			continue
-		}
-		var info safetensorsTensorInfo
-		if err := json.Unmarshal(raw, &info); err != nil {
-			return nil, fmt.Errorf("failed to parse tensor info: %w", err)
-		}
-		return &info, nil
-	}
-
-	return nil, fmt.Errorf("no tensor found in header")
-}

x/server/show_test.go

@@ -1,605 +0,0 @@
-package server
-
-import (
-	"bytes"
-	"encoding/binary"
-	"encoding/json"
-	"os"
-	"path/filepath"
-	"testing"
-
-	"github.com/ollama/ollama/x/imagegen"
-)
-
-func TestBuildModelInfo(t *testing.T) {
-	tests := []struct {
-		name             string
-		config           modelConfig
-		totalTensorBytes int64
-		tensorCount      int64
-		wantArch         string
-		wantContextLen   int
-		wantEmbedLen     int
-		wantBlockCount   int
-		wantParamCount   int64
-	}{
-		{
-			name: "gemma3 model with model_type",
-			config: modelConfig{
-				ModelType:             "gemma3",
-				HiddenSize:            2560,
-				NumHiddenLayers:       34,
-				MaxPositionEmbeddings: 131072,
-				IntermediateSize:      10240,
-				NumAttentionHeads:     8,
-				NumKeyValueHeads:      4,
-				VocabSize:             262144,
-				TorchDtype:            "bfloat16",
-			},
-			totalTensorBytes: 8_600_000_088, // ~4.3B params * 2 bytes + 88 bytes header
-			tensorCount:      1,
-			wantArch:         "gemma3",
-			wantContextLen:   131072,
-			wantEmbedLen:     2560,
-			wantBlockCount:   34,
-			wantParamCount:   4_300_000_000,
-		},
-		{
-			name: "llama model with architectures array",
-			config: modelConfig{
-				Architectures:         []string{"LlamaForCausalLM"},
-				HiddenSize:            4096,
-				NumHiddenLayers:       32,
-				MaxPositionEmbeddings: 4096,
-				IntermediateSize:      11008,
-				NumAttentionHeads:     32,
-				NumKeyValueHeads:      32,
-				VocabSize:             32000,
-				TorchDtype:            "float16",
-			},
-			totalTensorBytes: 14_000_000_088, // ~7B params * 2 bytes + 88 bytes header
-			tensorCount:      1,
-			wantArch:         "llama",
-			wantContextLen:   4096,
-			wantEmbedLen:     4096,
-			wantBlockCount:   32,
-			wantParamCount:   7_000_000_000,
-		},
-		{
-			name: "multimodal model with text_config",
-			config: modelConfig{
-				Architectures: []string{"Gemma3ForConditionalGeneration"},
-				HiddenSize:    1152, // vision hidden size
-				TextConfig: &struct {
-					HiddenSize            int `json:"hidden_size"`
-					MaxPositionEmbeddings int `json:"max_position_embeddings"`
-					NumHiddenLayers       int `json:"num_hidden_layers"`
-				}{
-					HiddenSize:            2560,
-					MaxPositionEmbeddings: 131072,
-					NumHiddenLayers:       34,
-				},
-				NumAttentionHeads: 8,
-				NumKeyValueHeads:  4,
-				VocabSize:         262144,
-				TorchDtype:        "bfloat16",
-			},
-			totalTensorBytes: 8_600_000_088,
-			tensorCount:      1,
-			wantArch:         "gemma3",
-			wantContextLen:   131072,
-			wantEmbedLen:     2560,
-			wantBlockCount:   34,
-			wantParamCount:   4_300_000_000,
-		},
-		{
-			name: "float32 model",
-			config: modelConfig{
-				ModelType:             "test",
-				HiddenSize:            512,
-				NumHiddenLayers:       6,
-				MaxPositionEmbeddings: 2048,
-				TorchDtype:            "float32",
-			},
-			totalTensorBytes: 400_000_088, // 100M params * 4 bytes + 88 bytes header
-			tensorCount:      1,
-			wantArch:         "test",
-			wantContextLen:   2048,
-			wantEmbedLen:     512,
-			wantBlockCount:   6,
-			wantParamCount:   100_000_000,
-		},
-		{
-			name: "multiple tensors with header overhead",
-			config: modelConfig{
-				ModelType:             "test",
-				HiddenSize:            256,
-				NumHiddenLayers:       4,
-				MaxPositionEmbeddings: 1024,
-				TorchDtype:            "bfloat16",
-			},
-			totalTensorBytes: 2_000_880, // 1M params * 2 bytes + 10 tensors * 88 bytes
-			tensorCount:      10,
-			wantArch:         "test",
-			wantContextLen:   1024,
-			wantEmbedLen:     256,
-			wantBlockCount:   4,
-			wantParamCount:   1_000_000,
-		},
-	}
-
-	for _, tt := range tests {
-		t.Run(tt.name, func(t *testing.T) {
-			info := buildModelInfo(tt.config, tt.totalTensorBytes, tt.tensorCount)
-
-			// Check architecture
-			if arch, ok := info["general.architecture"].(string); !ok || arch != tt.wantArch {
-				t.Errorf("architecture = %v, want %v", info["general.architecture"], tt.wantArch)
-			}
-
-			// Check context length
-			contextKey := tt.wantArch + ".context_length"
-			if contextLen, ok := info[contextKey].(int); !ok || contextLen != tt.wantContextLen {
-				t.Errorf("context_length = %v, want %v", info[contextKey], tt.wantContextLen)
-			}
-
-			// Check embedding length
-			embedKey := tt.wantArch + ".embedding_length"
-			if embedLen, ok := info[embedKey].(int); !ok || embedLen != tt.wantEmbedLen {
-				t.Errorf("embedding_length = %v, want %v", info[embedKey], tt.wantEmbedLen)
-			}
-
-			// Check block count
-			blockKey := tt.wantArch + ".block_count"
-			if blockCount, ok := info[blockKey].(int); !ok || blockCount != tt.wantBlockCount {
-				t.Errorf("block_count = %v, want %v", info[blockKey], tt.wantBlockCount)
-			}
-
-			// Check parameter count
-			if paramCount, ok := info["general.parameter_count"].(int64); !ok || paramCount != tt.wantParamCount {
-				t.Errorf("parameter_count = %v, want %v", info["general.parameter_count"], tt.wantParamCount)
-			}
-		})
-	}
-}
-
-func TestBuildModelInfo_ArchitectureConversion(t *testing.T) {
-	tests := []struct {
-		name         string
-		architectures []string
-		modelType    string
-		wantArch     string
-	}{
-		{
-			name:         "LlamaForCausalLM",
-			architectures: []string{"LlamaForCausalLM"},
-			wantArch:     "llama",
-		},
-		{
-			name:         "Gemma3ForCausalLM",
-			architectures: []string{"Gemma3ForCausalLM"},
-			wantArch:     "gemma3",
-		},
-		{
-			name:         "Gemma3ForConditionalGeneration",
-			architectures: []string{"Gemma3ForConditionalGeneration"},
-			wantArch:     "gemma3",
-		},
-		{
-			name:         "Qwen2ForCausalLM",
-			architectures: []string{"Qwen2ForCausalLM"},
-			wantArch:     "qwen2",
-		},
-		{
-			name:         "model_type takes precedence",
-			architectures: []string{"LlamaForCausalLM"},
-			modelType:    "custom",
-			wantArch:     "custom",
-		},
-		{
-			name:         "empty architectures with model_type",
-			architectures: nil,
-			modelType:    "mymodel",
-			wantArch:     "mymodel",
-		},
-	}
-
-	for _, tt := range tests {
-		t.Run(tt.name, func(t *testing.T) {
-			config := modelConfig{
-				Architectures: tt.architectures,
-				ModelType:     tt.modelType,
-			}
-			info := buildModelInfo(config, 0, 0)
-
-			if arch, ok := info["general.architecture"].(string); !ok || arch != tt.wantArch {
-				t.Errorf("architecture = %v, want %v", info["general.architecture"], tt.wantArch)
-			}
-		})
-	}
-}
-
-func TestBuildModelInfo_BytesPerParam(t *testing.T) {
-	tests := []struct {
-		name           string
-		dtype          string
-		totalBytes     int64
-		tensorCount    int64
-		wantParamCount int64
-	}{
-		{
-			name:           "bfloat16",
-			dtype:          "bfloat16",
-			totalBytes:     2_000_088, // 1M * 2 + 88
-			tensorCount:    1,
-			wantParamCount: 1_000_000,
-		},
-		{
-			name:           "float16",
-			dtype:          "float16",
-			totalBytes:     2_000_088,
-			tensorCount:    1,
-			wantParamCount: 1_000_000,
-		},
-		{
-			name:           "float32",
-			dtype:          "float32",
-			totalBytes:     4_000_088, // 1M * 4 + 88
-			tensorCount:    1,
-			wantParamCount: 1_000_000,
-		},
-		{
-			name:           "int8",
-			dtype:          "int8",
-			totalBytes:     1_000_088, // 1M * 1 + 88
-			tensorCount:    1,
-			wantParamCount: 1_000_000,
-		},
-		{
-			name:           "unknown dtype defaults to 2 bytes",
-			dtype:          "unknown",
-			totalBytes:     2_000_088,
-			tensorCount:    1,
-			wantParamCount: 1_000_000,
-		},
-		{
-			name:           "empty dtype defaults to 2 bytes",
-			dtype:          "",
-			totalBytes:     2_000_088,
-			tensorCount:    1,
-			wantParamCount: 1_000_000,
-		},
-	}
-
-	for _, tt := range tests {
-		t.Run(tt.name, func(t *testing.T) {
-			config := modelConfig{
-				ModelType:  "test",
-				TorchDtype: tt.dtype,
-			}
-			info := buildModelInfo(config, tt.totalBytes, tt.tensorCount)
-
-			if paramCount, ok := info["general.parameter_count"].(int64); !ok || paramCount != tt.wantParamCount {
-				t.Errorf("parameter_count = %v, want %v", info["general.parameter_count"], tt.wantParamCount)
-			}
-		})
-	}
-}
-
-func TestParseSafetensorsHeader(t *testing.T) {
-	tests := []struct {
-		name      string
-		header    map[string]any
-		wantDtype string
-		wantShape []int64
-		wantErr   bool
-	}{
-		{
-			name: "simple tensor",
-			header: map[string]any{
-				"weight": map[string]any{
-					"dtype":        "BF16",
-					"shape":        []int64{2560, 262144},
-					"data_offsets": []int64{0, 1342177280},
-				},
-			},
-			wantDtype: "BF16",
-			wantShape: []int64{2560, 262144},
-		},
-		{
-			name: "with metadata",
-			header: map[string]any{
-				"__metadata__": map[string]any{
-					"format": "pt",
-				},
-				"bias": map[string]any{
-					"dtype":        "F32",
-					"shape":        []int64{1024},
-					"data_offsets": []int64{0, 4096},
-				},
-			},
-			wantDtype: "F32",
-			wantShape: []int64{1024},
-		},
-		{
-			name: "float16 tensor",
-			header: map[string]any{
-				"layer.weight": map[string]any{
-					"dtype":        "F16",
-					"shape":        []int64{512, 512, 3, 3},
-					"data_offsets": []int64{0, 4718592},
-				},
-			},
-			wantDtype: "F16",
-			wantShape: []int64{512, 512, 3, 3},
-		},
-	}
-
-	for _, tt := range tests {
-		t.Run(tt.name, func(t *testing.T) {
-			// Create safetensors format: 8-byte size + JSON header
-			headerJSON, err := json.Marshal(tt.header)
-			if err != nil {
-				t.Fatalf("failed to marshal header: %v", err)
-			}
-
-			var buf bytes.Buffer
-			if err := binary.Write(&buf, binary.LittleEndian, uint64(len(headerJSON))); err != nil {
-				t.Fatalf("failed to write header size: %v", err)
-			}
-			buf.Write(headerJSON)
-
-			info, err := parseSafetensorsHeader(&buf)
-			if (err != nil) != tt.wantErr {
-				t.Errorf("parseSafetensorsHeader() error = %v, wantErr %v", err, tt.wantErr)
-				return
-			}
-			if tt.wantErr {
-				return
-			}
-
-			if info.Dtype != tt.wantDtype {
-				t.Errorf("Dtype = %v, want %v", info.Dtype, tt.wantDtype)
-			}
-
-			if len(info.Shape) != len(tt.wantShape) {
-				t.Errorf("Shape length = %v, want %v", len(info.Shape), len(tt.wantShape))
-			} else {
-				for i, s := range info.Shape {
-					if s != tt.wantShape[i] {
-						t.Errorf("Shape[%d] = %v, want %v", i, s, tt.wantShape[i])
-					}
-				}
-			}
-		})
-	}
-}
-
-func TestParseSafetensorsHeader_Errors(t *testing.T) {
-	tests := []struct {
-		name    string
-		data    []byte
-		wantErr string
-	}{
-		{
-			name:    "empty data",
-			data:    []byte{},
-			wantErr: "failed to read header size",
-		},
-		{
-			name:    "truncated header size",
-			data:    []byte{0x01, 0x02, 0x03},
-			wantErr: "failed to read header size",
-		},
-		{
-			name: "header size too large",
-			data: func() []byte {
-				var buf bytes.Buffer
-				binary.Write(&buf, binary.LittleEndian, uint64(2*1024*1024)) // 2MB
-				return buf.Bytes()
-			}(),
-			wantErr: "header size too large",
-		},
-		{
-			name: "truncated header",
-			data: func() []byte {
-				var buf bytes.Buffer
-				binary.Write(&buf, binary.LittleEndian, uint64(100))
-				buf.Write([]byte("short"))
-				return buf.Bytes()
-			}(),
-			wantErr: "failed to read header",
-		},
-		{
-			name: "invalid JSON",
-			data: func() []byte {
-				var buf bytes.Buffer
-				binary.Write(&buf, binary.LittleEndian, uint64(10))
-				buf.Write([]byte("not json!!"))
-				return buf.Bytes()
-			}(),
-			wantErr: "failed to parse header",
-		},
-		{
-			name: "no tensors in header",
-			data: func() []byte {
-				header := map[string]any{
-					"__metadata__": map[string]any{"format": "pt"},
-				}
-				headerJSON, _ := json.Marshal(header)
-				var buf bytes.Buffer
-				binary.Write(&buf, binary.LittleEndian, uint64(len(headerJSON)))
-				buf.Write(headerJSON)
-				return buf.Bytes()
-			}(),
-			wantErr: "no tensor found in header",
-		},
-	}
-
-	for _, tt := range tests {
-		t.Run(tt.name, func(t *testing.T) {
-			_, err := parseSafetensorsHeader(bytes.NewReader(tt.data))
-			if err == nil {
-				t.Error("expected error, got nil")
-				return
-			}
-			if !bytes.Contains([]byte(err.Error()), []byte(tt.wantErr)) {
-				t.Errorf("error = %v, want error containing %v", err, tt.wantErr)
-			}
-		})
-	}
-}
-
-func TestGetTensorInfoFromManifest(t *testing.T) {
-	// Create a temp directory for blobs
-	tempDir, err := os.MkdirTemp("", "ollama-test-*")
-	if err != nil {
-		t.Fatalf("failed to create temp dir: %v", err)
-	}
-	defer os.RemoveAll(tempDir)
-
-	// Create test tensor blobs
-	tensors := []struct {
-		name   string
-		digest string
-		dtype  string
-		shape  []int64
-	}{
-		{
-			name:   "model.embed_tokens.weight",
-			digest: "sha256:abc123",
-			dtype:  "BF16",
-			shape:  []int64{262144, 2560},
-		},
-		{
-			name:   "model.layers.0.self_attn.q_proj.weight",
-			digest: "sha256:def456",
-			dtype:  "BF16",
-			shape:  []int64{2560, 2560},
-		},
-		{
-			name:   "model.norm.weight",
-			digest: "sha256:ghi789",
-			dtype:  "F32",
-			shape:  []int64{2560},
-		},
-	}
-
-	// Create blob files
-	var layers []imagegen.ManifestLayer
-	for _, tensor := range tensors {
-		// Create safetensors blob
-		header := map[string]any{
-			tensor.name: map[string]any{
-				"dtype":        tensor.dtype,
-				"shape":        tensor.shape,
-				"data_offsets": []int64{0, 1000},
-			},
-		}
-		headerJSON, _ := json.Marshal(header)
-
-		var buf bytes.Buffer
-		binary.Write(&buf, binary.LittleEndian, uint64(len(headerJSON)))
-		buf.Write(headerJSON)
-
-		// Write blob file
-		blobName := "sha256-" + tensor.digest[7:]
-		blobPath := filepath.Join(tempDir, blobName)
-		if err := os.WriteFile(blobPath, buf.Bytes(), 0644); err != nil {
-			t.Fatalf("failed to write blob: %v", err)
-		}
-
-		layers = append(layers, imagegen.ManifestLayer{
-			MediaType: "application/vnd.ollama.image.tensor",
-			Digest:    tensor.digest,
-			Size:      int64(buf.Len() + 1000), // header + fake data
-			Name:      tensor.name,
-		})
-	}
-
-	// Add a non-tensor layer (should be skipped)
-	layers = append(layers, imagegen.ManifestLayer{
-		MediaType: "application/vnd.ollama.image.json",
-		Digest:    "sha256:config",
-		Size:      100,
-		Name:      "config.json",
-	})
-
-	manifest := &imagegen.ModelManifest{
-		Manifest: &imagegen.Manifest{
-			Layers: layers,
-		},
-		BlobDir: tempDir,
-	}
-
-	result, err := getTensorInfoFromManifest(manifest)
-	if err != nil {
-		t.Fatalf("getTensorInfoFromManifest() error = %v", err)
-	}
-
-	if len(result) != 3 {
-		t.Errorf("got %d tensors, want 3", len(result))
-	}
-
-	// Verify each tensor
-	for i, tensor := range tensors {
-		if i >= len(result) {
-			break
-		}
-		if result[i].Name != tensor.name {
-			t.Errorf("tensor[%d].Name = %v, want %v", i, result[i].Name, tensor.name)
-		}
-		if result[i].Type != tensor.dtype {
-			t.Errorf("tensor[%d].Type = %v, want %v", i, result[i].Type, tensor.dtype)
-		}
-		if len(result[i].Shape) != len(tensor.shape) {
-			t.Errorf("tensor[%d].Shape length = %v, want %v", i, len(result[i].Shape), len(tensor.shape))
-		}
-	}
-}
-
-func TestReadSafetensorsHeader(t *testing.T) {
-	// Create a temp file with a valid safetensors header
-	tempDir, err := os.MkdirTemp("", "ollama-test-*")
-	if err != nil {
-		t.Fatalf("failed to create temp dir: %v", err)
-	}
-	defer os.RemoveAll(tempDir)
-
-	header := map[string]any{
-		"test_tensor": map[string]any{
-			"dtype":        "BF16",
-			"shape":        []int64{1024, 768},
-			"data_offsets": []int64{0, 1572864},
-		},
-	}
-	headerJSON, _ := json.Marshal(header)
-
-	var buf bytes.Buffer
-	binary.Write(&buf, binary.LittleEndian, uint64(len(headerJSON)))
-	buf.Write(headerJSON)
-
-	filePath := filepath.Join(tempDir, "test.safetensors")
-	if err := os.WriteFile(filePath, buf.Bytes(), 0644); err != nil {
-		t.Fatalf("failed to write test file: %v", err)
-	}
-
-	info, err := readSafetensorsHeader(filePath)
-	if err != nil {
-		t.Fatalf("readSafetensorsHeader() error = %v", err)
-	}
-
-	if info.Dtype != "BF16" {
-		t.Errorf("Dtype = %v, want BF16", info.Dtype)
-	}
-	if len(info.Shape) != 2 || info.Shape[0] != 1024 || info.Shape[1] != 768 {
-		t.Errorf("Shape = %v, want [1024, 768]", info.Shape)
-	}
-}
-
-func TestReadSafetensorsHeader_FileNotFound(t *testing.T) {
-	_, err := readSafetensorsHeader("/nonexistent/path/file.safetensors")
-	if err == nil {
-		t.Error("expected error for nonexistent file")
-	}
-}