fix unit test

---------

Co-authored-by: ParthSareen <parth.sareen@ollama.com>

.github/workflows/release.yaml

@@ -92,7 +92,7 @@ jobs:
             flags: ''
           - os: windows
             arch: amd64
-            preset: 'CUDA 13'
+            preset: 'CUDA 13 Windows'
             install: https://developer.download.nvidia.com/compute/cuda/13.0.0/local_installers/cuda_13.0.0_windows.exe
             cuda-components:
               - '"cudart"'

CMakePresets.json

@@ -40,7 +40,17 @@
       "name": "CUDA 13",
       "inherits": [ "CUDA" ],
       "cacheVariables": {
-        "CMAKE_CUDA_ARCHITECTURES": "75-virtual;80-virtual;86-virtual;87-virtual;89-virtual;90-virtual;90a-virtual;100-virtual;103-virtual;110-virtual;120-virtual;121-virtual",
+        "CMAKE_CUDA_ARCHITECTURES": "75-virtual;80-virtual;86-virtual;89-virtual;90-virtual;90a-virtual;100-virtual;103-virtual;110-virtual;120-virtual;121-virtual",
+        "CMAKE_CUDA_FLAGS": "-t 4",
+        "OLLAMA_RUNNER_DIR": "cuda_v13"
+      }
+    },
+    {
+      "name": "CUDA 13 Windows",
+      "inherits": [ "CUDA" ],
+      "description": "Reduced architecture set for Windows to avoid MSVC template compilation issues",
+      "cacheVariables": {
+        "CMAKE_CUDA_ARCHITECTURES": "75-virtual;89-virtual;100-virtual;120-virtual",
         "CMAKE_CUDA_FLAGS": "-t 4",
         "OLLAMA_RUNNER_DIR": "cuda_v13"
       }
@@ -138,6 +148,11 @@
       "inherits": [ "CUDA" ],
       "configurePreset": "CUDA 13"
     },
+    {
+      "name": "CUDA 13 Windows",
+      "inherits": [ "CUDA" ],
+      "configurePreset": "CUDA 13 Windows"
+    },
     {
       "name": "JetPack 5",
       "inherits": [ "CUDA" ],

Makefile.sync

@@ -1,6 +1,6 @@
 UPSTREAM=https://github.com/ggml-org/llama.cpp.git
 WORKDIR=llama/vendor
-FETCH_HEAD=ec98e2002
+FETCH_HEAD=a5bb8ba4c50257437630c136210396810741bbf7
 
 .PHONY: help
 help:

README.md

@@ -358,6 +358,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [Odin Runes](https://github.com/leonid20000/OdinRunes)
 - [LLM-X](https://github.com/mrdjohnson/llm-x) (Progressive Web App)
 - [AnythingLLM (Docker + MacOs/Windows/Linux native app)](https://github.com/Mintplex-Labs/anything-llm)
+- [Screenpipe](https://github.com/mediar-ai/screenpipe) (24/7 screen & mic recording with AI-powered search, uses Ollama for local LLM features)
 - [Ollama Basic Chat: Uses HyperDiv Reactive UI](https://github.com/rapidarchitect/ollama_basic_chat)
 - [Ollama-chats RPG](https://github.com/drazdra/ollama-chats)
 - [IntelliBar](https://intellibar.app/) (AI-powered assistant for macOS)
@@ -465,6 +466,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [Clueless](https://github.com/KashyapTan/clueless) (Open Source & Local Cluely: A desktop application LLM assistant to help you talk to anything on your screen using locally served Ollama models. Also undetectable to screenshare)
 - [ollama-co2](https://github.com/carbonatedWaterOrg/ollama-co2) (FastAPI web interface for monitoring and managing local and remote Ollama servers with real-time model monitoring and concurrent downloads)
 - [Hillnote](https://hillnote.com) (A Markdown-first workspace designed to supercharge your AI workflow. Create documents ready to integrate with Claude, ChatGPT, Gemini, Cursor, and more - all while keeping your work on your device.)
+- [Stakpak](https://github.com/stakpak/agent) (An open source, vendor neutral DevOps agent that works with any model, and any stack, for teams who just want to ship)
 
 ### Cloud
 

anthropic/anthropic.go

@@ -211,6 +211,7 @@ type MessageDelta struct {
 
 // DeltaUsage contains cumulative token usage
 type DeltaUsage struct {
+	InputTokens  int `json:"input_tokens"`
 	OutputTokens int `json:"output_tokens"`
 }
 
@@ -721,6 +722,7 @@ func (c *StreamConverter) Process(r api.ChatResponse) []StreamEvent {
 			})
 		}
 
+		c.inputTokens = r.Metrics.PromptEvalCount
 		c.outputTokens = r.Metrics.EvalCount
 		stopReason := mapStopReason(r.DoneReason, len(c.toolCallsSent) > 0)
 
@@ -732,6 +734,7 @@ func (c *StreamConverter) Process(r api.ChatResponse) []StreamEvent {
 					StopReason: stopReason,
 				},
 				Usage: DeltaUsage{
+					InputTokens:  c.inputTokens,
 					OutputTokens: c.outputTokens,
 				},
 			},

anthropic/anthropic_test.go

@@ -642,7 +642,7 @@ func TestStreamConverter_Basic(t *testing.T) {
 		},
 		Done:       true,
 		DoneReason: "stop",
-		Metrics:    api.Metrics{EvalCount: 5},
+		Metrics:    api.Metrics{PromptEvalCount: 10, EvalCount: 5},
 	}
 
 	events2 := conv.Process(resp2)
@@ -650,6 +650,24 @@ func TestStreamConverter_Basic(t *testing.T) {
 	// Should have content_block_delta, content_block_stop, message_delta, message_stop
 	hasStop := false
 	for _, e := range events2 {
+		if e.Event == "message_delta" {
+			if data, ok := e.Data.(MessageDeltaEvent); ok {
+				if data.Type != "message_delta" {
+					t.Errorf("unexpected data type: %+v", data)
+				}
+
+				if data.Delta.StopReason != "end_turn" {
+					t.Errorf("unexpected stop reason: %+v", data.Delta.StopReason)
+				}
+
+				if data.Usage.InputTokens != 10 || data.Usage.OutputTokens != 5 {
+					t.Errorf("unexpected usage: %+v", data.Usage)
+				}
+			} else {
+				t.Errorf("unexpected data: %+v", e.Data)
+			}
+		}
+
 		if e.Event == "message_stop" {
 			hasStop = true
 		}

cmd/cmd.go

@@ -36,6 +36,7 @@ import (
 
 	"github.com/ollama/ollama/api"
 	"github.com/ollama/ollama/cmd/config"
+	"github.com/ollama/ollama/cmd/tui"
 	"github.com/ollama/ollama/envconfig"
 	"github.com/ollama/ollama/format"
 	"github.com/ollama/ollama/parser"
@@ -1791,6 +1792,134 @@ Environment Variables:
 	cmd.SetUsageTemplate(cmd.UsageTemplate() + envUsage)
 }
 
+// runInteractiveTUI runs the main interactive TUI menu.
+func runInteractiveTUI(cmd *cobra.Command) {
+	// errSelectionCancelled is returned when user cancels model selection
+	errSelectionCancelled := errors.New("cancelled")
+
+	// Selector adapters for tui
+	singleSelector := func(title string, items []config.ModelItem) (string, error) {
+		tuiItems := make([]tui.SelectItem, len(items))
+		for i, item := range items {
+			tuiItems[i] = tui.SelectItem{Name: item.Name, Description: item.Description}
+		}
+		result, err := tui.SelectSingle(title, tuiItems)
+		if errors.Is(err, tui.ErrCancelled) {
+			return "", errSelectionCancelled
+		}
+		return result, err
+	}
+
+	multiSelector := func(title string, items []config.ModelItem, preChecked []string) ([]string, error) {
+		tuiItems := make([]tui.SelectItem, len(items))
+		for i, item := range items {
+			tuiItems[i] = tui.SelectItem{Name: item.Name, Description: item.Description}
+		}
+		result, err := tui.SelectMultiple(title, tuiItems, preChecked)
+		if errors.Is(err, tui.ErrCancelled) {
+			return nil, errSelectionCancelled
+		}
+		return result, err
+	}
+
+	for {
+		result, err := tui.Run()
+		if err != nil {
+			fmt.Fprintf(os.Stderr, "Error: %v\n", err)
+			return
+		}
+
+		runModel := func(modelName string) {
+			_ = config.SetLastModel(modelName)
+			opts := runOptions{
+				Model:       modelName,
+				WordWrap:    os.Getenv("TERM") == "xterm-256color",
+				Options:     map[string]any{},
+				ShowConnect: true,
+			}
+			if err := loadOrUnloadModel(cmd, &opts); err != nil {
+				fmt.Fprintf(os.Stderr, "Error loading model: %v\n", err)
+				return
+			}
+			if err := generateInteractive(cmd, opts); err != nil {
+				fmt.Fprintf(os.Stderr, "Error running model: %v\n", err)
+			}
+		}
+
+		launchIntegration := func(name string) bool {
+			// If not configured or model no longer exists, prompt for model selection
+			configuredModel := config.IntegrationModel(name)
+			if configuredModel == "" || !config.ModelExists(cmd.Context(), configuredModel) {
+				err := config.ConfigureIntegrationWithSelectors(cmd.Context(), name, singleSelector, multiSelector)
+				if errors.Is(err, errSelectionCancelled) {
+					return false // Return to main menu
+				}
+				if err != nil {
+					fmt.Fprintf(os.Stderr, "Error configuring %s: %v\n", name, err)
+					return true
+				}
+			}
+			if err := config.LaunchIntegration(name); err != nil {
+				fmt.Fprintf(os.Stderr, "Error launching %s: %v\n", name, err)
+			}
+			return true
+		}
+
+		switch result.Selection {
+		case tui.SelectionNone:
+			// User quit
+			return
+		case tui.SelectionRunModel:
+			_ = config.SetLastSelection("run")
+			// Run last model directly if configured and still exists
+			if modelName := config.LastModel(); modelName != "" && config.ModelExists(cmd.Context(), modelName) {
+				runModel(modelName)
+			} else {
+				// No last model or model no longer exists, show picker
+				modelName, err := config.SelectModelWithSelector(cmd.Context(), singleSelector)
+				if errors.Is(err, errSelectionCancelled) {
+					continue // Return to main menu
+				}
+				if err != nil {
+					fmt.Fprintf(os.Stderr, "Error selecting model: %v\n", err)
+					continue
+				}
+				runModel(modelName)
+			}
+		case tui.SelectionChangeRunModel:
+			_ = config.SetLastSelection("run")
+			// Always show picker
+			modelName, err := config.SelectModelWithSelector(cmd.Context(), singleSelector)
+			if errors.Is(err, errSelectionCancelled) {
+				continue // Return to main menu
+			}
+			if err != nil {
+				fmt.Fprintf(os.Stderr, "Error selecting model: %v\n", err)
+				continue
+			}
+			runModel(modelName)
+		case tui.SelectionIntegration:
+			_ = config.SetLastSelection(result.Integration)
+			if !launchIntegration(result.Integration) {
+				continue // Return to main menu
+			}
+		case tui.SelectionChangeIntegration:
+			_ = config.SetLastSelection(result.Integration)
+			err := config.ConfigureIntegrationWithSelectors(cmd.Context(), result.Integration, singleSelector, multiSelector)
+			if errors.Is(err, errSelectionCancelled) {
+				continue // Return to main menu
+			}
+			if err != nil {
+				fmt.Fprintf(os.Stderr, "Error configuring %s: %v\n", result.Integration, err)
+				continue
+			}
+			if err := config.LaunchIntegration(result.Integration); err != nil {
+				fmt.Fprintf(os.Stderr, "Error launching %s: %v\n", result.Integration, err)
+			}
+		}
+	}
+}
+
 func NewCLI() *cobra.Command {
 	log.SetFlags(log.LstdFlags | log.Lshortfile)
 	cobra.EnableCommandSorting = false
@@ -1813,11 +1942,13 @@ func NewCLI() *cobra.Command {
 				return
 			}
 
-			cmd.Print(cmd.UsageString())
+			runInteractiveTUI(cmd)
 		},
 	}
 
 	rootCmd.Flags().BoolP("version", "v", false, "Show version information")
+	rootCmd.Flags().Bool("verbose", false, "Show timings for response")
+	rootCmd.Flags().Bool("nowordwrap", false, "Don't wrap words to the next line automatically")
 
 	createCmd := &cobra.Command{
 		Use:   "create MODEL",
@@ -1888,7 +2019,7 @@ func NewCLI() *cobra.Command {
 	serveCmd := &cobra.Command{
 		Use:     "serve",
 		Aliases: []string{"start"},
-		Short:   "Start ollama",
+		Short:   "Start Ollama",
 		Args:    cobra.ExactArgs(0),
 		RunE:    RunServer,
 	}
@@ -1963,7 +2094,7 @@ func NewCLI() *cobra.Command {
 		Use:    "runner",
 		Hidden: true,
 		RunE: func(cmd *cobra.Command, args []string) error {
-			return runner.Execute(os.Args[2:])
+			return runner.Execute(os.Args[1:])
 		},
 		FParseErrWhitelist: cobra.FParseErrWhitelist{UnknownFlags: true},
 	}
@@ -2031,7 +2162,7 @@ func NewCLI() *cobra.Command {
 		copyCmd,
 		deleteCmd,
 		runnerCmd,
-		config.LaunchCmd(checkServerHeartbeat),
+		config.LaunchCmd(checkServerHeartbeat, runInteractiveTUI),
 	)
 
 	return rootCmd

cmd/cmd_test.go

@@ -1553,7 +1553,7 @@ func TestShowInfoImageGen(t *testing.T) {
 		Details: api.ModelDetails{
 			Family:            "ZImagePipeline",
 			ParameterSize:     "10.3B",
-			QuantizationLevel: "FP8",
+			QuantizationLevel: "Q8",
 		},
 		Capabilities: []model.Capability{model.CapabilityImage},
 		Requires:     "0.14.0",
@@ -1565,7 +1565,7 @@ func TestShowInfoImageGen(t *testing.T) {
 	expect := "  Model\n" +
 		"    architecture    ZImagePipeline    \n" +
 		"    parameters      10.3B             \n" +
-		"    quantization    FP8               \n" +
+		"    quantization    Q8                \n" +
 		"    requires        0.14.0            \n" +
 		"\n" +
 		"  Capabilities\n" +

cmd/config/claude.go

@@ -6,6 +6,8 @@ import (
 	"os/exec"
 	"path/filepath"
 	"runtime"
+
+	"github.com/ollama/ollama/envconfig"
 )
 
 // Claude implements Runner for Claude Code integration
@@ -13,11 +15,13 @@ type Claude struct{}
 
 func (c *Claude) String() string { return "Claude Code" }
 
-func (c *Claude) args(model string) []string {
+func (c *Claude) args(model string, extra []string) []string {
+	var args []string
 	if model != "" {
-		return []string{"--model", model}
+		args = append(args, "--model", model)
 	}
-	return nil
+	args = append(args, extra...)
+	return args
 }
 
 func (c *Claude) findPath() (string, error) {
@@ -39,18 +43,18 @@ func (c *Claude) findPath() (string, error) {
 	return fallback, nil
 }
 
-func (c *Claude) Run(model string) error {
+func (c *Claude) Run(model string, args []string) error {
 	claudePath, err := c.findPath()
 	if err != nil {
 		return fmt.Errorf("claude is not installed, install from https://code.claude.com/docs/en/quickstart")
 	}
 
-	cmd := exec.Command(claudePath, c.args(model)...)
+	cmd := exec.Command(claudePath, c.args(model, args)...)
 	cmd.Stdin = os.Stdin
 	cmd.Stdout = os.Stdout
 	cmd.Stderr = os.Stderr
 	cmd.Env = append(os.Environ(),
-		"ANTHROPIC_BASE_URL=http://localhost:11434",
+		"ANTHROPIC_BASE_URL="+envconfig.Host().String(),
 		"ANTHROPIC_API_KEY=",
 		"ANTHROPIC_AUTH_TOKEN=ollama",
 	)

cmd/config/claude_test.go

@@ -84,17 +84,21 @@ func TestClaudeArgs(t *testing.T) {
 	tests := []struct {
 		name  string
 		model string
+		args  []string
 		want  []string
 	}{
-		{"with model", "llama3.2", []string{"--model", "llama3.2"}},
-		{"empty model", "", nil},
+		{"with model", "llama3.2", nil, []string{"--model", "llama3.2"}},
+		{"empty model", "", nil, nil},
+		{"with model and verbose", "llama3.2", []string{"--verbose"}, []string{"--model", "llama3.2", "--verbose"}},
+		{"empty model with help", "", []string{"--help"}, []string{"--help"}},
+		{"with allowed tools", "llama3.2", []string{"--allowedTools", "Read,Write,Bash"}, []string{"--model", "llama3.2", "--allowedTools", "Read,Write,Bash"}},
 	}
 
 	for _, tt := range tests {
 		t.Run(tt.name, func(t *testing.T) {
-			got := c.args(tt.model)
+			got := c.args(tt.model, tt.args)
 			if !slices.Equal(got, tt.want) {
-				t.Errorf("args(%q) = %v, want %v", tt.model, got, tt.want)
+				t.Errorf("args(%q, %v) = %v, want %v", tt.model, tt.args, got, tt.want)
 			}
 		})
 	}

cmd/config/codex.go

@@ -14,20 +14,21 @@ type Codex struct{}
 
 func (c *Codex) String() string { return "Codex" }
 
-func (c *Codex) args(model string) []string {
+func (c *Codex) args(model string, extra []string) []string {
 	args := []string{"--oss"}
 	if model != "" {
 		args = append(args, "-m", model)
 	}
+	args = append(args, extra...)
 	return args
 }
 
-func (c *Codex) Run(model string) error {
+func (c *Codex) Run(model string, args []string) error {
 	if err := checkCodexVersion(); err != nil {
 		return err
 	}
 
-	cmd := exec.Command("codex", c.args(model)...)
+	cmd := exec.Command("codex", c.args(model, args)...)
 	cmd.Stdin = os.Stdin
 	cmd.Stdout = os.Stdout
 	cmd.Stderr = os.Stderr

cmd/config/codex_test.go

@@ -11,17 +11,20 @@ func TestCodexArgs(t *testing.T) {
 	tests := []struct {
 		name  string
 		model string
+		args  []string
 		want  []string
 	}{
-		{"with model", "llama3.2", []string{"--oss", "-m", "llama3.2"}},
-		{"empty model", "", []string{"--oss"}},
+		{"with model", "llama3.2", nil, []string{"--oss", "-m", "llama3.2"}},
+		{"empty model", "", nil, []string{"--oss"}},
+		{"with model and profile", "qwen3-coder", []string{"-p", "myprofile"}, []string{"--oss", "-m", "qwen3-coder", "-p", "myprofile"}},
+		{"with sandbox flag", "llama3.2", []string{"--sandbox", "workspace-write"}, []string{"--oss", "-m", "llama3.2", "--sandbox", "workspace-write"}},
 	}
 
 	for _, tt := range tests {
 		t.Run(tt.name, func(t *testing.T) {
-			got := c.args(tt.model)
+			got := c.args(tt.model, tt.args)
 			if !slices.Equal(got, tt.want) {
-				t.Errorf("args(%q) = %v, want %v", tt.model, got, tt.want)
+				t.Errorf("args(%q, %v) = %v, want %v", tt.model, tt.args, got, tt.want)
 			}
 		})
 	}

cmd/config/config.go

@@ -3,12 +3,16 @@
 package config
 
 import (
+	"context"
 	"encoding/json"
 	"errors"
 	"fmt"
+	"log/slog"
 	"os"
 	"path/filepath"
 	"strings"
+
+	"github.com/ollama/ollama/api"
 )
 
 type integration struct {
@@ -16,10 +20,20 @@ type integration struct {
 }
 
 type config struct {
-	Integrations map[string]*integration `json:"integrations"`
+	Integrations  map[string]*integration `json:"integrations"`
+	LastModel     string                  `json:"last_model,omitempty"`
+	LastSelection string                  `json:"last_selection,omitempty"` // "run" or integration name
 }
 
 func configPath() (string, error) {
+	home, err := os.UserHomeDir()
+	if err != nil {
+		return "", err
+	}
+	return filepath.Join(home, ".ollama", "config.json"), nil
+}
+
+func legacyConfigPath() (string, error) {
 	home, err := os.UserHomeDir()
 	if err != nil {
 		return "", err
@@ -27,6 +41,46 @@ func configPath() (string, error) {
 	return filepath.Join(home, ".ollama", "config", "config.json"), nil
 }
 
+// migrateConfig moves the config from the legacy path to ~/.ollama/config.json
+func migrateConfig() (bool, error) {
+	oldPath, err := legacyConfigPath()
+	if err != nil {
+		return false, err
+	}
+
+	oldData, err := os.ReadFile(oldPath)
+	if err != nil {
+		if os.IsNotExist(err) {
+			return false, nil
+		}
+		return false, err
+	}
+
+	var js json.RawMessage
+	if err := json.Unmarshal(oldData, &js); err != nil {
+		slog.Warn("legacy config has invalid JSON, skipping migration", "path", oldPath, "error", err)
+		return false, nil
+	}
+
+	newPath, err := configPath()
+	if err != nil {
+		return false, err
+	}
+
+	if err := os.MkdirAll(filepath.Dir(newPath), 0o755); err != nil {
+		return false, err
+	}
+	if err := os.WriteFile(newPath, oldData, 0o644); err != nil {
+		return false, fmt.Errorf("write new config: %w", err)
+	}
+
+	_ = os.Remove(oldPath)
+	_ = os.Remove(filepath.Dir(oldPath)) // clean up empty directory
+
+	slog.Info("migrated config", "from", oldPath, "to", newPath)
+	return true, nil
+}
+
 func load() (*config, error) {
 	path, err := configPath()
 	if err != nil {
@@ -34,6 +88,11 @@ func load() (*config, error) {
 	}
 
 	data, err := os.ReadFile(path)
+	if err != nil && os.IsNotExist(err) {
+		if migrated, merr := migrateConfig(); merr == nil && migrated {
+			data, err = os.ReadFile(path)
+		}
+	}
 	if err != nil {
 		if os.IsNotExist(err) {
 			return &config{Integrations: make(map[string]*integration)}, nil
@@ -86,6 +145,74 @@ func saveIntegration(appName string, models []string) error {
 	return save(cfg)
 }
 
+// IntegrationModel returns the first configured model for an integration, or empty string if not configured.
+func IntegrationModel(appName string) string {
+	ic, err := loadIntegration(appName)
+	if err != nil || len(ic.Models) == 0 {
+		return ""
+	}
+	return ic.Models[0]
+}
+
+// LastModel returns the last model that was run, or empty string if none.
+func LastModel() string {
+	cfg, err := load()
+	if err != nil {
+		return ""
+	}
+	return cfg.LastModel
+}
+
+// SetLastModel saves the last model that was run.
+func SetLastModel(model string) error {
+	cfg, err := load()
+	if err != nil {
+		return err
+	}
+	cfg.LastModel = model
+	return save(cfg)
+}
+
+// LastSelection returns the last menu selection ("run" or integration name), or empty string if none.
+func LastSelection() string {
+	cfg, err := load()
+	if err != nil {
+		return ""
+	}
+	return cfg.LastSelection
+}
+
+// SetLastSelection saves the last menu selection ("run" or integration name).
+func SetLastSelection(selection string) error {
+	cfg, err := load()
+	if err != nil {
+		return err
+	}
+	cfg.LastSelection = selection
+	return save(cfg)
+}
+
+// ModelExists checks if a model exists on the Ollama server.
+func ModelExists(ctx context.Context, name string) bool {
+	if name == "" {
+		return false
+	}
+	client, err := api.ClientFromEnvironment()
+	if err != nil {
+		return false
+	}
+	models, err := client.List(ctx)
+	if err != nil {
+		return false
+	}
+	for _, m := range models.Models {
+		if m.Name == name || strings.HasPrefix(m.Name, name+":") {
+			return true
+		}
+	}
+	return false
+}
+
 func loadIntegration(appName string) (*integration, error) {
 	cfg, err := load()
 	if err != nil {

cmd/config/config_test.go

@@ -200,12 +200,10 @@ func TestLoadIntegration_CorruptedJSON(t *testing.T) {
 	tmpDir := t.TempDir()
 	setTestHome(t, tmpDir)
 
-	// Create corrupted config.json file
-	dir := filepath.Join(tmpDir, ".ollama", "config")
+	dir := filepath.Join(tmpDir, ".ollama")
 	os.MkdirAll(dir, 0o755)
 	os.WriteFile(filepath.Join(dir, "config.json"), []byte(`{corrupted json`), 0o644)
 
-	// Corrupted file is treated as empty, so loadIntegration returns not found
 	_, err := loadIntegration("test")
 	if err == nil {
 		t.Error("expected error for nonexistent integration in corrupted file")
@@ -267,7 +265,7 @@ func TestConfigPath(t *testing.T) {
 		t.Fatal(err)
 	}
 
-	expected := filepath.Join(tmpDir, ".ollama", "config", "config.json")
+	expected := filepath.Join(tmpDir, ".ollama", "config.json")
 	if path != expected {
 		t.Errorf("expected %s, got %s", expected, path)
 	}
@@ -322,6 +320,183 @@ func TestLoad(t *testing.T) {
 	})
 }
 
+func TestMigrateConfig(t *testing.T) {
+	t.Run("migrates legacy file to new location", func(t *testing.T) {
+		tmpDir := t.TempDir()
+		setTestHome(t, tmpDir)
+
+		legacyDir := filepath.Join(tmpDir, ".ollama", "config")
+		os.MkdirAll(legacyDir, 0o755)
+		data := []byte(`{"integrations":{"claude":{"models":["llama3.2"]}}}`)
+		os.WriteFile(filepath.Join(legacyDir, "config.json"), data, 0o644)
+
+		migrated, err := migrateConfig()
+		if err != nil {
+			t.Fatal(err)
+		}
+		if !migrated {
+			t.Fatal("expected migration to occur")
+		}
+
+		newPath, _ := configPath()
+		got, err := os.ReadFile(newPath)
+		if err != nil {
+			t.Fatalf("new config not found: %v", err)
+		}
+		if string(got) != string(data) {
+			t.Errorf("content mismatch: got %s", got)
+		}
+
+		if _, err := os.Stat(filepath.Join(legacyDir, "config.json")); !os.IsNotExist(err) {
+			t.Error("legacy file should have been removed")
+		}
+
+		if _, err := os.Stat(legacyDir); !os.IsNotExist(err) {
+			t.Error("legacy directory should have been removed")
+		}
+	})
+
+	t.Run("no-op when no legacy file exists", func(t *testing.T) {
+		tmpDir := t.TempDir()
+		setTestHome(t, tmpDir)
+
+		migrated, err := migrateConfig()
+		if err != nil {
+			t.Fatal(err)
+		}
+		if migrated {
+			t.Error("expected no migration")
+		}
+	})
+
+	t.Run("skips corrupt legacy file", func(t *testing.T) {
+		tmpDir := t.TempDir()
+		setTestHome(t, tmpDir)
+
+		legacyDir := filepath.Join(tmpDir, ".ollama", "config")
+		os.MkdirAll(legacyDir, 0o755)
+		os.WriteFile(filepath.Join(legacyDir, "config.json"), []byte(`{corrupt`), 0o644)
+
+		migrated, err := migrateConfig()
+		if err != nil {
+			t.Fatal(err)
+		}
+		if migrated {
+			t.Error("should not migrate corrupt file")
+		}
+
+		if _, err := os.Stat(filepath.Join(legacyDir, "config.json")); os.IsNotExist(err) {
+			t.Error("corrupt legacy file should not have been deleted")
+		}
+	})
+
+	t.Run("new path takes precedence over legacy", func(t *testing.T) {
+		tmpDir := t.TempDir()
+		setTestHome(t, tmpDir)
+
+		legacyDir := filepath.Join(tmpDir, ".ollama", "config")
+		os.MkdirAll(legacyDir, 0o755)
+		os.WriteFile(filepath.Join(legacyDir, "config.json"), []byte(`{"integrations":{"old":{"models":["old-model"]}}}`), 0o644)
+
+		newDir := filepath.Join(tmpDir, ".ollama")
+		os.WriteFile(filepath.Join(newDir, "config.json"), []byte(`{"integrations":{"new":{"models":["new-model"]}}}`), 0o644)
+
+		cfg, err := load()
+		if err != nil {
+			t.Fatal(err)
+		}
+		if _, ok := cfg.Integrations["new"]; !ok {
+			t.Error("expected new-path integration to be loaded")
+		}
+		if _, ok := cfg.Integrations["old"]; ok {
+			t.Error("legacy integration should not have been loaded")
+		}
+	})
+
+	t.Run("idempotent when called twice", func(t *testing.T) {
+		tmpDir := t.TempDir()
+		setTestHome(t, tmpDir)
+
+		legacyDir := filepath.Join(tmpDir, ".ollama", "config")
+		os.MkdirAll(legacyDir, 0o755)
+		os.WriteFile(filepath.Join(legacyDir, "config.json"), []byte(`{"integrations":{}}`), 0o644)
+
+		if _, err := migrateConfig(); err != nil {
+			t.Fatal(err)
+		}
+
+		migrated, err := migrateConfig()
+		if err != nil {
+			t.Fatal(err)
+		}
+		if migrated {
+			t.Error("second migration should be a no-op")
+		}
+	})
+
+	t.Run("legacy directory preserved if not empty", func(t *testing.T) {
+		tmpDir := t.TempDir()
+		setTestHome(t, tmpDir)
+
+		legacyDir := filepath.Join(tmpDir, ".ollama", "config")
+		os.MkdirAll(legacyDir, 0o755)
+		os.WriteFile(filepath.Join(legacyDir, "config.json"), []byte(`{"integrations":{}}`), 0o644)
+		os.WriteFile(filepath.Join(legacyDir, "other-file.txt"), []byte("keep me"), 0o644)
+
+		if _, err := migrateConfig(); err != nil {
+			t.Fatal(err)
+		}
+
+		if _, err := os.Stat(legacyDir); os.IsNotExist(err) {
+			t.Error("directory with other files should not have been removed")
+		}
+		if _, err := os.Stat(filepath.Join(legacyDir, "other-file.txt")); os.IsNotExist(err) {
+			t.Error("other files in legacy directory should be untouched")
+		}
+	})
+
+	t.Run("save writes to new path after migration", func(t *testing.T) {
+		tmpDir := t.TempDir()
+		setTestHome(t, tmpDir)
+
+		legacyDir := filepath.Join(tmpDir, ".ollama", "config")
+		os.MkdirAll(legacyDir, 0o755)
+		os.WriteFile(filepath.Join(legacyDir, "config.json"), []byte(`{"integrations":{"claude":{"models":["llama3.2"]}}}`), 0o644)
+
+		// load triggers migration, then save should write to new path
+		if err := saveIntegration("codex", []string{"qwen2.5"}); err != nil {
+			t.Fatal(err)
+		}
+
+		newPath := filepath.Join(tmpDir, ".ollama", "config.json")
+		if _, err := os.Stat(newPath); os.IsNotExist(err) {
+			t.Error("save should write to new path")
+		}
+
+		// old path should not be recreated
+		if _, err := os.Stat(filepath.Join(legacyDir, "config.json")); !os.IsNotExist(err) {
+			t.Error("save should not recreate legacy path")
+		}
+	})
+
+	t.Run("load triggers migration transparently", func(t *testing.T) {
+		tmpDir := t.TempDir()
+		setTestHome(t, tmpDir)
+
+		legacyDir := filepath.Join(tmpDir, ".ollama", "config")
+		os.MkdirAll(legacyDir, 0o755)
+		os.WriteFile(filepath.Join(legacyDir, "config.json"), []byte(`{"integrations":{"claude":{"models":["llama3.2"]}}}`), 0o644)
+
+		cfg, err := load()
+		if err != nil {
+			t.Fatal(err)
+		}
+		if cfg.Integrations["claude"] == nil || cfg.Integrations["claude"].Models[0] != "llama3.2" {
+			t.Error("migration via load() did not preserve data")
+		}
+	})
+}
+
 func TestSave(t *testing.T) {
 	tmpDir := t.TempDir()
 	setTestHome(t, tmpDir)

cmd/config/droid.go

@@ -7,6 +7,8 @@ import (
 	"os/exec"
 	"path/filepath"
 	"slices"
+
+	"github.com/ollama/ollama/envconfig"
 )
 
 // Droid implements Runner and Editor for Droid integration
@@ -37,7 +39,7 @@ type modelEntry struct {
 
 func (d *Droid) String() string { return "Droid" }
 
-func (d *Droid) Run(model string) error {
+func (d *Droid) Run(model string, args []string) error {
 	if _, err := exec.LookPath("droid"); err != nil {
 		return fmt.Errorf("droid is not installed, install from https://docs.factory.ai/cli/getting-started/quickstart")
 	}
@@ -51,7 +53,7 @@ func (d *Droid) Run(model string) error {
 		return fmt.Errorf("setup failed: %w", err)
 	}
 
-	cmd := exec.Command("droid")
+	cmd := exec.Command("droid", args...)
 	cmd.Stdin = os.Stdin
 	cmd.Stdout = os.Stdout
 	cmd.Stderr = os.Stderr
@@ -117,7 +119,7 @@ func (d *Droid) Edit(models []string) error {
 		newModels = append(newModels, modelEntry{
 			Model:           model,
 			DisplayName:     model,
-			BaseURL:         "http://localhost:11434/v1",
+			BaseURL:         envconfig.Host().String() + "/v1",
 			APIKey:          "ollama",
 			Provider:        "generic-chat-completion-api",
 			MaxOutputTokens: 64000,

cmd/config/droid_test.go

@@ -218,7 +218,7 @@ func TestDroidEdit(t *testing.T) {
 			}
 		}
 
-		if model["baseUrl"] != "http://localhost:11434/v1" {
+		if model["baseUrl"] != "http://127.0.0.1:11434/v1" {
 			t.Errorf("unexpected baseUrl: %s", model["baseUrl"])
 		}
 		if model["apiKey"] != "ollama" {
@@ -447,7 +447,7 @@ const testDroidSettingsFixture = `{
     {
       "model": "existing-ollama-model",
       "displayName": "existing-ollama-model",
-      "baseUrl": "http://localhost:11434/v1",
+      "baseUrl": "http://127.0.0.1:11434/v1",
       "apiKey": "ollama",
       "provider": "generic-chat-completion-api",
       "maxOutputTokens": 64000,

cmd/config/integrations.go

@@ -4,15 +4,19 @@ import (
 	"context"
 	"errors"
 	"fmt"
+	"io"
 	"maps"
+	"net/http"
 	"os"
 	"os/exec"
+	"path/filepath"
 	"runtime"
 	"slices"
 	"strings"
 	"time"
 
 	"github.com/ollama/ollama/api"
+	"github.com/ollama/ollama/progress"
 	"github.com/spf13/cobra"
 )
 
@@ -22,7 +26,7 @@ import (
 // Runner can run an integration with a model.
 
 type Runner interface {
-	Run(model string) error
+	Run(model string, args []string) error
 	// String returns the human-readable name of the integration
 	String() string
 }
@@ -41,9 +45,186 @@ type Editor interface {
 // integrations is the registry of available integrations.
 var integrations = map[string]Runner{
 	"claude":   &Claude{},
+	"clawdbot": &Openclaw{},
 	"codex":    &Codex{},
+	"moltbot":  &Openclaw{},
 	"droid":    &Droid{},
 	"opencode": &OpenCode{},
+	"openclaw": &Openclaw{},
+}
+
+// recommendedModels are shown when the user has no models or as suggestions.
+// Order matters: local models first, then cloud models.
+var recommendedModels = []ModelItem{
+	{Name: "glm-4.7-flash", Description: "Recommended (requires ~25GB VRAM)"},
+	{Name: "qwen3:8b", Description: "Recommended (requires ~11GB VRAM)"},
+	{Name: "glm-4.7:cloud", Description: "Recommended"},
+	{Name: "kimi-k2.5:cloud", Description: "Recommended"},
+}
+
+// integrationAliases are hidden from the interactive selector but work as CLI arguments.
+var integrationAliases = map[string]bool{
+	"clawdbot": true,
+	"moltbot":  true,
+}
+
+// integrationInstallURLs maps integration names to their install script URLs.
+var integrationInstallURLs = map[string]string{
+	"claude":   "https://claude.ai/install.sh",
+	"openclaw": "https://openclaw.ai/install.sh",
+	"droid":    "https://app.factory.ai/cli",
+	"opencode": "https://opencode.ai/install",
+}
+
+// CanInstallIntegration returns true if we have an install script for this integration.
+func CanInstallIntegration(name string) bool {
+	_, ok := integrationInstallURLs[name]
+	return ok
+}
+
+// IsIntegrationInstalled checks if an integration binary is installed.
+func IsIntegrationInstalled(name string) bool {
+	switch name {
+	case "claude":
+		c := &Claude{}
+		_, err := c.findPath()
+		return err == nil
+	case "openclaw":
+		if _, err := exec.LookPath("openclaw"); err == nil {
+			return true
+		}
+		if _, err := exec.LookPath("clawdbot"); err == nil {
+			return true
+		}
+		return false
+	case "codex":
+		_, err := exec.LookPath("codex")
+		return err == nil
+	case "droid":
+		_, err := exec.LookPath("droid")
+		return err == nil
+	case "opencode":
+		_, err := exec.LookPath("opencode")
+		return err == nil
+	default:
+		return true // Assume installed for unknown integrations
+	}
+}
+
+// InstallIntegration downloads and runs the install script for an integration.
+func InstallIntegration(name string) error {
+	url, ok := integrationInstallURLs[name]
+	if !ok {
+		return fmt.Errorf("no install script available for %s", name)
+	}
+
+	// Download the install script
+	resp, err := http.Get(url)
+	if err != nil {
+		return fmt.Errorf("failed to download install script: %w", err)
+	}
+	defer resp.Body.Close()
+
+	if resp.StatusCode != http.StatusOK {
+		return fmt.Errorf("failed to download install script: HTTP %d", resp.StatusCode)
+	}
+
+	script, err := io.ReadAll(resp.Body)
+	if err != nil {
+		return fmt.Errorf("failed to read install script: %w", err)
+	}
+
+	// Create a temporary file for the script
+	tmpDir := os.TempDir()
+	scriptPath := filepath.Join(tmpDir, fmt.Sprintf("install-%s.sh", name))
+	if err := os.WriteFile(scriptPath, script, 0o700); err != nil {
+		return fmt.Errorf("failed to write install script: %w", err)
+	}
+	defer os.Remove(scriptPath)
+
+	// Execute the script with bash
+	cmd := exec.Command("bash", scriptPath)
+	cmd.Stdin = os.Stdin
+	cmd.Stdout = os.Stdout
+	cmd.Stderr = os.Stderr
+
+	if err := cmd.Run(); err != nil {
+		return fmt.Errorf("install script failed: %w", err)
+	}
+
+	return nil
+}
+
+// SelectModel lets the user select a model to run.
+// ModelItem represents a model for selection.
+type ModelItem struct {
+	Name        string
+	Description string
+}
+
+// SingleSelector is a function type for single item selection.
+type SingleSelector func(title string, items []ModelItem) (string, error)
+
+// MultiSelector is a function type for multi item selection.
+type MultiSelector func(title string, items []ModelItem, preChecked []string) ([]string, error)
+
+// SelectModelWithSelector prompts the user to select a model using the provided selector.
+func SelectModelWithSelector(ctx context.Context, selector SingleSelector) (string, error) {
+	client, err := api.ClientFromEnvironment()
+	if err != nil {
+		return "", err
+	}
+
+	models, err := client.List(ctx)
+	if err != nil {
+		return "", err
+	}
+
+	if len(models.Models) == 0 {
+		return "", fmt.Errorf("no models available, run 'ollama pull <model>' first")
+	}
+
+	lastModel := LastModel()
+
+	var items []ModelItem
+	for _, m := range models.Models {
+		items = append(items, ModelItem{Name: m.Name})
+	}
+
+	// Sort with last model first, then alphabetically
+	slices.SortFunc(items, func(a, b ModelItem) int {
+		aIsLast := a.Name == lastModel
+		bIsLast := b.Name == lastModel
+		if aIsLast != bIsLast {
+			if aIsLast {
+				return -1
+			}
+			return 1
+		}
+		return strings.Compare(strings.ToLower(a.Name), strings.ToLower(b.Name))
+	})
+
+	return selector("Select model to run:", items)
+}
+
+func SelectModel(ctx context.Context) (string, error) {
+	return SelectModelWithSelector(ctx, defaultSingleSelector)
+}
+
+func defaultSingleSelector(title string, items []ModelItem) (string, error) {
+	selectItems := make([]selectItem, len(items))
+	for i, item := range items {
+		selectItems[i] = selectItem(item)
+	}
+	return selectPrompt(title, selectItems)
+}
+
+func defaultMultiSelector(title string, items []ModelItem, preChecked []string) ([]string, error) {
+	selectItems := make([]selectItem, len(items))
+	for i, item := range items {
+		selectItems[i] = selectItem(item)
+	}
+	return multiSelectPrompt(title, selectItems, preChecked)
 }
 
 func selectIntegration() (string, error) {
@@ -54,6 +235,9 @@ func selectIntegration() (string, error) {
 	names := slices.Sorted(maps.Keys(integrations))
 	var items []selectItem
 	for _, name := range names {
+		if integrationAliases[name] {
+			continue
+		}
 		r := integrations[name]
 		description := r.String()
 		if conn, err := loadIntegration(name); err == nil && len(conn.Models) > 0 {
@@ -65,8 +249,8 @@ func selectIntegration() (string, error) {
 	return selectPrompt("Select integration:", items)
 }
 
-// selectModels lets the user select models for an integration
-func selectModels(ctx context.Context, name, current string) ([]string, error) {
+// selectModelsWithSelectors lets the user select models for an integration using provided selectors.
+func selectModelsWithSelectors(ctx context.Context, name, current string, single SingleSelector, multi MultiSelector) ([]string, error) {
 	r, ok := integrations[name]
 	if !ok {
 		return nil, fmt.Errorf("unknown integration: %s", name)
@@ -82,76 +266,59 @@ func selectModels(ctx context.Context, name, current string) ([]string, error) {
 		return nil, err
 	}
 
-	if len(models.Models) == 0 {
-		return nil, fmt.Errorf("no models available, run 'ollama pull <model>' first")
-	}
-
-	var items []selectItem
-	cloudModels := make(map[string]bool)
+	var existing []modelInfo
 	for _, m := range models.Models {
-		if m.RemoteModel != "" {
-			cloudModels[m.Name] = true
-		}
-		items = append(items, selectItem{Name: m.Name})
+		existing = append(existing, modelInfo{Name: m.Name, Remote: m.RemoteModel != ""})
 	}
 
-	if len(items) == 0 {
-		return nil, fmt.Errorf("no local models available, run 'ollama pull <model>' first")
-	}
-
-	// Get previously configured models (saved config takes precedence)
 	var preChecked []string
 	if saved, err := loadIntegration(name); err == nil {
 		preChecked = saved.Models
 	} else if editor, ok := r.(Editor); ok {
 		preChecked = editor.Models()
 	}
-	checked := make(map[string]bool, len(preChecked))
-	for _, n := range preChecked {
-		checked[n] = true
-	}
 
-	// Resolve current to full name (e.g., "llama3.2" -> "llama3.2:latest")
-	for _, item := range items {
-		if item.Name == current || strings.HasPrefix(item.Name, current+":") {
-			current = item.Name
-			break
-		}
-	}
+	items, preChecked, existingModels, cloudModels := buildModelList(existing, preChecked, current)
 
-	// If current model is configured, move to front of preChecked
-	if checked[current] {
-		preChecked = append([]string{current}, slices.DeleteFunc(preChecked, func(m string) bool { return m == current })...)
+	if len(items) == 0 {
+		return nil, fmt.Errorf("no models available")
 	}
 
-	// Sort: checked first, then alphabetical
-	slices.SortFunc(items, func(a, b selectItem) int {
-		ac, bc := checked[a.Name], checked[b.Name]
-		if ac != bc {
-			if ac {
-				return -1
-			}
-			return 1
-		}
-		return strings.Compare(strings.ToLower(a.Name), strings.ToLower(b.Name))
-	})
-
 	var selected []string
-	// only editors support multi-model selection
 	if _, ok := r.(Editor); ok {
-		selected, err = multiSelectPrompt(fmt.Sprintf("Select models for %s:", r), items, preChecked)
+		selected, err = multi(fmt.Sprintf("Select models for %s:", r), items, preChecked)
 		if err != nil {
 			return nil, err
 		}
 	} else {
-		model, err := selectPrompt(fmt.Sprintf("Select model for %s:", r), items)
+		model, err := single(fmt.Sprintf("Select model for %s:", r), items)
 		if err != nil {
 			return nil, err
 		}
 		selected = []string{model}
 	}
 
-	// if any model in selected is a cloud model, ensure signed in
+	var toPull []string
+	for _, m := range selected {
+		if !existingModels[m] {
+			toPull = append(toPull, m)
+		}
+	}
+	if len(toPull) > 0 {
+		msg := fmt.Sprintf("Download %s?", strings.Join(toPull, ", "))
+		if ok, err := confirmPrompt(msg); err != nil {
+			return nil, err
+		} else if !ok {
+			return nil, errCancelled
+		}
+		for _, m := range toPull {
+			fmt.Fprintf(os.Stderr, "\n")
+			if err := pullModel(ctx, client, m); err != nil {
+				return nil, fmt.Errorf("failed to pull %s: %w", m, err)
+			}
+		}
+	}
+
 	var selectedCloudModels []string
 	for _, m := range selected {
 		if cloudModels[m] {
@@ -221,43 +388,149 @@ func selectModels(ctx context.Context, name, current string) ([]string, error) {
 	return selected, nil
 }
 
-func runIntegration(name, modelName string) error {
+// selectModels lets the user select models for an integration using default selectors.
+func selectModels(ctx context.Context, name, current string) ([]string, error) {
+	return selectModelsWithSelectors(ctx, name, current, defaultSingleSelector, defaultMultiSelector)
+}
+
+func runIntegration(name, modelName string, args []string) error {
 	r, ok := integrations[name]
 	if !ok {
 		return fmt.Errorf("unknown integration: %s", name)
 	}
 	fmt.Fprintf(os.Stderr, "\nLaunching %s with %s...\n", r, modelName)
-	return r.Run(modelName)
+	return r.Run(modelName, args)
+}
+
+// LaunchIntegration launches the named integration using saved config or prompts for setup.
+func LaunchIntegration(name string) error {
+	r, ok := integrations[name]
+	if !ok {
+		return fmt.Errorf("unknown integration: %s", name)
+	}
+
+	// Try to use saved config
+	if config, err := loadIntegration(name); err == nil && len(config.Models) > 0 {
+		return runIntegration(name, config.Models[0], nil)
+	}
+
+	// No saved config - prompt user to run setup
+	return fmt.Errorf("%s is not configured. Run 'ollama launch %s' to set it up", r, name)
+}
+
+// ConfigureIntegrationWithSelectors allows the user to select/change the model for an integration using custom selectors.
+func ConfigureIntegrationWithSelectors(ctx context.Context, name string, single SingleSelector, multi MultiSelector) error {
+	r, ok := integrations[name]
+	if !ok {
+		return fmt.Errorf("unknown integration: %s", name)
+	}
+
+	models, err := selectModelsWithSelectors(ctx, name, "", single, multi)
+	if errors.Is(err, errCancelled) {
+		return nil
+	}
+	if err != nil {
+		return err
+	}
+
+	if editor, isEditor := r.(Editor); isEditor {
+		paths := editor.Paths()
+		if len(paths) > 0 {
+			fmt.Fprintf(os.Stderr, "This will modify your %s configuration:\n", r)
+			for _, p := range paths {
+				fmt.Fprintf(os.Stderr, "  %s\n", p)
+			}
+			fmt.Fprintf(os.Stderr, "Backups will be saved to %s/\n\n", backupDir())
+
+			if ok, _ := confirmPrompt("Proceed?"); !ok {
+				return nil
+			}
+		}
+
+		if err := editor.Edit(models); err != nil {
+			return fmt.Errorf("setup failed: %w", err)
+		}
+	}
+
+	if err := saveIntegration(name, models); err != nil {
+		return fmt.Errorf("failed to save: %w", err)
+	}
+
+	if len(models) == 1 {
+		fmt.Fprintf(os.Stderr, "Configured %s with %s\n", r, models[0])
+	} else {
+		fmt.Fprintf(os.Stderr, "Configured %s with %d models (default: %s)\n", r, len(models), models[0])
+	}
+
+	return nil
+}
+
+// ConfigureIntegration allows the user to select/change the model for an integration.
+func ConfigureIntegration(ctx context.Context, name string) error {
+	return ConfigureIntegrationWithSelectors(ctx, name, defaultSingleSelector, defaultMultiSelector)
 }
 
 // LaunchCmd returns the cobra command for launching integrations.
-func LaunchCmd(checkServerHeartbeat func(cmd *cobra.Command, args []string) error) *cobra.Command {
+// The runTUI callback is called when no arguments are provided (alias for main TUI).
+func LaunchCmd(checkServerHeartbeat func(cmd *cobra.Command, args []string) error, runTUI func(cmd *cobra.Command)) *cobra.Command {
 	var modelFlag string
 	var configFlag bool
 
 	cmd := &cobra.Command{
-		Use:   "launch [INTEGRATION]",
-		Short: "Launch an integration with Ollama",
-		Long: `Launch an integration configured with Ollama models.
+		Use:   "launch [INTEGRATION] [-- [EXTRA_ARGS...]]",
+		Short: "Launch the Ollama menu or an integration",
+		Long: `Launch the Ollama interactive menu, or directly launch a specific integration.
+
+Without arguments, this is equivalent to running 'ollama' directly.
 
 Supported integrations:
   claude    Claude Code
   codex     Codex
   droid     Droid
   opencode  OpenCode
+  openclaw  OpenClaw (aliases: clawdbot, moltbot)
 
 Examples:
   ollama launch
   ollama launch claude
   ollama launch claude --model <model>
-  ollama launch droid --config (does not auto-launch)`,
-		Args:    cobra.MaximumNArgs(1),
+  ollama launch droid --config (does not auto-launch)
+  ollama launch codex -- -p myprofile (pass extra args to integration)
+  ollama launch codex -- --sandbox workspace-write`,
+		Args:    cobra.ArbitraryArgs,
 		PreRunE: checkServerHeartbeat,
 		RunE: func(cmd *cobra.Command, args []string) error {
+			// No args - run the main TUI (same as 'ollama')
+			if len(args) == 0 && modelFlag == "" && !configFlag {
+				runTUI(cmd)
+				return nil
+			}
+
+			// Extract integration name and args to pass through using -- separator
 			var name string
-			if len(args) > 0 {
-				name = args[0]
+			var passArgs []string
+			dashIdx := cmd.ArgsLenAtDash()
+
+			if dashIdx == -1 {
+				// No "--" separator: only allow 0 or 1 args (integration name)
+				if len(args) > 1 {
+					return fmt.Errorf("unexpected arguments: %v\nUse '--' to pass extra arguments to the integration", args[1:])
+				}
+				if len(args) == 1 {
+					name = args[0]
+				}
 			} else {
+				// "--" was used: args before it = integration name, args after = passthrough
+				if dashIdx > 1 {
+					return fmt.Errorf("expected at most 1 integration name before '--', got %d", dashIdx)
+				}
+				if dashIdx == 1 {
+					name = args[0]
+				}
+				passArgs = args[dashIdx:]
+			}
+
+			if name == "" {
 				var err error
 				name, err = selectIntegration()
 				if errors.Is(err, errCancelled) {
@@ -273,16 +546,14 @@ Examples:
 				return fmt.Errorf("unknown integration: %s", name)
 			}
 
-			// If launching without --model, use saved config if available
 			if !configFlag && modelFlag == "" {
 				if config, err := loadIntegration(name); err == nil && len(config.Models) > 0 {
-					return runIntegration(name, config.Models[0])
+					return runIntegration(name, config.Models[0], passArgs)
 				}
 			}
 
 			var models []string
 			if modelFlag != "" {
-				// When --model is specified, merge with existing models (new model becomes default)
 				models = []string{modelFlag}
 				if existing, err := loadIntegration(name); err == nil && len(existing.Models) > 0 {
 					for _, m := range existing.Models {
@@ -337,13 +608,13 @@ Examples:
 
 			if configFlag {
 				if launch, _ := confirmPrompt(fmt.Sprintf("\nLaunch %s now?", r)); launch {
-					return runIntegration(name, models[0])
+					return runIntegration(name, models[0], passArgs)
 				}
 				fmt.Fprintf(os.Stderr, "Run 'ollama launch %s' to start with %s\n", strings.ToLower(name), models[0])
 				return nil
 			}
 
-			return runIntegration(name, models[0])
+			return runIntegration(name, models[0], passArgs)
 		},
 	}
 
@@ -351,3 +622,154 @@ Examples:
 	cmd.Flags().BoolVar(&configFlag, "config", false, "Configure without launching")
 	return cmd
 }
+
+type modelInfo struct {
+	Name   string
+	Remote bool
+}
+
+// buildModelList merges existing models with recommendations, sorts them, and returns
+// the ordered items along with maps of existing and cloud model names.
+func buildModelList(existing []modelInfo, preChecked []string, current string) (items []ModelItem, orderedChecked []string, existingModels, cloudModels map[string]bool) {
+	existingModels = make(map[string]bool)
+	cloudModels = make(map[string]bool)
+	recommended := make(map[string]bool)
+	var hasLocalModel, hasCloudModel bool
+
+	for _, rec := range recommendedModels {
+		recommended[rec.Name] = true
+	}
+
+	for _, m := range existing {
+		existingModels[m.Name] = true
+		if m.Remote {
+			cloudModels[m.Name] = true
+			hasCloudModel = true
+		} else {
+			hasLocalModel = true
+		}
+		displayName := strings.TrimSuffix(m.Name, ":latest")
+		existingModels[displayName] = true
+		item := ModelItem{Name: displayName}
+		if recommended[displayName] {
+			item.Description = "recommended"
+		}
+		items = append(items, item)
+	}
+
+	for _, rec := range recommendedModels {
+		if existingModels[rec.Name] || existingModels[rec.Name+":latest"] {
+			continue
+		}
+		items = append(items, rec)
+		if isCloudModel(rec.Name) {
+			cloudModels[rec.Name] = true
+		}
+	}
+
+	checked := make(map[string]bool, len(preChecked))
+	for _, n := range preChecked {
+		checked[n] = true
+	}
+
+	// Resolve current to full name (e.g., "llama3.2" -> "llama3.2:latest")
+	for _, item := range items {
+		if item.Name == current || strings.HasPrefix(item.Name, current+":") {
+			current = item.Name
+			break
+		}
+	}
+
+	if checked[current] {
+		preChecked = append([]string{current}, slices.DeleteFunc(preChecked, func(m string) bool { return m == current })...)
+	}
+
+	// Non-existing models get "install?" suffix and are pushed to the bottom.
+	// When user has no models, preserve recommended order.
+	notInstalled := make(map[string]bool)
+	for i := range items {
+		if !existingModels[items[i].Name] {
+			notInstalled[items[i].Name] = true
+			if items[i].Description != "" {
+				items[i].Description += ", install?"
+			} else {
+				items[i].Description = "install?"
+			}
+		}
+	}
+
+	if hasLocalModel || hasCloudModel {
+		slices.SortStableFunc(items, func(a, b ModelItem) int {
+			ac, bc := checked[a.Name], checked[b.Name]
+			aNew, bNew := notInstalled[a.Name], notInstalled[b.Name]
+
+			if ac != bc {
+				if ac {
+					return -1
+				}
+				return 1
+			}
+			if !ac && !bc && aNew != bNew {
+				if aNew {
+					return 1
+				}
+				return -1
+			}
+			return strings.Compare(strings.ToLower(a.Name), strings.ToLower(b.Name))
+		})
+	}
+
+	return items, preChecked, existingModels, cloudModels
+}
+
+func isCloudModel(name string) bool {
+	return strings.HasSuffix(name, ":cloud")
+}
+
+func pullModel(ctx context.Context, client *api.Client, model string) error {
+	p := progress.NewProgress(os.Stderr)
+	defer p.Stop()
+
+	bars := make(map[string]*progress.Bar)
+	var status string
+	var spinner *progress.Spinner
+
+	fn := func(resp api.ProgressResponse) error {
+		if resp.Digest != "" {
+			if resp.Completed == 0 {
+				return nil
+			}
+
+			if spinner != nil {
+				spinner.Stop()
+			}
+
+			bar, ok := bars[resp.Digest]
+			if !ok {
+				name, isDigest := strings.CutPrefix(resp.Digest, "sha256:")
+				name = strings.TrimSpace(name)
+				if isDigest {
+					name = name[:min(12, len(name))]
+				}
+				bar = progress.NewBar(fmt.Sprintf("pulling %s:", name), resp.Total, resp.Completed)
+				bars[resp.Digest] = bar
+				p.Add(resp.Digest, bar)
+			}
+
+			bar.Set(resp.Completed)
+		} else if status != resp.Status {
+			if spinner != nil {
+				spinner.Stop()
+			}
+
+			status = resp.Status
+			spinner = progress.NewSpinner(status)
+			p.Add(status, spinner)
+		}
+
+		return nil
+	}
+
+	request := api.PullRequest{Name: model}
+	return client.Pull(ctx, &request, fn)
+}

cmd/config/integrations_test.go

@@ -1,10 +1,12 @@
 package config
 
 import (
+	"fmt"
 	"slices"
 	"strings"
 	"testing"
 
+	"github.com/google/go-cmp/cmp"
 	"github.com/spf13/cobra"
 )
 
@@ -86,12 +88,14 @@ func TestLaunchCmd(t *testing.T) {
 	mockCheck := func(cmd *cobra.Command, args []string) error {
 		return nil
 	}
+	// Mock TUI function (not called in these tests)
+	mockTUI := func(cmd *cobra.Command) {}
 
-	cmd := LaunchCmd(mockCheck)
+	cmd := LaunchCmd(mockCheck, mockTUI)
 
 	t.Run("command structure", func(t *testing.T) {
-		if cmd.Use != "launch [INTEGRATION]" {
-			t.Errorf("Use = %q, want %q", cmd.Use, "launch [INTEGRATION]")
+		if cmd.Use != "launch [INTEGRATION] [-- [EXTRA_ARGS...]]" {
+			t.Errorf("Use = %q, want %q", cmd.Use, "launch [INTEGRATION] [-- [EXTRA_ARGS...]]")
 		}
 		if cmd.Short == "" {
 			t.Error("Short description should not be empty")
@@ -120,8 +124,77 @@ func TestLaunchCmd(t *testing.T) {
 	})
 }
 
+func TestLaunchCmd_TUICallback(t *testing.T) {
+	mockCheck := func(cmd *cobra.Command, args []string) error {
+		return nil
+	}
+
+	t.Run("no args calls TUI", func(t *testing.T) {
+		tuiCalled := false
+		mockTUI := func(cmd *cobra.Command) {
+			tuiCalled = true
+		}
+
+		cmd := LaunchCmd(mockCheck, mockTUI)
+		cmd.SetArgs([]string{})
+		_ = cmd.Execute()
+
+		if !tuiCalled {
+			t.Error("TUI callback should be called when no args provided")
+		}
+	})
+
+	t.Run("integration arg bypasses TUI", func(t *testing.T) {
+		tuiCalled := false
+		mockTUI := func(cmd *cobra.Command) {
+			tuiCalled = true
+		}
+
+		cmd := LaunchCmd(mockCheck, mockTUI)
+		cmd.SetArgs([]string{"claude"})
+		// Will error because claude isn't configured, but that's OK
+		_ = cmd.Execute()
+
+		if tuiCalled {
+			t.Error("TUI callback should NOT be called when integration arg provided")
+		}
+	})
+
+	t.Run("--model flag bypasses TUI", func(t *testing.T) {
+		tuiCalled := false
+		mockTUI := func(cmd *cobra.Command) {
+			tuiCalled = true
+		}
+
+		cmd := LaunchCmd(mockCheck, mockTUI)
+		cmd.SetArgs([]string{"--model", "test-model"})
+		// Will error because no integration specified, but that's OK
+		_ = cmd.Execute()
+
+		if tuiCalled {
+			t.Error("TUI callback should NOT be called when --model flag provided")
+		}
+	})
+
+	t.Run("--config flag bypasses TUI", func(t *testing.T) {
+		tuiCalled := false
+		mockTUI := func(cmd *cobra.Command) {
+			tuiCalled = true
+		}
+
+		cmd := LaunchCmd(mockCheck, mockTUI)
+		cmd.SetArgs([]string{"--config"})
+		// Will error because no integration specified, but that's OK
+		_ = cmd.Execute()
+
+		if tuiCalled {
+			t.Error("TUI callback should NOT be called when --config flag provided")
+		}
+	})
+}
+
 func TestRunIntegration_UnknownIntegration(t *testing.T) {
-	err := runIntegration("unknown-integration", "model")
+	err := runIntegration("unknown-integration", "model", nil)
 	if err == nil {
 		t.Error("expected error for unknown integration, got nil")
 	}
@@ -160,7 +233,7 @@ func TestHasLocalModel_DocumentsHeuristic(t *testing.T) {
 
 func TestLaunchCmd_NilHeartbeat(t *testing.T) {
 	// This should not panic - cmd creation should work even with nil
-	cmd := LaunchCmd(nil)
+	cmd := LaunchCmd(nil, nil)
 	if cmd == nil {
 		t.Fatal("LaunchCmd returned nil")
 	}
@@ -174,15 +247,336 @@ func TestLaunchCmd_NilHeartbeat(t *testing.T) {
 func TestAllIntegrations_HaveRequiredMethods(t *testing.T) {
 	for name, r := range integrations {
 		t.Run(name, func(t *testing.T) {
-			// Test String() doesn't panic and returns non-empty
 			displayName := r.String()
 			if displayName == "" {
 				t.Error("String() should not return empty")
 			}
-
-			// Test Run() exists (we can't call it without actually running the command)
-			// Just verify the method is available
-			var _ func(string) error = r.Run
+			var _ func(string, []string) error = r.Run
 		})
 	}
 }
+
+func TestParseArgs(t *testing.T) {
+	// Tests reflect cobra's ArgsLenAtDash() semantics:
+	// - cobra strips "--" from args
+	// - ArgsLenAtDash() returns the index where "--" was, or -1
+	tests := []struct {
+		name     string
+		args     []string // args as cobra delivers them (no "--")
+		dashIdx  int      // what ArgsLenAtDash() returns
+		wantName string
+		wantArgs []string
+		wantErr  bool
+	}{
+		{
+			name:     "no extra args, no dash",
+			args:     []string{"claude"},
+			dashIdx:  -1,
+			wantName: "claude",
+		},
+		{
+			name:     "with extra args after --",
+			args:     []string{"codex", "-p", "myprofile"},
+			dashIdx:  1,
+			wantName: "codex",
+			wantArgs: []string{"-p", "myprofile"},
+		},
+		{
+			name:     "extra args only after --",
+			args:     []string{"codex", "--sandbox", "workspace-write"},
+			dashIdx:  1,
+			wantName: "codex",
+			wantArgs: []string{"--sandbox", "workspace-write"},
+		},
+		{
+			name:     "-- at end with no args after",
+			args:     []string{"claude"},
+			dashIdx:  1,
+			wantName: "claude",
+		},
+		{
+			name:     "-- with no integration name",
+			args:     []string{"--verbose"},
+			dashIdx:  0,
+			wantName: "",
+			wantArgs: []string{"--verbose"},
+		},
+		{
+			name:    "multiple args before -- is error",
+			args:    []string{"claude", "codex", "--verbose"},
+			dashIdx: 2,
+			wantErr: true,
+		},
+		{
+			name:    "multiple args without -- is error",
+			args:    []string{"claude", "codex"},
+			dashIdx: -1,
+			wantErr: true,
+		},
+		{
+			name:     "no args, no dash",
+			args:     []string{},
+			dashIdx:  -1,
+			wantName: "",
+		},
+	}
+
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			// Simulate the parsing logic from LaunchCmd using dashIdx
+			var name string
+			var parsedArgs []string
+			var err error
+
+			dashIdx := tt.dashIdx
+			args := tt.args
+
+			if dashIdx == -1 {
+				if len(args) > 1 {
+					err = fmt.Errorf("unexpected arguments: %v", args[1:])
+				} else if len(args) == 1 {
+					name = args[0]
+				}
+			} else {
+				if dashIdx > 1 {
+					err = fmt.Errorf("expected at most 1 integration name before '--', got %d", dashIdx)
+				} else {
+					if dashIdx == 1 {
+						name = args[0]
+					}
+					parsedArgs = args[dashIdx:]
+				}
+			}
+
+			if tt.wantErr {
+				if err == nil {
+					t.Fatal("expected error, got nil")
+				}
+				return
+			}
+			if err != nil {
+				t.Fatalf("unexpected error: %v", err)
+			}
+			if name != tt.wantName {
+				t.Errorf("name = %q, want %q", name, tt.wantName)
+			}
+			if !slices.Equal(parsedArgs, tt.wantArgs) {
+				t.Errorf("args = %v, want %v", parsedArgs, tt.wantArgs)
+			}
+		})
+	}
+}
+
+func TestIsCloudModel(t *testing.T) {
+	tests := []struct {
+		name string
+		want bool
+	}{
+		{"glm-4.7:cloud", true},
+		{"kimi-k2.5:cloud", true},
+		{"glm-4.7-flash", false},
+		{"glm-4.7-flash:latest", false},
+		{"cloud-model", false},
+		{"model:cloudish", false},
+	}
+	for _, tt := range tests {
+		t.Run(tt.name, func(t *testing.T) {
+			if got := isCloudModel(tt.name); got != tt.want {
+				t.Errorf("isCloudModel(%q) = %v, want %v", tt.name, got, tt.want)
+			}
+		})
+	}
+}
+
+func names(items []ModelItem) []string {
+	var out []string
+	for _, item := range items {
+		out = append(out, item.Name)
+	}
+	return out
+}
+
+func TestBuildModelList_NoExistingModels(t *testing.T) {
+	items, _, _, _ := buildModelList(nil, nil, "")
+
+	want := []string{"glm-4.7-flash", "qwen3:8b", "glm-4.7:cloud", "kimi-k2.5:cloud"}
+	if diff := cmp.Diff(want, names(items)); diff != "" {
+		t.Errorf("with no existing models, items should be recommended in order (-want +got):\n%s", diff)
+	}
+
+	for _, item := range items {
+		if !strings.HasSuffix(item.Description, "install?") {
+			t.Errorf("item %q should have description ending with 'install?', got %q", item.Name, item.Description)
+		}
+	}
+}
+
+func TestBuildModelList_OnlyLocalModels_CloudRecsAtBottom(t *testing.T) {
+	existing := []modelInfo{
+		{Name: "llama3.2:latest", Remote: false},
+		{Name: "qwen2.5:latest", Remote: false},
+	}
+
+	items, _, _, _ := buildModelList(existing, nil, "")
+	got := names(items)
+
+	want := []string{"llama3.2", "qwen2.5", "glm-4.7-flash", "glm-4.7:cloud", "kimi-k2.5:cloud", "qwen3:8b"}
+	if diff := cmp.Diff(want, got); diff != "" {
+		t.Errorf("cloud recs should be at bottom (-want +got):\n%s", diff)
+	}
+}
+
+func TestBuildModelList_BothCloudAndLocal_RegularSort(t *testing.T) {
+	existing := []modelInfo{
+		{Name: "llama3.2:latest", Remote: false},
+		{Name: "glm-4.7:cloud", Remote: true},
+	}
+
+	items, _, _, _ := buildModelList(existing, nil, "")
+	got := names(items)
+
+	want := []string{"glm-4.7:cloud", "llama3.2", "glm-4.7-flash", "kimi-k2.5:cloud", "qwen3:8b"}
+	if diff := cmp.Diff(want, got); diff != "" {
+		t.Errorf("mixed models should be alphabetical (-want +got):\n%s", diff)
+	}
+}
+
+func TestBuildModelList_PreCheckedFirst(t *testing.T) {
+	existing := []modelInfo{
+		{Name: "llama3.2:latest", Remote: false},
+		{Name: "glm-4.7:cloud", Remote: true},
+	}
+
+	items, _, _, _ := buildModelList(existing, []string{"llama3.2"}, "")
+	got := names(items)
+
+	if got[0] != "llama3.2" {
+		t.Errorf("pre-checked model should be first, got %v", got)
+	}
+}
+
+func TestBuildModelList_ExistingRecommendedMarked(t *testing.T) {
+	existing := []modelInfo{
+		{Name: "glm-4.7-flash", Remote: false},
+		{Name: "glm-4.7:cloud", Remote: true},
+	}
+
+	items, _, _, _ := buildModelList(existing, nil, "")
+
+	for _, item := range items {
+		switch item.Name {
+		case "glm-4.7-flash", "glm-4.7:cloud":
+			if strings.HasSuffix(item.Description, "install?") {
+				t.Errorf("installed recommended %q should not have 'install?' suffix, got %q", item.Name, item.Description)
+			}
+		case "kimi-k2.5:cloud", "qwen3:8b":
+			if !strings.HasSuffix(item.Description, "install?") {
+				t.Errorf("non-installed recommended %q should have 'install?' suffix, got %q", item.Name, item.Description)
+			}
+		}
+	}
+}
+
+func TestBuildModelList_ExistingCloudModelsNotPushedToBottom(t *testing.T) {
+	existing := []modelInfo{
+		{Name: "glm-4.7-flash", Remote: false},
+		{Name: "glm-4.7:cloud", Remote: true},
+	}
+
+	items, _, _, _ := buildModelList(existing, nil, "")
+	got := names(items)
+
+	// glm-4.7-flash and glm-4.7:cloud are installed so they sort normally;
+	// kimi-k2.5:cloud and qwen3:8b are not installed so they go to the bottom
+	want := []string{"glm-4.7-flash", "glm-4.7:cloud", "kimi-k2.5:cloud", "qwen3:8b"}
+	if diff := cmp.Diff(want, got); diff != "" {
+		t.Errorf("existing cloud models should sort normally (-want +got):\n%s", diff)
+	}
+}
+
+func TestBuildModelList_HasRecommendedCloudModel_OnlyNonInstalledAtBottom(t *testing.T) {
+	existing := []modelInfo{
+		{Name: "llama3.2:latest", Remote: false},
+		{Name: "kimi-k2.5:cloud", Remote: true},
+	}
+
+	items, _, _, _ := buildModelList(existing, nil, "")
+	got := names(items)
+
+	// kimi-k2.5:cloud is installed so it sorts normally;
+	// the rest of the recommendations are not installed so they go to the bottom
+	want := []string{"kimi-k2.5:cloud", "llama3.2", "glm-4.7-flash", "glm-4.7:cloud", "qwen3:8b"}
+	if diff := cmp.Diff(want, got); diff != "" {
+		t.Errorf("only non-installed models should be at bottom (-want +got):\n%s", diff)
+	}
+
+	for _, item := range items {
+		if !slices.Contains([]string{"kimi-k2.5:cloud", "llama3.2"}, item.Name) {
+			if !strings.HasSuffix(item.Description, "install?") {
+				t.Errorf("non-installed %q should have 'install?' suffix, got %q", item.Name, item.Description)
+			}
+		}
+	}
+}
+
+func TestBuildModelList_LatestTagStripped(t *testing.T) {
+	existing := []modelInfo{
+		{Name: "glm-4.7-flash:latest", Remote: false},
+		{Name: "llama3.2:latest", Remote: false},
+	}
+
+	items, _, existingModels, _ := buildModelList(existing, nil, "")
+	got := names(items)
+
+	// :latest should be stripped from display names
+	for _, name := range got {
+		if strings.HasSuffix(name, ":latest") {
+			t.Errorf("name %q should not have :latest suffix", name)
+		}
+	}
+
+	// glm-4.7-flash should not be duplicated (existing :latest matches the recommendation)
+	count := 0
+	for _, name := range got {
+		if name == "glm-4.7-flash" {
+			count++
+		}
+	}
+	if count != 1 {
+		t.Errorf("glm-4.7-flash should appear exactly once, got %d in %v", count, got)
+	}
+
+	// Stripped name should be in existingModels so it won't be pulled
+	if !existingModels["glm-4.7-flash"] {
+		t.Error("glm-4.7-flash should be in existingModels")
+	}
+}
+
+func TestBuildModelList_ReturnsExistingAndCloudMaps(t *testing.T) {
+	existing := []modelInfo{
+		{Name: "llama3.2:latest", Remote: false},
+		{Name: "glm-4.7:cloud", Remote: true},
+	}
+
+	_, _, existingModels, cloudModels := buildModelList(existing, nil, "")
+
+	if !existingModels["llama3.2"] {
+		t.Error("llama3.2 should be in existingModels")
+	}
+	if !existingModels["glm-4.7:cloud"] {
+		t.Error("glm-4.7:cloud should be in existingModels")
+	}
+	if existingModels["glm-4.7-flash"] {
+		t.Error("glm-4.7-flash should not be in existingModels (it's a recommendation)")
+	}
+
+	if !cloudModels["glm-4.7:cloud"] {
+		t.Error("glm-4.7:cloud should be in cloudModels")
+	}
+	if !cloudModels["kimi-k2.5:cloud"] {
+		t.Error("kimi-k2.5:cloud should be in cloudModels (recommended cloud)")
+	}
+	if cloudModels["llama3.2"] {
+		t.Error("llama3.2 should not be in cloudModels")
+	}
+}

cmd/config/openclaw.go

@@ -0,0 +1,254 @@
+package config
+
+import (
+	"bytes"
+	"encoding/json"
+	"fmt"
+	"io"
+	"os"
+	"os/exec"
+	"path/filepath"
+	"strings"
+
+	"github.com/ollama/ollama/envconfig"
+)
+
+type Openclaw struct{}
+
+func (c *Openclaw) String() string { return "OpenClaw" }
+
+const ansiGreen = "\033[32m"
+
+func (c *Openclaw) Run(model string, args []string) error {
+	bin := "openclaw"
+	if _, err := exec.LookPath(bin); err != nil {
+		bin = "clawdbot"
+		if _, err := exec.LookPath(bin); err != nil {
+			return fmt.Errorf("openclaw is not installed, install from https://docs.openclaw.ai")
+		}
+	}
+
+	models := []string{model}
+	if config, err := loadIntegration("openclaw"); err == nil && len(config.Models) > 0 {
+		models = config.Models
+	} else if config, err := loadIntegration("clawdbot"); err == nil && len(config.Models) > 0 {
+		models = config.Models
+	}
+	if err := c.Edit(models); err != nil {
+		return fmt.Errorf("setup failed: %w", err)
+	}
+
+	if !c.onboarded() {
+		// Onboarding not completed: run it (model already set via Edit)
+		// Use "ollama" as gateway token for simple local access
+		cmd := exec.Command(bin, "onboard",
+			"--auth-choice", "skip",
+			"--gateway-token", "ollama",
+		)
+		cmd.Stdin = os.Stdin
+		cmd.Stdout = os.Stdout
+		cmd.Stderr = os.Stderr
+		return cmd.Run()
+	}
+
+	// Onboarding completed: run gateway
+	cmd := exec.Command(bin, append([]string{"gateway"}, args...)...)
+	cmd.Stdin = os.Stdin
+
+	// Capture output to detect "already running" message
+	var outputBuf bytes.Buffer
+	cmd.Stdout = io.MultiWriter(os.Stdout, &outputBuf)
+	cmd.Stderr = io.MultiWriter(os.Stderr, &outputBuf)
+
+	err := cmd.Run()
+	if err != nil && strings.Contains(outputBuf.String(), "Gateway already running") {
+		fmt.Fprintf(os.Stderr, "%sOpenClaw has been configured with Ollama. Gateway is already running.%s\n", ansiGreen, ansiReset)
+		return nil
+	}
+	return err
+}
+
+// onboarded checks if OpenClaw onboarding wizard was completed
+// by looking for the wizard.lastRunAt marker in the config
+func (c *Openclaw) onboarded() bool {
+	home, err := os.UserHomeDir()
+	if err != nil {
+		return false
+	}
+
+	configPath := filepath.Join(home, ".openclaw", "openclaw.json")
+	legacyPath := filepath.Join(home, ".clawdbot", "clawdbot.json")
+
+	config := make(map[string]any)
+	if data, err := os.ReadFile(configPath); err == nil {
+		_ = json.Unmarshal(data, &config)
+	} else if data, err := os.ReadFile(legacyPath); err == nil {
+		_ = json.Unmarshal(data, &config)
+	} else {
+		return false
+	}
+
+	// Check for wizard.lastRunAt marker (set when onboarding completes)
+	wizard, _ := config["wizard"].(map[string]any)
+	if wizard == nil {
+		return false
+	}
+	lastRunAt, _ := wizard["lastRunAt"].(string)
+	return lastRunAt != ""
+}
+
+func (c *Openclaw) Paths() []string {
+	home, _ := os.UserHomeDir()
+	p := filepath.Join(home, ".openclaw", "openclaw.json")
+	if _, err := os.Stat(p); err == nil {
+		return []string{p}
+	}
+	legacy := filepath.Join(home, ".clawdbot", "clawdbot.json")
+	if _, err := os.Stat(legacy); err == nil {
+		return []string{legacy}
+	}
+	return nil
+}
+
+func (c *Openclaw) Edit(models []string) error {
+	if len(models) == 0 {
+		return nil
+	}
+
+	home, err := os.UserHomeDir()
+	if err != nil {
+		return err
+	}
+
+	configPath := filepath.Join(home, ".openclaw", "openclaw.json")
+	legacyPath := filepath.Join(home, ".clawdbot", "clawdbot.json")
+	if err := os.MkdirAll(filepath.Dir(configPath), 0o755); err != nil {
+		return err
+	}
+
+	// Read into map[string]any to preserve unknown fields
+	config := make(map[string]any)
+	if data, err := os.ReadFile(configPath); err == nil {
+		_ = json.Unmarshal(data, &config)
+	} else if data, err := os.ReadFile(legacyPath); err == nil {
+		_ = json.Unmarshal(data, &config)
+	}
+
+	// Navigate/create: models.providers.ollama (preserving other providers)
+	modelsSection, _ := config["models"].(map[string]any)
+	if modelsSection == nil {
+		modelsSection = make(map[string]any)
+	}
+	providers, _ := modelsSection["providers"].(map[string]any)
+	if providers == nil {
+		providers = make(map[string]any)
+	}
+	ollama, _ := providers["ollama"].(map[string]any)
+	if ollama == nil {
+		ollama = make(map[string]any)
+	}
+
+	ollama["baseUrl"] = envconfig.Host().String() + "/v1"
+	// needed to register provider
+	ollama["apiKey"] = "ollama-local"
+	// TODO(parthsareen): potentially move to responses
+	ollama["api"] = "openai-completions"
+
+	// Build map of existing models to preserve user customizations
+	existingModels, _ := ollama["models"].([]any)
+	existingByID := make(map[string]map[string]any)
+	for _, m := range existingModels {
+		if entry, ok := m.(map[string]any); ok {
+			if id, ok := entry["id"].(string); ok {
+				existingByID[id] = entry
+			}
+		}
+	}
+
+	var newModels []any
+	for _, model := range models {
+		entry := map[string]any{
+			"id":        model,
+			"name":      model,
+			"reasoning": false,
+			"input":     []any{"text"},
+			"cost": map[string]any{
+				"input":      0,
+				"output":     0,
+				"cacheRead":  0,
+				"cacheWrite": 0,
+			},
+			// TODO(parthsareen): get these values from API
+			"contextWindow": 131072,
+			"maxTokens":     16384,
+		}
+		// Merge existing fields (user customizations)
+		if existing, ok := existingByID[model]; ok {
+			for k, v := range existing {
+				if _, isNew := entry[k]; !isNew {
+					entry[k] = v
+				}
+			}
+		}
+		newModels = append(newModels, entry)
+	}
+	ollama["models"] = newModels
+
+	providers["ollama"] = ollama
+	modelsSection["providers"] = providers
+	config["models"] = modelsSection
+
+	// Update agents.defaults.model.primary (preserving other agent settings)
+	agents, _ := config["agents"].(map[string]any)
+	if agents == nil {
+		agents = make(map[string]any)
+	}
+	defaults, _ := agents["defaults"].(map[string]any)
+	if defaults == nil {
+		defaults = make(map[string]any)
+	}
+	modelConfig, _ := defaults["model"].(map[string]any)
+	if modelConfig == nil {
+		modelConfig = make(map[string]any)
+	}
+	modelConfig["primary"] = "ollama/" + models[0]
+	defaults["model"] = modelConfig
+	agents["defaults"] = defaults
+	config["agents"] = agents
+
+	data, err := json.MarshalIndent(config, "", "  ")
+	if err != nil {
+		return err
+	}
+	return writeWithBackup(configPath, data)
+}
+
+func (c *Openclaw) Models() []string {
+	home, err := os.UserHomeDir()
+	if err != nil {
+		return nil
+	}
+
+	config, err := readJSONFile(filepath.Join(home, ".openclaw", "openclaw.json"))
+	if err != nil {
+		config, err = readJSONFile(filepath.Join(home, ".clawdbot", "clawdbot.json"))
+		if err != nil {
+			return nil
+		}
+	}
+
+	modelsSection, _ := config["models"].(map[string]any)
+	providers, _ := modelsSection["providers"].(map[string]any)
+	ollama, _ := providers["ollama"].(map[string]any)
+	modelList, _ := ollama["models"].([]any)
+
+	var result []string
+	for _, m := range modelList {
+		if entry, ok := m.(map[string]any); ok {
+			if id, ok := entry["id"].(string); ok {
+				result = append(result, id)
+			}
+		}
+	}
+	return result
+}

cmd/config/openclaw_test.go

@@ -0,0 +1,878 @@
+package config
+
+import (
+	"encoding/json"
+	"fmt"
+	"os"
+	"path/filepath"
+	"testing"
+)
+
+func TestOpenclawIntegration(t *testing.T) {
+	c := &Openclaw{}
+
+	t.Run("String", func(t *testing.T) {
+		if got := c.String(); got != "OpenClaw" {
+			t.Errorf("String() = %q, want %q", got, "OpenClaw")
+		}
+	})
+
+	t.Run("implements Runner", func(t *testing.T) {
+		var _ Runner = c
+	})
+
+	t.Run("implements Editor", func(t *testing.T) {
+		var _ Editor = c
+	})
+}
+
+func TestOpenclawEdit(t *testing.T) {
+	c := &Openclaw{}
+	tmpDir := t.TempDir()
+	setTestHome(t, tmpDir)
+
+	configDir := filepath.Join(tmpDir, ".openclaw")
+	configPath := filepath.Join(configDir, "openclaw.json")
+
+	cleanup := func() { os.RemoveAll(configDir) }
+
+	t.Run("fresh install", func(t *testing.T) {
+		cleanup()
+		if err := c.Edit([]string{"llama3.2"}); err != nil {
+			t.Fatal(err)
+		}
+		assertOpenclawModelExists(t, configPath, "llama3.2")
+		assertOpenclawPrimaryModel(t, configPath, "ollama/llama3.2")
+	})
+
+	t.Run("multiple models - first is primary", func(t *testing.T) {
+		cleanup()
+		if err := c.Edit([]string{"llama3.2", "mistral"}); err != nil {
+			t.Fatal(err)
+		}
+		assertOpenclawModelExists(t, configPath, "llama3.2")
+		assertOpenclawModelExists(t, configPath, "mistral")
+		assertOpenclawPrimaryModel(t, configPath, "ollama/llama3.2")
+	})
+
+	t.Run("preserve other providers", func(t *testing.T) {
+		cleanup()
+		os.MkdirAll(configDir, 0o755)
+		os.WriteFile(configPath, []byte(`{"models":{"providers":{"anthropic":{"apiKey":"xxx"}}}}`), 0o644)
+		if err := c.Edit([]string{"llama3.2"}); err != nil {
+			t.Fatal(err)
+		}
+		data, _ := os.ReadFile(configPath)
+		var cfg map[string]any
+		json.Unmarshal(data, &cfg)
+		models := cfg["models"].(map[string]any)
+		providers := models["providers"].(map[string]any)
+		if providers["anthropic"] == nil {
+			t.Error("anthropic provider was removed")
+		}
+	})
+
+	t.Run("preserve top-level keys", func(t *testing.T) {
+		cleanup()
+		os.MkdirAll(configDir, 0o755)
+		os.WriteFile(configPath, []byte(`{"theme":"dark","mcp":{"servers":{}}}`), 0o644)
+		if err := c.Edit([]string{"llama3.2"}); err != nil {
+			t.Fatal(err)
+		}
+		data, _ := os.ReadFile(configPath)
+		var cfg map[string]any
+		json.Unmarshal(data, &cfg)
+		if cfg["theme"] != "dark" {
+			t.Error("theme was removed")
+		}
+		if cfg["mcp"] == nil {
+			t.Error("mcp was removed")
+		}
+	})
+
+	t.Run("preserve user customizations on models", func(t *testing.T) {
+		cleanup()
+		c.Edit([]string{"llama3.2"})
+
+		// User adds custom field
+		data, _ := os.ReadFile(configPath)
+		var cfg map[string]any
+		json.Unmarshal(data, &cfg)
+		models := cfg["models"].(map[string]any)
+		providers := models["providers"].(map[string]any)
+		ollama := providers["ollama"].(map[string]any)
+		modelList := ollama["models"].([]any)
+		entry := modelList[0].(map[string]any)
+		entry["customField"] = "user-value"
+		configData, _ := json.MarshalIndent(cfg, "", "  ")
+		os.WriteFile(configPath, configData, 0o644)
+
+		// Re-run Edit
+		c.Edit([]string{"llama3.2"})
+
+		data, _ = os.ReadFile(configPath)
+		json.Unmarshal(data, &cfg)
+		models = cfg["models"].(map[string]any)
+		providers = models["providers"].(map[string]any)
+		ollama = providers["ollama"].(map[string]any)
+		modelList = ollama["models"].([]any)
+		entry = modelList[0].(map[string]any)
+		if entry["customField"] != "user-value" {
+			t.Error("custom field was lost")
+		}
+	})
+
+	t.Run("edit replaces models list", func(t *testing.T) {
+		cleanup()
+		c.Edit([]string{"llama3.2", "mistral"})
+		c.Edit([]string{"llama3.2"})
+
+		assertOpenclawModelExists(t, configPath, "llama3.2")
+		assertOpenclawModelNotExists(t, configPath, "mistral")
+	})
+
+	t.Run("empty models is no-op", func(t *testing.T) {
+		cleanup()
+		os.MkdirAll(configDir, 0o755)
+		original := `{"existing":"data"}`
+		os.WriteFile(configPath, []byte(original), 0o644)
+
+		c.Edit([]string{})
+
+		data, _ := os.ReadFile(configPath)
+		if string(data) != original {
+			t.Error("empty models should not modify file")
+		}
+	})
+
+	t.Run("corrupted JSON treated as empty", func(t *testing.T) {
+		cleanup()
+		os.MkdirAll(configDir, 0o755)
+		os.WriteFile(configPath, []byte(`{corrupted`), 0o644)
+
+		if err := c.Edit([]string{"llama3.2"}); err != nil {
+			t.Fatal(err)
+		}
+
+		data, _ := os.ReadFile(configPath)
+		var cfg map[string]any
+		if err := json.Unmarshal(data, &cfg); err != nil {
+			t.Error("result should be valid JSON")
+		}
+	})
+
+	t.Run("wrong type models section", func(t *testing.T) {
+		cleanup()
+		os.MkdirAll(configDir, 0o755)
+		os.WriteFile(configPath, []byte(`{"models":"not a map"}`), 0o644)
+
+		if err := c.Edit([]string{"llama3.2"}); err != nil {
+			t.Fatal(err)
+		}
+		assertOpenclawModelExists(t, configPath, "llama3.2")
+	})
+}
+
+func TestOpenclawModels(t *testing.T) {
+	c := &Openclaw{}
+	tmpDir := t.TempDir()
+	setTestHome(t, tmpDir)
+
+	t.Run("no config returns nil", func(t *testing.T) {
+		if models := c.Models(); len(models) > 0 {
+			t.Errorf("expected nil/empty, got %v", models)
+		}
+	})
+
+	t.Run("returns all ollama models", func(t *testing.T) {
+		configDir := filepath.Join(tmpDir, ".openclaw")
+		os.MkdirAll(configDir, 0o755)
+		os.WriteFile(filepath.Join(configDir, "openclaw.json"), []byte(`{
+			"models":{"providers":{"ollama":{"models":[
+				{"id":"llama3.2"},
+				{"id":"mistral"}
+			]}}}
+		}`), 0o644)
+
+		models := c.Models()
+		if len(models) != 2 {
+			t.Errorf("expected 2 models, got %v", models)
+		}
+	})
+}
+
+// Helper functions
+func assertOpenclawModelExists(t *testing.T, path, model string) {
+	t.Helper()
+	data, _ := os.ReadFile(path)
+	var cfg map[string]any
+	json.Unmarshal(data, &cfg)
+	models := cfg["models"].(map[string]any)
+	providers := models["providers"].(map[string]any)
+	ollama := providers["ollama"].(map[string]any)
+	modelList := ollama["models"].([]any)
+	for _, m := range modelList {
+		if entry, ok := m.(map[string]any); ok {
+			if entry["id"] == model {
+				return
+			}
+		}
+	}
+	t.Errorf("model %s not found", model)
+}
+
+func assertOpenclawModelNotExists(t *testing.T, path, model string) {
+	t.Helper()
+	data, _ := os.ReadFile(path)
+	var cfg map[string]any
+	json.Unmarshal(data, &cfg)
+	models, _ := cfg["models"].(map[string]any)
+	providers, _ := models["providers"].(map[string]any)
+	ollama, _ := providers["ollama"].(map[string]any)
+	modelList, _ := ollama["models"].([]any)
+	for _, m := range modelList {
+		if entry, ok := m.(map[string]any); ok {
+			if entry["id"] == model {
+				t.Errorf("model %s should not exist", model)
+			}
+		}
+	}
+}
+
+func assertOpenclawPrimaryModel(t *testing.T, path, expected string) {
+	t.Helper()
+	data, _ := os.ReadFile(path)
+	var cfg map[string]any
+	json.Unmarshal(data, &cfg)
+	agents := cfg["agents"].(map[string]any)
+	defaults := agents["defaults"].(map[string]any)
+	model := defaults["model"].(map[string]any)
+	if model["primary"] != expected {
+		t.Errorf("primary model = %v, want %v", model["primary"], expected)
+	}
+}
+
+func TestOpenclawPaths(t *testing.T) {
+	c := &Openclaw{}
+
+	t.Run("returns path when config exists", func(t *testing.T) {
+		tmpDir := t.TempDir()
+		setTestHome(t, tmpDir)
+		configDir := filepath.Join(tmpDir, ".openclaw")
+		os.MkdirAll(configDir, 0o755)
+		os.WriteFile(filepath.Join(configDir, "openclaw.json"), []byte(`{}`), 0o644)
+
+		paths := c.Paths()
+		if len(paths) != 1 {
+			t.Errorf("expected 1 path, got %d", len(paths))
+		}
+	})
+
+	t.Run("returns nil when config missing", func(t *testing.T) {
+		tmpDir := t.TempDir()
+		setTestHome(t, tmpDir)
+		if paths := c.Paths(); paths != nil {
+			t.Errorf("expected nil, got %v", paths)
+		}
+	})
+}
+
+func TestOpenclawModelsEdgeCases(t *testing.T) {
+	c := &Openclaw{}
+	tmpDir := t.TempDir()
+	setTestHome(t, tmpDir)
+	configDir := filepath.Join(tmpDir, ".openclaw")
+	configPath := filepath.Join(configDir, "openclaw.json")
+	cleanup := func() { os.RemoveAll(configDir) }
+
+	t.Run("corrupted JSON returns nil", func(t *testing.T) {
+		cleanup()
+		os.MkdirAll(configDir, 0o755)
+		os.WriteFile(configPath, []byte(`{corrupted`), 0o644)
+		if models := c.Models(); models != nil {
+			t.Errorf("expected nil, got %v", models)
+		}
+	})
+
+	t.Run("wrong type at models level", func(t *testing.T) {
+		cleanup()
+		os.MkdirAll(configDir, 0o755)
+		os.WriteFile(configPath, []byte(`{"models":"string"}`), 0o644)
+		if models := c.Models(); models != nil {
+			t.Errorf("expected nil, got %v", models)
+		}
+	})
+
+	t.Run("wrong type at providers level", func(t *testing.T) {
+		cleanup()
+		os.MkdirAll(configDir, 0o755)
+		os.WriteFile(configPath, []byte(`{"models":{"providers":"string"}}`), 0o644)
+		if models := c.Models(); models != nil {
+			t.Errorf("expected nil, got %v", models)
+		}
+	})
+
+	t.Run("wrong type at ollama level", func(t *testing.T) {
+		cleanup()
+		os.MkdirAll(configDir, 0o755)
+		os.WriteFile(configPath, []byte(`{"models":{"providers":{"ollama":"string"}}}`), 0o644)
+		if models := c.Models(); models != nil {
+			t.Errorf("expected nil, got %v", models)
+		}
+	})
+
+	t.Run("model entry missing id", func(t *testing.T) {
+		cleanup()
+		os.MkdirAll(configDir, 0o755)
+		os.WriteFile(configPath, []byte(`{"models":{"providers":{"ollama":{"models":[{"name":"test"}]}}}}`), 0o644)
+		if len(c.Models()) != 0 {
+			t.Error("expected empty for missing id")
+		}
+	})
+
+	t.Run("model id is not string", func(t *testing.T) {
+		cleanup()
+		os.MkdirAll(configDir, 0o755)
+		os.WriteFile(configPath, []byte(`{"models":{"providers":{"ollama":{"models":[{"id":123}]}}}}`), 0o644)
+		if len(c.Models()) != 0 {
+			t.Error("expected empty for non-string id")
+		}
+	})
+}
+
+func TestOpenclawEditSchemaFields(t *testing.T) {
+	c := &Openclaw{}
+	tmpDir := t.TempDir()
+	setTestHome(t, tmpDir)
+	configPath := filepath.Join(tmpDir, ".openclaw", "openclaw.json")
+
+	if err := c.Edit([]string{"llama3.2"}); err != nil {
+		t.Fatal(err)
+	}
+
+	data, _ := os.ReadFile(configPath)
+	var cfg map[string]any
+	json.Unmarshal(data, &cfg)
+	models := cfg["models"].(map[string]any)
+	providers := models["providers"].(map[string]any)
+	ollama := providers["ollama"].(map[string]any)
+	modelList := ollama["models"].([]any)
+	entry := modelList[0].(map[string]any)
+
+	// Verify required schema fields
+	if entry["reasoning"] != false {
+		t.Error("reasoning should be false")
+	}
+	if entry["input"] == nil {
+		t.Error("input should be set")
+	}
+	if entry["contextWindow"] == nil {
+		t.Error("contextWindow should be set")
+	}
+	if entry["maxTokens"] == nil {
+		t.Error("maxTokens should be set")
+	}
+	cost := entry["cost"].(map[string]any)
+	if cost["cacheRead"] == nil {
+		t.Error("cost.cacheRead should be set")
+	}
+	if cost["cacheWrite"] == nil {
+		t.Error("cost.cacheWrite should be set")
+	}
+}
+
+func TestOpenclawEditModelNames(t *testing.T) {
+	c := &Openclaw{}
+	tmpDir := t.TempDir()
+	setTestHome(t, tmpDir)
+	configPath := filepath.Join(tmpDir, ".openclaw", "openclaw.json")
+	cleanup := func() { os.RemoveAll(filepath.Join(tmpDir, ".openclaw")) }
+
+	t.Run("model with colon tag", func(t *testing.T) {
+		cleanup()
+		if err := c.Edit([]string{"llama3.2:70b"}); err != nil {
+			t.Fatal(err)
+		}
+		assertOpenclawModelExists(t, configPath, "llama3.2:70b")
+		assertOpenclawPrimaryModel(t, configPath, "ollama/llama3.2:70b")
+	})
+
+	t.Run("model with slash", func(t *testing.T) {
+		cleanup()
+		if err := c.Edit([]string{"library/model:tag"}); err != nil {
+			t.Fatal(err)
+		}
+		assertOpenclawModelExists(t, configPath, "library/model:tag")
+		assertOpenclawPrimaryModel(t, configPath, "ollama/library/model:tag")
+	})
+
+	t.Run("model with hyphen", func(t *testing.T) {
+		cleanup()
+		if err := c.Edit([]string{"test-model"}); err != nil {
+			t.Fatal(err)
+		}
+		assertOpenclawModelExists(t, configPath, "test-model")
+	})
+}
+
+func TestOpenclawEditAgentsPreservation(t *testing.T) {
+	c := &Openclaw{}
+	tmpDir := t.TempDir()
+	setTestHome(t, tmpDir)
+	configDir := filepath.Join(tmpDir, ".openclaw")
+	configPath := filepath.Join(configDir, "openclaw.json")
+	cleanup := func() { os.RemoveAll(configDir) }
+
+	t.Run("preserve other agent defaults", func(t *testing.T) {
+		cleanup()
+		os.MkdirAll(configDir, 0o755)
+		os.WriteFile(configPath, []byte(`{"agents":{"defaults":{"model":{"primary":"old"},"temperature":0.7}}}`), 0o644)
+
+		c.Edit([]string{"llama3.2"})
+
+		data, _ := os.ReadFile(configPath)
+		var cfg map[string]any
+		json.Unmarshal(data, &cfg)
+		agents := cfg["agents"].(map[string]any)
+		defaults := agents["defaults"].(map[string]any)
+		if defaults["temperature"] != 0.7 {
+			t.Error("temperature setting was lost")
+		}
+	})
+
+	t.Run("preserve other agents besides defaults", func(t *testing.T) {
+		cleanup()
+		os.MkdirAll(configDir, 0o755)
+		os.WriteFile(configPath, []byte(`{"agents":{"defaults":{},"custom-agent":{"foo":"bar"}}}`), 0o644)
+
+		c.Edit([]string{"llama3.2"})
+
+		data, _ := os.ReadFile(configPath)
+		var cfg map[string]any
+		json.Unmarshal(data, &cfg)
+		agents := cfg["agents"].(map[string]any)
+		if agents["custom-agent"] == nil {
+			t.Error("custom-agent was lost")
+		}
+	})
+}
+
+const testOpenclawFixture = `{
+  "theme": "dark",
+  "mcp": {"servers": {"custom": {"enabled": true}}},
+  "models": {
+    "providers": {
+      "anthropic": {"apiKey": "xxx"},
+      "ollama": {
+        "baseUrl": "http://127.0.0.1:11434/v1",
+        "models": [{"id": "old-model", "customField": "preserved"}]
+      }
+    }
+  },
+  "agents": {
+    "defaults": {"model": {"primary": "old"}, "temperature": 0.7},
+    "custom-agent": {"foo": "bar"}
+  }
+}`
+
+func TestOpenclawEdit_RoundTrip(t *testing.T) {
+	c := &Openclaw{}
+	tmpDir := t.TempDir()
+	setTestHome(t, tmpDir)
+	configDir := filepath.Join(tmpDir, ".openclaw")
+	configPath := filepath.Join(configDir, "openclaw.json")
+
+	os.MkdirAll(configDir, 0o755)
+	os.WriteFile(configPath, []byte(testOpenclawFixture), 0o644)
+
+	if err := c.Edit([]string{"llama3.2", "mistral"}); err != nil {
+		t.Fatal(err)
+	}
+
+	data, _ := os.ReadFile(configPath)
+	var cfg map[string]any
+	json.Unmarshal(data, &cfg)
+
+	// Verify top-level preserved
+	if cfg["theme"] != "dark" {
+		t.Error("theme not preserved")
+	}
+	mcp := cfg["mcp"].(map[string]any)
+	servers := mcp["servers"].(map[string]any)
+	if servers["custom"] == nil {
+		t.Error("mcp.servers.custom not preserved")
+	}
+
+	// Verify other providers preserved
+	models := cfg["models"].(map[string]any)
+	providers := models["providers"].(map[string]any)
+	if providers["anthropic"] == nil {
+		t.Error("anthropic provider not preserved")
+	}
+
+	// Verify agents preserved
+	agents := cfg["agents"].(map[string]any)
+	if agents["custom-agent"] == nil {
+		t.Error("custom-agent not preserved")
+	}
+	defaults := agents["defaults"].(map[string]any)
+	if defaults["temperature"] != 0.7 {
+		t.Error("temperature not preserved")
+	}
+}
+
+func TestOpenclawEdit_Idempotent(t *testing.T) {
+	c := &Openclaw{}
+	tmpDir := t.TempDir()
+	setTestHome(t, tmpDir)
+	configDir := filepath.Join(tmpDir, ".openclaw")
+	configPath := filepath.Join(configDir, "openclaw.json")
+
+	os.MkdirAll(configDir, 0o755)
+	os.WriteFile(configPath, []byte(testOpenclawFixture), 0o644)
+
+	c.Edit([]string{"llama3.2", "mistral"})
+	firstData, _ := os.ReadFile(configPath)
+
+	c.Edit([]string{"llama3.2", "mistral"})
+	secondData, _ := os.ReadFile(configPath)
+
+	if string(firstData) != string(secondData) {
+		t.Error("repeated edits with same models produced different results")
+	}
+}
+
+func TestOpenclawEdit_MultipleConsecutiveEdits(t *testing.T) {
+	c := &Openclaw{}
+	tmpDir := t.TempDir()
+	setTestHome(t, tmpDir)
+	configDir := filepath.Join(tmpDir, ".openclaw")
+	configPath := filepath.Join(configDir, "openclaw.json")
+
+	os.MkdirAll(configDir, 0o755)
+	os.WriteFile(configPath, []byte(testOpenclawFixture), 0o644)
+
+	for i := range 10 {
+		models := []string{"model-a", "model-b"}
+		if i%2 == 0 {
+			models = []string{"model-x", "model-y", "model-z"}
+		}
+		if err := c.Edit(models); err != nil {
+			t.Fatalf("edit %d failed: %v", i, err)
+		}
+	}
+
+	data, _ := os.ReadFile(configPath)
+	var cfg map[string]any
+	if err := json.Unmarshal(data, &cfg); err != nil {
+		t.Fatalf("file is not valid JSON after multiple edits: %v", err)
+	}
+
+	if cfg["theme"] != "dark" {
+		t.Error("theme lost after multiple edits")
+	}
+}
+
+func TestOpenclawEdit_BackupCreated(t *testing.T) {
+	c := &Openclaw{}
+	tmpDir := t.TempDir()
+	setTestHome(t, tmpDir)
+	configDir := filepath.Join(tmpDir, ".openclaw")
+	configPath := filepath.Join(configDir, "openclaw.json")
+	backupDir := filepath.Join(os.TempDir(), "ollama-backups")
+
+	os.MkdirAll(configDir, 0o755)
+	uniqueMarker := fmt.Sprintf("test-marker-%d", os.Getpid())
+	original := fmt.Sprintf(`{"theme": "%s"}`, uniqueMarker)
+	os.WriteFile(configPath, []byte(original), 0o644)
+
+	if err := c.Edit([]string{"model-a"}); err != nil {
+		t.Fatal(err)
+	}
+
+	backups, _ := filepath.Glob(filepath.Join(backupDir, "openclaw.json.*"))
+	foundBackup := false
+	for _, backup := range backups {
+		data, _ := os.ReadFile(backup)
+		if string(data) == original {
+			foundBackup = true
+			break
+		}
+	}
+
+	if !foundBackup {
+		t.Error("backup with original content not found")
+	}
+}
+
+func TestOpenclawClawdbotAlias(t *testing.T) {
+	for _, alias := range []string{"clawdbot", "moltbot"} {
+		t.Run(alias+" alias resolves to Openclaw runner", func(t *testing.T) {
+			r, ok := integrations[alias]
+			if !ok {
+				t.Fatalf("%s not found in integrations", alias)
+			}
+			if _, ok := r.(*Openclaw); !ok {
+				t.Errorf("%s integration is %T, want *Openclaw", alias, r)
+			}
+		})
+
+		t.Run(alias+" is hidden from selector", func(t *testing.T) {
+			if !integrationAliases[alias] {
+				t.Errorf("%s should be in integrationAliases", alias)
+			}
+		})
+	}
+}
+
+func TestOpenclawLegacyPaths(t *testing.T) {
+	c := &Openclaw{}
+
+	t.Run("falls back to legacy clawdbot path", func(t *testing.T) {
+		tmpDir := t.TempDir()
+		setTestHome(t, tmpDir)
+		legacyDir := filepath.Join(tmpDir, ".clawdbot")
+		os.MkdirAll(legacyDir, 0o755)
+		os.WriteFile(filepath.Join(legacyDir, "clawdbot.json"), []byte(`{}`), 0o644)
+
+		paths := c.Paths()
+		if len(paths) != 1 {
+			t.Fatalf("expected 1 path, got %d", len(paths))
+		}
+		if paths[0] != filepath.Join(legacyDir, "clawdbot.json") {
+			t.Errorf("expected legacy path, got %s", paths[0])
+		}
+	})
+
+	t.Run("prefers new path over legacy", func(t *testing.T) {
+		tmpDir := t.TempDir()
+		setTestHome(t, tmpDir)
+		newDir := filepath.Join(tmpDir, ".openclaw")
+		legacyDir := filepath.Join(tmpDir, ".clawdbot")
+		os.MkdirAll(newDir, 0o755)
+		os.MkdirAll(legacyDir, 0o755)
+		os.WriteFile(filepath.Join(newDir, "openclaw.json"), []byte(`{}`), 0o644)
+		os.WriteFile(filepath.Join(legacyDir, "clawdbot.json"), []byte(`{}`), 0o644)
+
+		paths := c.Paths()
+		if len(paths) != 1 {
+			t.Fatalf("expected 1 path, got %d", len(paths))
+		}
+		if paths[0] != filepath.Join(newDir, "openclaw.json") {
+			t.Errorf("expected new path, got %s", paths[0])
+		}
+	})
+
+	t.Run("Models reads from legacy path", func(t *testing.T) {
+		tmpDir := t.TempDir()
+		setTestHome(t, tmpDir)
+		legacyDir := filepath.Join(tmpDir, ".clawdbot")
+		os.MkdirAll(legacyDir, 0o755)
+		os.WriteFile(filepath.Join(legacyDir, "clawdbot.json"), []byte(`{
+			"models":{"providers":{"ollama":{"models":[{"id":"llama3.2"}]}}}
+		}`), 0o644)
+
+		models := c.Models()
+		if len(models) != 1 || models[0] != "llama3.2" {
+			t.Errorf("expected [llama3.2], got %v", models)
+		}
+	})
+
+	t.Run("Models prefers new path over legacy", func(t *testing.T) {
+		tmpDir := t.TempDir()
+		setTestHome(t, tmpDir)
+		newDir := filepath.Join(tmpDir, ".openclaw")
+		legacyDir := filepath.Join(tmpDir, ".clawdbot")
+		os.MkdirAll(newDir, 0o755)
+		os.MkdirAll(legacyDir, 0o755)
+		os.WriteFile(filepath.Join(newDir, "openclaw.json"), []byte(`{
+			"models":{"providers":{"ollama":{"models":[{"id":"new-model"}]}}}
+		}`), 0o644)
+		os.WriteFile(filepath.Join(legacyDir, "clawdbot.json"), []byte(`{
+			"models":{"providers":{"ollama":{"models":[{"id":"legacy-model"}]}}}
+		}`), 0o644)
+
+		models := c.Models()
+		if len(models) != 1 || models[0] != "new-model" {
+			t.Errorf("expected [new-model], got %v", models)
+		}
+	})
+
+	t.Run("Edit reads new path over legacy when both exist", func(t *testing.T) {
+		tmpDir := t.TempDir()
+		setTestHome(t, tmpDir)
+		newDir := filepath.Join(tmpDir, ".openclaw")
+		legacyDir := filepath.Join(tmpDir, ".clawdbot")
+		os.MkdirAll(newDir, 0o755)
+		os.MkdirAll(legacyDir, 0o755)
+		os.WriteFile(filepath.Join(newDir, "openclaw.json"), []byte(`{"theme":"new"}`), 0o644)
+		os.WriteFile(filepath.Join(legacyDir, "clawdbot.json"), []byte(`{"theme":"legacy"}`), 0o644)
+
+		if err := c.Edit([]string{"llama3.2"}); err != nil {
+			t.Fatal(err)
+		}
+
+		data, _ := os.ReadFile(filepath.Join(newDir, "openclaw.json"))
+		var cfg map[string]any
+		json.Unmarshal(data, &cfg)
+		if cfg["theme"] != "new" {
+			t.Errorf("expected theme from new config, got %v", cfg["theme"])
+		}
+	})
+
+	t.Run("Edit migrates from legacy config", func(t *testing.T) {
+		tmpDir := t.TempDir()
+		setTestHome(t, tmpDir)
+		legacyDir := filepath.Join(tmpDir, ".clawdbot")
+		os.MkdirAll(legacyDir, 0o755)
+		os.WriteFile(filepath.Join(legacyDir, "clawdbot.json"), []byte(`{"theme":"dark"}`), 0o644)
+
+		if err := c.Edit([]string{"llama3.2"}); err != nil {
+			t.Fatal(err)
+		}
+
+		// Should write to new path
+		newPath := filepath.Join(tmpDir, ".openclaw", "openclaw.json")
+		data, err := os.ReadFile(newPath)
+		if err != nil {
+			t.Fatal("expected new config file to be created")
+		}
+		var cfg map[string]any
+		json.Unmarshal(data, &cfg)
+		if cfg["theme"] != "dark" {
+			t.Error("legacy theme setting was not migrated")
+		}
+	})
+}
+
+func TestOpenclawEdit_CreatesDirectoryIfMissing(t *testing.T) {
+	c := &Openclaw{}
+	tmpDir := t.TempDir()
+	setTestHome(t, tmpDir)
+	configDir := filepath.Join(tmpDir, ".openclaw")
+
+	if _, err := os.Stat(configDir); !os.IsNotExist(err) {
+		t.Fatal("directory should not exist before test")
+	}
+
+	if err := c.Edit([]string{"model-a"}); err != nil {
+		t.Fatal(err)
+	}
+
+	if _, err := os.Stat(configDir); os.IsNotExist(err) {
+		t.Fatal("directory was not created")
+	}
+}
+
+func TestOpenclawOnboarded(t *testing.T) {
+	c := &Openclaw{}
+
+	t.Run("returns false when no config exists", func(t *testing.T) {
+		tmpDir := t.TempDir()
+		setTestHome(t, tmpDir)
+		if c.onboarded() {
+			t.Error("expected false when no config exists")
+		}
+	})
+
+	t.Run("returns false when config exists but no wizard section", func(t *testing.T) {
+		tmpDir := t.TempDir()
+		setTestHome(t, tmpDir)
+		configDir := filepath.Join(tmpDir, ".openclaw")
+		os.MkdirAll(configDir, 0o755)
+		os.WriteFile(filepath.Join(configDir, "openclaw.json"), []byte(`{"theme":"dark"}`), 0o644)
+
+		if c.onboarded() {
+			t.Error("expected false when no wizard section")
+		}
+	})
+
+	t.Run("returns false when wizard section exists but no lastRunAt", func(t *testing.T) {
+		tmpDir := t.TempDir()
+		setTestHome(t, tmpDir)
+		configDir := filepath.Join(tmpDir, ".openclaw")
+		os.MkdirAll(configDir, 0o755)
+		os.WriteFile(filepath.Join(configDir, "openclaw.json"), []byte(`{"wizard":{}}`), 0o644)
+
+		if c.onboarded() {
+			t.Error("expected false when wizard.lastRunAt is missing")
+		}
+	})
+
+	t.Run("returns false when wizard.lastRunAt is empty string", func(t *testing.T) {
+		tmpDir := t.TempDir()
+		setTestHome(t, tmpDir)
+		configDir := filepath.Join(tmpDir, ".openclaw")
+		os.MkdirAll(configDir, 0o755)
+		os.WriteFile(filepath.Join(configDir, "openclaw.json"), []byte(`{"wizard":{"lastRunAt":""}}`), 0o644)
+
+		if c.onboarded() {
+			t.Error("expected false when wizard.lastRunAt is empty")
+		}
+	})
+
+	t.Run("returns true when wizard.lastRunAt is set", func(t *testing.T) {
+		tmpDir := t.TempDir()
+		setTestHome(t, tmpDir)
+		configDir := filepath.Join(tmpDir, ".openclaw")
+		os.MkdirAll(configDir, 0o755)
+		os.WriteFile(filepath.Join(configDir, "openclaw.json"), []byte(`{"wizard":{"lastRunAt":"2024-01-01T00:00:00Z"}}`), 0o644)
+
+		if !c.onboarded() {
+			t.Error("expected true when wizard.lastRunAt is set")
+		}
+	})
+
+	t.Run("checks legacy clawdbot path", func(t *testing.T) {
+		tmpDir := t.TempDir()
+		setTestHome(t, tmpDir)
+		legacyDir := filepath.Join(tmpDir, ".clawdbot")
+		os.MkdirAll(legacyDir, 0o755)
+		os.WriteFile(filepath.Join(legacyDir, "clawdbot.json"), []byte(`{"wizard":{"lastRunAt":"2024-01-01T00:00:00Z"}}`), 0o644)
+
+		if !c.onboarded() {
+			t.Error("expected true when legacy config has wizard.lastRunAt")
+		}
+	})
+
+	t.Run("prefers new path over legacy", func(t *testing.T) {
+		tmpDir := t.TempDir()
+		setTestHome(t, tmpDir)
+		newDir := filepath.Join(tmpDir, ".openclaw")
+		legacyDir := filepath.Join(tmpDir, ".clawdbot")
+		os.MkdirAll(newDir, 0o755)
+		os.MkdirAll(legacyDir, 0o755)
+		// New path has no wizard marker
+		os.WriteFile(filepath.Join(newDir, "openclaw.json"), []byte(`{}`), 0o644)
+		// Legacy has wizard marker
+		os.WriteFile(filepath.Join(legacyDir, "clawdbot.json"), []byte(`{"wizard":{"lastRunAt":"2024-01-01T00:00:00Z"}}`), 0o644)
+
+		if c.onboarded() {
+			t.Error("expected false - should prefer new path which has no wizard marker")
+		}
+	})
+
+	t.Run("handles corrupted JSON gracefully", func(t *testing.T) {
+		tmpDir := t.TempDir()
+		setTestHome(t, tmpDir)
+		configDir := filepath.Join(tmpDir, ".openclaw")
+		os.MkdirAll(configDir, 0o755)
+		os.WriteFile(filepath.Join(configDir, "openclaw.json"), []byte(`{corrupted`), 0o644)
+
+		if c.onboarded() {
+			t.Error("expected false for corrupted JSON")
+		}
+	})
+
+	t.Run("handles wrong type for wizard section", func(t *testing.T) {
+		tmpDir := t.TempDir()
+		setTestHome(t, tmpDir)
+		configDir := filepath.Join(tmpDir, ".openclaw")
+		os.MkdirAll(configDir, 0o755)
+		os.WriteFile(filepath.Join(configDir, "openclaw.json"), []byte(`{"wizard":"not a map"}`), 0o644)
+
+		if c.onboarded() {
+			t.Error("expected false when wizard is wrong type")
+		}
+	})
+}

cmd/config/opencode.go

@@ -9,6 +9,8 @@ import (
 	"path/filepath"
 	"slices"
 	"strings"
+
+	"github.com/ollama/ollama/envconfig"
 )
 
 // OpenCode implements Runner and Editor for OpenCode integration
@@ -16,7 +18,7 @@ type OpenCode struct{}
 
 func (o *OpenCode) String() string { return "OpenCode" }
 
-func (o *OpenCode) Run(model string) error {
+func (o *OpenCode) Run(model string, args []string) error {
 	if _, err := exec.LookPath("opencode"); err != nil {
 		return fmt.Errorf("opencode is not installed, install from https://opencode.ai")
 	}
@@ -30,7 +32,7 @@ func (o *OpenCode) Run(model string) error {
 		return fmt.Errorf("setup failed: %w", err)
 	}
 
-	cmd := exec.Command("opencode")
+	cmd := exec.Command("opencode", args...)
 	cmd.Stdin = os.Stdin
 	cmd.Stdout = os.Stdout
 	cmd.Stderr = os.Stderr
@@ -88,7 +90,7 @@ func (o *OpenCode) Edit(modelList []string) error {
 			"npm":  "@ai-sdk/openai-compatible",
 			"name": "Ollama (local)",
 			"options": map[string]any{
-				"baseURL": "http://localhost:11434/v1",
+				"baseURL": envconfig.Host().String() + "/v1",
 			},
 		}
 	}

cmd/config/selector.go

@@ -275,7 +275,11 @@ func parseInput(r io.Reader) (inputEvent, byte, error) {
 func renderSelect(w io.Writer, prompt string, s *selectState) int {
 	filtered := s.filtered()
 
-	fmt.Fprintf(w, "%s %s\r\n", prompt, s.filter)
+	if s.filter == "" {
+		fmt.Fprintf(w, "%s %sType to filter...%s\r\n", prompt, ansiGray, ansiReset)
+	} else {
+		fmt.Fprintf(w, "%s %s\r\n", prompt, s.filter)
+	}
 	lineCount := 1
 
 	if len(filtered) == 0 {
@@ -314,7 +318,11 @@ func renderSelect(w io.Writer, prompt string, s *selectState) int {
 func renderMultiSelect(w io.Writer, prompt string, s *multiSelectState) int {
 	filtered := s.filtered()
 
-	fmt.Fprintf(w, "%s %s\r\n", prompt, s.filter)
+	if s.filter == "" {
+		fmt.Fprintf(w, "%s %sType to filter...%s\r\n", prompt, ansiGray, ansiReset)
+	} else {
+		fmt.Fprintf(w, "%s %s\r\n", prompt, s.filter)
+	}
 	lineCount := 1
 
 	if len(filtered) == 0 {
@@ -345,10 +353,15 @@ func renderMultiSelect(w io.Writer, prompt string, s *multiSelectState) int {
 				suffix = " " + ansiGray + "(default)" + ansiReset
 			}
 
+			desc := ""
+			if item.Description != "" {
+				desc = " " + ansiGray + "- " + item.Description + ansiReset
+			}
+
 			if idx == s.highlighted && !s.focusOnButton {
-				fmt.Fprintf(w, "  %s%s %s %s%s%s\r\n", ansiBold, prefix, checkbox, item.Name, ansiReset, suffix)
+				fmt.Fprintf(w, "  %s%s %s %s%s%s%s\r\n", ansiBold, prefix, checkbox, item.Name, ansiReset, desc, suffix)
 			} else {
-				fmt.Fprintf(w, "  %s %s %s%s\r\n", prefix, checkbox, item.Name, suffix)
+				fmt.Fprintf(w, "  %s %s %s%s%s\r\n", prefix, checkbox, item.Name, desc, suffix)
 			}
 			lineCount++
 		}

cmd/runner/main.go

@@ -0,0 +1,15 @@
+package main
+
+import (
+	"fmt"
+	"os"
+
+	"github.com/ollama/ollama/runner"
+)
+
+func main() {
+	if err := runner.Execute(os.Args[1:]); err != nil {
+		fmt.Fprintf(os.Stderr, "error: %s\n", err)
+		os.Exit(1)
+	}
+}

cmd/tui/selector.go

@@ -0,0 +1,471 @@
+package tui
+
+import (
+	"errors"
+	"fmt"
+	"strings"
+
+	tea "github.com/charmbracelet/bubbletea"
+	"github.com/charmbracelet/lipgloss"
+)
+
+var (
+	selectorTitleStyle = lipgloss.NewStyle().
+				Bold(true).
+				Foreground(lipgloss.Color("147"))
+
+	selectorItemStyle = lipgloss.NewStyle().
+				PaddingLeft(4)
+
+	selectorSelectedItemStyle = lipgloss.NewStyle().
+					PaddingLeft(2).
+					Foreground(lipgloss.Color("147")).
+					Bold(true)
+
+	selectorDescStyle = lipgloss.NewStyle().
+				Foreground(lipgloss.Color("241"))
+
+	selectorFilterStyle = lipgloss.NewStyle().
+				Foreground(lipgloss.Color("241")).
+				Italic(true)
+
+	selectorInputStyle = lipgloss.NewStyle().
+				Foreground(lipgloss.Color("252"))
+
+	selectorCheckboxStyle = lipgloss.NewStyle().
+				Foreground(lipgloss.Color("241"))
+
+	selectorCheckboxCheckedStyle = lipgloss.NewStyle().
+					Foreground(lipgloss.Color("147"))
+
+	selectorDefaultTagStyle = lipgloss.NewStyle().
+				Foreground(lipgloss.Color("241")).
+				Italic(true)
+
+	selectorHelpStyle = lipgloss.NewStyle().
+				Foreground(lipgloss.Color("241"))
+
+	selectorMoreStyle = lipgloss.NewStyle().
+				PaddingLeft(4).
+				Foreground(lipgloss.Color("241")).
+				Italic(true)
+)
+
+const maxSelectorItems = 10
+
+// ErrCancelled is returned when the user cancels the selection.
+var ErrCancelled = errors.New("cancelled")
+
+// SelectItem represents an item that can be selected.
+type SelectItem struct {
+	Name        string
+	Description string
+}
+
+// selectorModel is the bubbletea model for single selection.
+type selectorModel struct {
+	title        string
+	items        []SelectItem
+	filter       string
+	cursor       int
+	scrollOffset int
+	selected     string
+	cancelled    bool
+}
+
+func (m selectorModel) filteredItems() []SelectItem {
+	if m.filter == "" {
+		return m.items
+	}
+	filterLower := strings.ToLower(m.filter)
+	var result []SelectItem
+	for _, item := range m.items {
+		if strings.Contains(strings.ToLower(item.Name), filterLower) {
+			result = append(result, item)
+		}
+	}
+	return result
+}
+
+func (m selectorModel) Init() tea.Cmd {
+	return nil
+}
+
+func (m selectorModel) Update(msg tea.Msg) (tea.Model, tea.Cmd) {
+	switch msg := msg.(type) {
+	case tea.KeyMsg:
+		filtered := m.filteredItems()
+
+		switch msg.Type {
+		case tea.KeyCtrlC, tea.KeyEsc:
+			m.cancelled = true
+			return m, tea.Quit
+
+		case tea.KeyEnter:
+			if len(filtered) > 0 && m.cursor < len(filtered) {
+				m.selected = filtered[m.cursor].Name
+			}
+			return m, tea.Quit
+
+		case tea.KeyUp:
+			if m.cursor > 0 {
+				m.cursor--
+				if m.cursor < m.scrollOffset {
+					m.scrollOffset = m.cursor
+				}
+			}
+
+		case tea.KeyDown:
+			if m.cursor < len(filtered)-1 {
+				m.cursor++
+				if m.cursor >= m.scrollOffset+maxSelectorItems {
+					m.scrollOffset = m.cursor - maxSelectorItems + 1
+				}
+			}
+
+		case tea.KeyBackspace:
+			if len(m.filter) > 0 {
+				m.filter = m.filter[:len(m.filter)-1]
+				m.cursor = 0
+				m.scrollOffset = 0
+			}
+
+		case tea.KeyRunes:
+			m.filter += string(msg.Runes)
+			m.cursor = 0
+			m.scrollOffset = 0
+		}
+	}
+
+	return m, nil
+}
+
+func (m selectorModel) View() string {
+	// Clear screen when exiting
+	if m.cancelled || m.selected != "" {
+		return ""
+	}
+
+	var s strings.Builder
+
+	// Title with filter
+	s.WriteString(selectorTitleStyle.Render(m.title))
+	s.WriteString(" ")
+	if m.filter == "" {
+		s.WriteString(selectorFilterStyle.Render("Type to filter..."))
+	} else {
+		s.WriteString(selectorInputStyle.Render(m.filter))
+	}
+	s.WriteString("\n\n")
+
+	filtered := m.filteredItems()
+
+	if len(filtered) == 0 {
+		s.WriteString(selectorItemStyle.Render(selectorDescStyle.Render("(no matches)")))
+		s.WriteString("\n")
+	} else {
+		displayCount := min(len(filtered), maxSelectorItems)
+
+		for i := range displayCount {
+			idx := m.scrollOffset + i
+			if idx >= len(filtered) {
+				break
+			}
+			item := filtered[idx]
+
+			if idx == m.cursor {
+				s.WriteString(selectorSelectedItemStyle.Render("▸ " + item.Name))
+			} else {
+				s.WriteString(selectorItemStyle.Render(item.Name))
+			}
+
+			if item.Description != "" {
+				s.WriteString(" ")
+				s.WriteString(selectorDescStyle.Render("- " + item.Description))
+			}
+			s.WriteString("\n")
+		}
+
+		if remaining := len(filtered) - m.scrollOffset - displayCount; remaining > 0 {
+			s.WriteString(selectorMoreStyle.Render(fmt.Sprintf("... and %d more", remaining)))
+			s.WriteString("\n")
+		}
+	}
+
+	s.WriteString("\n")
+	s.WriteString(selectorHelpStyle.Render("↑/↓ navigate • enter select • esc cancel"))
+
+	return s.String()
+}
+
+// SelectSingle prompts the user to select a single item from a list.
+func SelectSingle(title string, items []SelectItem) (string, error) {
+	if len(items) == 0 {
+		return "", fmt.Errorf("no items to select from")
+	}
+
+	m := selectorModel{
+		title: title,
+		items: items,
+	}
+
+	p := tea.NewProgram(m)
+	finalModel, err := p.Run()
+	if err != nil {
+		return "", fmt.Errorf("error running selector: %w", err)
+	}
+
+	fm := finalModel.(selectorModel)
+	if fm.cancelled {
+		return "", ErrCancelled
+	}
+
+	return fm.selected, nil
+}
+
+// multiSelectorModel is the bubbletea model for multi selection.
+type multiSelectorModel struct {
+	title        string
+	items        []SelectItem
+	itemIndex    map[string]int
+	filter       string
+	cursor       int
+	scrollOffset int
+	checked      map[int]bool
+	checkOrder   []int
+	cancelled    bool
+	confirmed    bool
+}
+
+func newMultiSelectorModel(title string, items []SelectItem, preChecked []string) multiSelectorModel {
+	m := multiSelectorModel{
+		title:     title,
+		items:     items,
+		itemIndex: make(map[string]int, len(items)),
+		checked:   make(map[int]bool),
+	}
+
+	for i, item := range items {
+		m.itemIndex[item.Name] = i
+	}
+
+	for _, name := range preChecked {
+		if idx, ok := m.itemIndex[name]; ok {
+			m.checked[idx] = true
+			m.checkOrder = append(m.checkOrder, idx)
+		}
+	}
+
+	return m
+}
+
+func (m multiSelectorModel) filteredItems() []SelectItem {
+	if m.filter == "" {
+		return m.items
+	}
+	filterLower := strings.ToLower(m.filter)
+	var result []SelectItem
+	for _, item := range m.items {
+		if strings.Contains(strings.ToLower(item.Name), filterLower) {
+			result = append(result, item)
+		}
+	}
+	return result
+}
+
+func (m *multiSelectorModel) toggleItem() {
+	filtered := m.filteredItems()
+	if len(filtered) == 0 || m.cursor >= len(filtered) {
+		return
+	}
+
+	item := filtered[m.cursor]
+	origIdx := m.itemIndex[item.Name]
+
+	if m.checked[origIdx] {
+		delete(m.checked, origIdx)
+		for i, idx := range m.checkOrder {
+			if idx == origIdx {
+				m.checkOrder = append(m.checkOrder[:i], m.checkOrder[i+1:]...)
+				break
+			}
+		}
+	} else {
+		m.checked[origIdx] = true
+		m.checkOrder = append(m.checkOrder, origIdx)
+	}
+}
+
+func (m multiSelectorModel) selectedCount() int {
+	return len(m.checkOrder)
+}
+
+func (m multiSelectorModel) Init() tea.Cmd {
+	return nil
+}
+
+func (m multiSelectorModel) Update(msg tea.Msg) (tea.Model, tea.Cmd) {
+	switch msg := msg.(type) {
+	case tea.KeyMsg:
+		filtered := m.filteredItems()
+
+		switch msg.Type {
+		case tea.KeyCtrlC, tea.KeyEsc:
+			m.cancelled = true
+			return m, tea.Quit
+
+		case tea.KeyEnter:
+			// Enter confirms if at least one item is selected
+			if len(m.checkOrder) > 0 {
+				m.confirmed = true
+				return m, tea.Quit
+			}
+
+		case tea.KeySpace:
+			// Space always toggles selection
+			m.toggleItem()
+
+		case tea.KeyUp:
+			if m.cursor > 0 {
+				m.cursor--
+				if m.cursor < m.scrollOffset {
+					m.scrollOffset = m.cursor
+				}
+			}
+
+		case tea.KeyDown:
+			if m.cursor < len(filtered)-1 {
+				m.cursor++
+				if m.cursor >= m.scrollOffset+maxSelectorItems {
+					m.scrollOffset = m.cursor - maxSelectorItems + 1
+				}
+			}
+
+		case tea.KeyBackspace:
+			if len(m.filter) > 0 {
+				m.filter = m.filter[:len(m.filter)-1]
+				m.cursor = 0
+				m.scrollOffset = 0
+			}
+
+		case tea.KeyRunes:
+			m.filter += string(msg.Runes)
+			m.cursor = 0
+			m.scrollOffset = 0
+		}
+	}
+
+	return m, nil
+}
+
+func (m multiSelectorModel) View() string {
+	// Clear screen when exiting
+	if m.cancelled || m.confirmed {
+		return ""
+	}
+
+	var s strings.Builder
+
+	// Title with filter
+	s.WriteString(selectorTitleStyle.Render(m.title))
+	s.WriteString(" ")
+	if m.filter == "" {
+		s.WriteString(selectorFilterStyle.Render("Type to filter..."))
+	} else {
+		s.WriteString(selectorInputStyle.Render(m.filter))
+	}
+	s.WriteString("\n\n")
+
+	filtered := m.filteredItems()
+
+	if len(filtered) == 0 {
+		s.WriteString(selectorItemStyle.Render(selectorDescStyle.Render("(no matches)")))
+		s.WriteString("\n")
+	} else {
+		displayCount := min(len(filtered), maxSelectorItems)
+
+		for i := range displayCount {
+			idx := m.scrollOffset + i
+			if idx >= len(filtered) {
+				break
+			}
+			item := filtered[idx]
+			origIdx := m.itemIndex[item.Name]
+
+			// Checkbox
+			var checkbox string
+			if m.checked[origIdx] {
+				checkbox = selectorCheckboxCheckedStyle.Render("[x]")
+			} else {
+				checkbox = selectorCheckboxStyle.Render("[ ]")
+			}
+
+			// Cursor and name
+			var line string
+			if idx == m.cursor {
+				line = selectorSelectedItemStyle.Render("▸ ") + checkbox + " " + selectorSelectedItemStyle.Render(item.Name)
+			} else {
+				line = "  " + checkbox + " " + item.Name
+			}
+
+			// Default tag
+			if len(m.checkOrder) > 0 && m.checkOrder[0] == origIdx {
+				line += " " + selectorDefaultTagStyle.Render("(default)")
+			}
+
+			s.WriteString(line)
+			s.WriteString("\n")
+		}
+
+		if remaining := len(filtered) - m.scrollOffset - displayCount; remaining > 0 {
+			s.WriteString(selectorMoreStyle.Render(fmt.Sprintf("... and %d more", remaining)))
+			s.WriteString("\n")
+		}
+	}
+
+	s.WriteString("\n")
+
+	// Status line
+	count := m.selectedCount()
+	if count == 0 {
+		s.WriteString(selectorDescStyle.Render("  Select at least one model."))
+	} else {
+		s.WriteString(selectorDescStyle.Render(fmt.Sprintf("  %d selected - press enter to continue", count)))
+	}
+	s.WriteString("\n\n")
+
+	s.WriteString(selectorHelpStyle.Render("↑/↓ navigate • space toggle • enter confirm • esc cancel"))
+
+	return s.String()
+}
+
+// SelectMultiple prompts the user to select multiple items from a list.
+func SelectMultiple(title string, items []SelectItem, preChecked []string) ([]string, error) {
+	if len(items) == 0 {
+		return nil, fmt.Errorf("no items to select from")
+	}
+
+	m := newMultiSelectorModel(title, items, preChecked)
+
+	p := tea.NewProgram(m)
+	finalModel, err := p.Run()
+	if err != nil {
+		return nil, fmt.Errorf("error running selector: %w", err)
+	}
+
+	fm := finalModel.(multiSelectorModel)
+	if fm.cancelled {
+		return nil, ErrCancelled
+	}
+
+	if !fm.confirmed {
+		return nil, ErrCancelled
+	}
+
+	var result []string
+	for _, idx := range fm.checkOrder {
+		result = append(result, fm.items[idx].Name)
+	}
+
+	return result, nil
+}

cmd/tui/tui.go

@@ -0,0 +1,442 @@
+package tui
+
+import (
+	"context"
+	"fmt"
+	"strings"
+	"time"
+
+	tea "github.com/charmbracelet/bubbletea"
+	"github.com/charmbracelet/lipgloss"
+	"github.com/ollama/ollama/api"
+	"github.com/ollama/ollama/cmd/config"
+	"github.com/ollama/ollama/version"
+)
+
+const (
+	logoNormal = ` ▆▁▂▃▂▁▆
+▟███████▙
+█▙▛▄ ▄▜▟█
+▟█▙▀▀▀▟█▙
+█████████
+▟███████▙
+▀▀▀▀▀▀▀▀▀`
+
+	logoBlink = ` ▆▁▂▃▂▁▆
+▟███████▙
+██▛▄ ▄▜██
+▟█▙▀▀▀▟█▙
+█████████
+▟███████▙
+▀▀▀▀▀▀▀▀▀`
+
+	blinkInterval = 15 * time.Second
+	blinkDuration = 250 * time.Millisecond
+)
+
+type (
+	blinkMsg   struct{}
+	unblinkMsg struct{}
+)
+
+var (
+	logoStyle = lipgloss.NewStyle().
+			Foreground(lipgloss.Color("255")).
+			Background(lipgloss.Color("0"))
+
+	titleStyle = lipgloss.NewStyle().
+			Bold(true).
+			MarginBottom(1)
+
+	versionStyle = lipgloss.NewStyle().
+			Foreground(lipgloss.Color("245"))
+
+	itemStyle = lipgloss.NewStyle().
+			PaddingLeft(2)
+
+	selectedStyle = lipgloss.NewStyle().
+			PaddingLeft(2).
+			Foreground(lipgloss.Color("147")).
+			Bold(true)
+
+	greyedStyle = lipgloss.NewStyle().
+			PaddingLeft(2).
+			Foreground(lipgloss.Color("241"))
+
+	greyedSelectedStyle = lipgloss.NewStyle().
+				PaddingLeft(2).
+				Foreground(lipgloss.Color("243"))
+
+	descStyle = lipgloss.NewStyle().
+			PaddingLeft(4).
+			Foreground(lipgloss.Color("241"))
+
+	modelStyle = lipgloss.NewStyle().
+			Foreground(lipgloss.Color("245"))
+
+	notInstalledStyle = lipgloss.NewStyle().
+				Foreground(lipgloss.Color("241")).
+				Italic(true)
+)
+
+type menuItem struct {
+	title       string
+	description string
+	integration string // integration name for loading model config, empty if not an integration
+	isRunModel  bool   // true for the "Run a model" option
+	isOthers    bool   // true for the "Others..." toggle item
+}
+
+var mainMenuItems = []menuItem{
+	{
+		title:       "Run a model",
+		description: "Start an interactive chat with a local model",
+		isRunModel:  true,
+	},
+	{
+		title:       "Launch Claude Code",
+		description: "Open Claude Code AI assistant",
+		integration: "claude",
+	},
+	{
+		title:       "Launch Open Claw",
+		description: "Open the Open Claw integration",
+		integration: "openclaw",
+	},
+}
+
+var othersMenuItem = menuItem{
+	title:       "Others...",
+	description: "Show additional integrations",
+	isOthers:    true,
+}
+
+// getOtherIntegrations returns the list of other integrations, filtering out
+// Codex if it's not installed (since it requires npm install).
+func getOtherIntegrations() []menuItem {
+	items := []menuItem{
+		{
+			title:       "Launch Droid",
+			description: "Open Droid integration",
+			integration: "droid",
+		},
+		{
+			title:       "Launch Open Code",
+			description: "Open Open Code integration",
+			integration: "opencode",
+		},
+	}
+
+	// Only show Codex if it's already installed
+	if config.IsIntegrationInstalled("codex") {
+		items = append([]menuItem{{
+			title:       "Launch Codex",
+			description: "Open Codex CLI",
+			integration: "codex",
+		}}, items...)
+	}
+
+	return items
+}
+
+type model struct {
+	items           []menuItem
+	cursor          int
+	quitting        bool
+	selected        bool            // true if user made a selection (enter/space)
+	changeModel     bool            // true if user pressed 'm' to change model
+	showOthers      bool            // true if "Others..." is expanded
+	availableModels map[string]bool // cache of available model names
+	blinking        bool            // true when showing blink logo
+	err             error
+}
+
+// modelExists checks if a model exists in the cached available models.
+func (m *model) modelExists(name string) bool {
+	if m.availableModels == nil || name == "" {
+		return false
+	}
+	if m.availableModels[name] {
+		return true
+	}
+	// Check for prefix match (e.g., "llama2" matches "llama2:latest")
+	for modelName := range m.availableModels {
+		if strings.HasPrefix(modelName, name+":") {
+			return true
+		}
+	}
+	return false
+}
+
+// loadAvailableModels fetches and caches the list of available models.
+func (m *model) loadAvailableModels() {
+	m.availableModels = make(map[string]bool)
+	client, err := api.ClientFromEnvironment()
+	if err != nil {
+		return
+	}
+	models, err := client.List(context.Background())
+	if err != nil {
+		return
+	}
+	for _, mdl := range models.Models {
+		m.availableModels[mdl.Name] = true
+	}
+}
+
+func (m *model) buildItems() {
+	others := getOtherIntegrations()
+	m.items = make([]menuItem, 0, len(mainMenuItems)+1+len(others))
+	m.items = append(m.items, mainMenuItems...)
+
+	if m.showOthers {
+		// Change "Others..." to "Hide others..."
+		hideItem := menuItem{
+			title:       "Hide others...",
+			description: "Hide additional integrations",
+			isOthers:    true,
+		}
+		m.items = append(m.items, hideItem)
+		m.items = append(m.items, others...)
+	} else {
+		m.items = append(m.items, othersMenuItem)
+	}
+}
+
+// isOthersIntegration returns true if the integration is in the "Others" menu
+func isOthersIntegration(name string) bool {
+	switch name {
+	case "codex", "droid", "opencode":
+		return true
+	}
+	return false
+}
+
+func initialModel() model {
+	m := model{
+		cursor: 0,
+	}
+	m.loadAvailableModels()
+
+	// Check last selection to determine if we need to expand "Others"
+	lastSelection := config.LastSelection()
+	if isOthersIntegration(lastSelection) {
+		m.showOthers = true
+	}
+
+	m.buildItems()
+
+	// Position cursor on last selection
+	if lastSelection != "" {
+		for i, item := range m.items {
+			if lastSelection == "run" && item.isRunModel {
+				m.cursor = i
+				break
+			} else if item.integration == lastSelection {
+				m.cursor = i
+				break
+			}
+		}
+	}
+
+	return m
+}
+
+func (m model) Init() tea.Cmd {
+	return tea.Tick(blinkInterval, func(t time.Time) tea.Msg {
+		return blinkMsg{}
+	})
+}
+
+func (m model) Update(msg tea.Msg) (tea.Model, tea.Cmd) {
+	switch msg := msg.(type) {
+	case blinkMsg:
+		m.blinking = true
+		return m, tea.Tick(blinkDuration, func(t time.Time) tea.Msg {
+			return unblinkMsg{}
+		})
+
+	case unblinkMsg:
+		m.blinking = false
+		return m, tea.Tick(blinkInterval, func(t time.Time) tea.Msg {
+			return blinkMsg{}
+		})
+
+	case tea.KeyMsg:
+		switch msg.String() {
+		case "ctrl+c", "q", "esc":
+			m.quitting = true
+			return m, tea.Quit
+
+		case "up", "k":
+			if m.cursor > 0 {
+				m.cursor--
+			}
+
+		case "down", "j":
+			if m.cursor < len(m.items)-1 {
+				m.cursor++
+			}
+
+		case "enter", " ":
+			item := m.items[m.cursor]
+
+			// Handle "Others..." toggle
+			if item.isOthers {
+				m.showOthers = !m.showOthers
+				m.buildItems()
+				// Keep cursor on the Others/Hide item
+				if m.cursor >= len(m.items) {
+					m.cursor = len(m.items) - 1
+				}
+				return m, nil
+			}
+
+			// Don't allow selecting uninstalled integrations
+			if item.integration != "" && !config.IsIntegrationInstalled(item.integration) {
+				return m, nil
+			}
+
+			m.selected = true
+			m.quitting = true
+			return m, tea.Quit
+
+		case "m":
+			// Allow model change for integrations and run model
+			item := m.items[m.cursor]
+			if item.integration != "" || item.isRunModel {
+				// Don't allow for uninstalled integrations
+				if item.integration != "" && !config.IsIntegrationInstalled(item.integration) {
+					return m, nil
+				}
+				m.changeModel = true
+				m.quitting = true
+				return m, tea.Quit
+			}
+		}
+	}
+
+	return m, nil
+}
+
+func (m model) View() string {
+	if m.quitting {
+		return ""
+	}
+
+	logo := logoNormal
+	if m.blinking {
+		logo = logoBlink
+	}
+
+	versionText := "\n\n  Ollama " + versionStyle.Render("v"+version.Version)
+
+	logoRendered := logoStyle.Render(logo)
+	logoBlock := lipgloss.NewStyle().Padding(0, 1).MarginLeft(2).Background(lipgloss.Color("0")).Render(logoRendered)
+	versionBlock := titleStyle.Render(versionText)
+	header := lipgloss.JoinHorizontal(lipgloss.Top, logoBlock, versionBlock)
+
+	s := header + "\n\n"
+
+	for i, item := range m.items {
+		cursor := "  "
+		style := itemStyle
+		isInstalled := true
+
+		if item.integration != "" {
+			isInstalled = config.IsIntegrationInstalled(item.integration)
+		}
+
+		if m.cursor == i {
+			cursor = "▸ "
+			if isInstalled {
+				style = selectedStyle
+			} else {
+				style = greyedSelectedStyle
+			}
+		} else if !isInstalled && item.integration != "" {
+			style = greyedStyle
+		}
+
+		title := item.title
+		if item.integration != "" {
+			if !isInstalled {
+				title += " " + notInstalledStyle.Render("(not installed)")
+			} else if mdl := config.IntegrationModel(item.integration); mdl != "" && m.modelExists(mdl) {
+				title += " " + modelStyle.Render("("+mdl+")")
+			}
+		} else if item.isRunModel {
+			if mdl := config.LastModel(); mdl != "" && m.modelExists(mdl) {
+				title += " " + modelStyle.Render("("+mdl+")")
+			}
+		}
+
+		s += style.Render(cursor+title) + "\n"
+		s += descStyle.Render(item.description) + "\n\n"
+	}
+
+	s += "\n" + lipgloss.NewStyle().Foreground(lipgloss.Color("241")).Render("↑/↓ navigate • enter select • m change model • esc quit")
+
+	return s
+}
+
+// Selection represents what the user selected
+type Selection int
+
+const (
+	SelectionNone Selection = iota
+	SelectionRunModel
+	SelectionChangeRunModel
+	SelectionIntegration       // Generic integration selection
+	SelectionChangeIntegration // Generic change model for integration
+)
+
+// Result contains the selection and any associated data
+type Result struct {
+	Selection   Selection
+	Integration string // integration name if applicable
+}
+
+// Run starts the TUI and returns the user's selection
+func Run() (Result, error) {
+	m := initialModel()
+	p := tea.NewProgram(m)
+
+	finalModel, err := p.Run()
+	if err != nil {
+		return Result{Selection: SelectionNone}, fmt.Errorf("error running TUI: %w", err)
+	}
+
+	fm := finalModel.(model)
+	if fm.err != nil {
+		return Result{Selection: SelectionNone}, fm.err
+	}
+
+	// User quit without selecting
+	if !fm.selected && !fm.changeModel {
+		return Result{Selection: SelectionNone}, nil
+	}
+
+	item := fm.items[fm.cursor]
+
+	// Handle model change request
+	if fm.changeModel {
+		if item.isRunModel {
+			return Result{Selection: SelectionChangeRunModel}, nil
+		}
+		return Result{
+			Selection:   SelectionChangeIntegration,
+			Integration: item.integration,
+		}, nil
+	}
+
+	// Handle selection
+	if item.isRunModel {
+		return Result{Selection: SelectionRunModel}, nil
+	}
+
+	return Result{
+		Selection:   SelectionIntegration,
+		Integration: item.integration,
+	}, nil
+}

convert/convert.go

@@ -313,6 +313,8 @@ func LoadModelMetadata(fsys fs.FS) (ModelKV, *Tokenizer, error) {
 		conv = &deepseek2Model{}
 	case "Glm4MoeLiteForCausalLM":
 		conv = &glm4MoeLiteModel{}
+	case "GlmOcrForConditionalGeneration":
+		conv = &glmOcrModel{}
 	case "Lfm2ForCausalLM":
 		conv = &lfm2Model{}
 	default:

convert/convert_glmocr.go

@@ -0,0 +1,455 @@
+package convert
+
+import (
+	"cmp"
+	"encoding/json"
+	"io/fs"
+	"log/slog"
+	"regexp"
+	"strconv"
+	"strings"
+
+	"github.com/ollama/ollama/fs/ggml"
+	"github.com/pdevine/tensor"
+	"github.com/pdevine/tensor/native"
+)
+
+// normalToNeoXRepacker creates a repacker that permutes Q/K weights from interleaved (LLaMA)
+// to NeoX ordering for compatibility with GGML's M-RoPE kernel.
+//
+// For weights: reshape [out, in] -> [n_heads, head_dim, in], permute rotary dims, reshape back
+// For biases: reshape [out] -> [n_heads, head_dim], permute rotary dims, reshape back
+func normalToNeoXRepacker(nHeads, headDim int, partialRotaryFactor float32) func(string, []float32, []uint64) ([]float32, error) {
+	return func(_ string, data []float32, shape []uint64) ([]float32, error) {
+		rotaryDim := int(float32(headDim) * partialRotaryFactor)
+		if rotaryDim%2 != 0 {
+			rotaryDim = (rotaryDim / 2) * 2 // Round down to even
+		}
+
+		// Handle 1D (bias) or 2D (weight) tensors
+		is1D := len(shape) == 1
+		var inFeatures int
+		if is1D {
+			inFeatures = 1
+		} else {
+			inFeatures = int(shape[1])
+		}
+		outFeatures := int(shape[0])
+		nEffectiveHeads := outFeatures / headDim
+
+		if nEffectiveHeads != nHeads {
+			slog.Warn("normalToNeoX: unexpected head count", "effective", nEffectiveHeads, "expected", nHeads)
+		}
+
+		// Reshape to [n_heads, head_dim, in_features]
+		reshaped := make([]float32, len(data))
+		copy(reshaped, data)
+
+		// Permute the rotary dimensions: even indices first, then odd
+		// For each head, reorder [0,1,2,3,4,5...] to [0,2,4...,1,3,5...]
+		result := make([]float32, len(data))
+		halfRotary := rotaryDim / 2
+
+		for h := range nEffectiveHeads {
+			for f := range inFeatures {
+				for i := range halfRotary {
+					// Even dim (0, 2, 4, ...) -> position i
+					srcIdx := h*headDim*inFeatures + (2*i)*inFeatures + f
+					dstIdx := h*headDim*inFeatures + i*inFeatures + f
+					result[dstIdx] = reshaped[srcIdx]
+
+					// Odd dim (1, 3, 5, ...) -> position halfRotary + i
+					srcIdx = h*headDim*inFeatures + (2*i+1)*inFeatures + f
+					dstIdx = h*headDim*inFeatures + (halfRotary+i)*inFeatures + f
+					result[dstIdx] = reshaped[srcIdx]
+				}
+
+				// Non-rotary part: copy as-is
+				for i := rotaryDim; i < headDim; i++ {
+					srcIdx := h*headDim*inFeatures + i*inFeatures + f
+					result[srcIdx] = reshaped[srcIdx]
+				}
+			}
+		}
+
+		return result, nil
+	}
+}
+
+type glmOcrModel struct {
+	ModelParameters
+
+	TextConfig struct {
+		HiddenSize          uint32  `json:"hidden_size"`
+		IntermediateSize    uint32  `json:"intermediate_size"`
+		NumHiddenLayers     uint32  `json:"num_hidden_layers"`
+		NumAttentionHeads   uint32  `json:"num_attention_heads"`
+		NumKeyValueHeads    uint32  `json:"num_key_value_heads"`
+		HeadDim             uint32  `json:"head_dim"`
+		MaxPositionEmbed    uint32  `json:"max_position_embeddings"`
+		RMSNormEps          float32 `json:"rms_norm_eps"`
+		PartialRotaryFactor float32 `json:"partial_rotary_factor"`
+		RopeParameters      struct {
+			RopeType            string  `json:"rope_type"`
+			MRopeSection        []int32 `json:"mrope_section"`
+			RopeTheta           float32 `json:"rope_theta"`
+			PartialRotaryFactor float32 `json:"partial_rotary_factor"`
+		} `json:"rope_parameters"`
+	} `json:"text_config"`
+
+	VisionConfig struct {
+		HiddenSize        uint32  `json:"hidden_size"`
+		IntermediateSize  uint32  `json:"intermediate_size"`
+		Depth             uint32  `json:"depth"`
+		NumHeads          uint32  `json:"num_heads"`
+		ImageSize         uint32  `json:"image_size"`
+		PatchSize         uint32  `json:"patch_size"`
+		OutHiddenSize     uint32  `json:"out_hidden_size"`
+		RMSNormEps        float32 `json:"rms_norm_eps"`
+		SpatialMergeSize  uint32  `json:"spatial_merge_size"`
+		TemporalPatchSize uint32  `json:"temporal_patch_size"`
+	} `json:"vision_config"`
+
+	ImageStartTokenID uint32 `json:"image_start_token_id"`
+	ImageEndTokenID   uint32 `json:"image_end_token_id"`
+	VideoStartTokenID uint32 `json:"video_start_token_id"`
+	VideoEndTokenID   uint32 `json:"video_end_token_id"`
+	ImageTokenID      uint32 `json:"image_token_id"`
+	VideoTokenID      uint32 `json:"video_token_id"`
+
+	// Preprocessor config (preprocessor_config.json)
+	Preprocessor struct {
+		Size struct {
+			ShortestEdge uint32 `json:"shortest_edge"`
+			LongestEdge  uint32 `json:"longest_edge"`
+		} `json:"size"`
+		PatchSize         uint32    `json:"patch_size"`
+		TemporalPatchSize uint32    `json:"temporal_patch_size"`
+		MergeSize         uint32    `json:"merge_size"`
+		ImageMean         []float32 `json:"image_mean"`
+		ImageStd          []float32 `json:"image_std"`
+	} `json:"-"`
+}
+
+var _ ModelConverter = (*glmOcrModel)(nil)
+
+func (m *glmOcrModel) parseMore(fsys fs.FS) error {
+	bts, err := fs.ReadFile(fsys, "preprocessor_config.json")
+	if err != nil {
+		return err
+	}
+
+	return json.Unmarshal(bts, &m.Preprocessor)
+}
+
+func (m *glmOcrModel) KV(t *Tokenizer) KV {
+	kv := m.ModelParameters.KV(t)
+	kv["general.architecture"] = "glmocr"
+
+	// Text model parameters
+	kv["glmocr.block_count"] = cmp.Or(m.TextConfig.NumHiddenLayers, 16)
+	kv["glmocr.embedding_length"] = cmp.Or(m.TextConfig.HiddenSize, 1536)
+	kv["glmocr.attention.head_count"] = cmp.Or(m.TextConfig.NumAttentionHeads, 16)
+	kv["glmocr.attention.head_count_kv"] = cmp.Or(m.TextConfig.NumKeyValueHeads, 8)
+	headDim := cmp.Or(m.TextConfig.HeadDim, m.TextConfig.HiddenSize/m.TextConfig.NumAttentionHeads)
+	kv["glmocr.attention.key_length"] = headDim
+	kv["glmocr.attention.value_length"] = headDim
+	kv["glmocr.feed_forward_length"] = cmp.Or(m.TextConfig.IntermediateSize, 4608)
+	kv["glmocr.attention.layer_norm_rms_epsilon"] = cmp.Or(m.TextConfig.RMSNormEps, 1e-5)
+	kv["glmocr.context_length"] = cmp.Or(m.TextConfig.MaxPositionEmbed, 131072)
+	kv["glmocr.rope.freq_base"] = cmp.Or(m.TextConfig.RopeParameters.RopeTheta, float32(10000))
+	kv["glmocr.rope.partial_rotary_factor"] = cmp.Or(m.TextConfig.RopeParameters.PartialRotaryFactor, m.TextConfig.PartialRotaryFactor, float32(1.0))
+	if len(m.TextConfig.RopeParameters.MRopeSection) > 0 {
+		kv["glmocr.rope.mrope_section"] = m.TextConfig.RopeParameters.MRopeSection
+	}
+
+	// Vision model parameters
+	kv["glmocr.vision.block_count"] = cmp.Or(m.VisionConfig.Depth, 24)
+	kv["glmocr.vision.embedding_length"] = cmp.Or(m.VisionConfig.HiddenSize, 1024)
+	kv["glmocr.vision.attention.head_count"] = cmp.Or(m.VisionConfig.NumHeads, 16)
+	kv["glmocr.vision.image_size"] = cmp.Or(m.VisionConfig.ImageSize, 336)
+	kv["glmocr.vision.patch_size"] = cmp.Or(m.VisionConfig.PatchSize, m.Preprocessor.PatchSize, 14)
+	kv["glmocr.vision.spatial_merge_size"] = cmp.Or(m.VisionConfig.SpatialMergeSize, m.Preprocessor.MergeSize, 2)
+	kv["glmocr.vision.temporal_patch_size"] = cmp.Or(m.VisionConfig.TemporalPatchSize, m.Preprocessor.TemporalPatchSize, 2)
+	kv["glmocr.vision.out_hidden_size"] = cmp.Or(m.VisionConfig.OutHiddenSize, 1536)
+	kv["glmocr.vision.intermediate_size"] = cmp.Or(m.VisionConfig.IntermediateSize, 4096)
+	kv["glmocr.vision.attention.layer_norm_rms_epsilon"] = cmp.Or(m.VisionConfig.RMSNormEps, 1e-5)
+
+	// Preprocessor-derived image settings (min/max pixels and normalization)
+	// Note: fs.Config.keyValue() auto-prepends architecture prefix, so use full key
+	if m.Preprocessor.Size.ShortestEdge > 0 {
+		kv["glmocr.vision.min_pixels"] = m.Preprocessor.Size.ShortestEdge
+	}
+	if m.Preprocessor.Size.LongestEdge > 0 {
+		kv["glmocr.vision.max_pixels"] = m.Preprocessor.Size.LongestEdge
+	}
+	if len(m.Preprocessor.ImageMean) == 3 {
+		kv["glmocr.vision.image_mean"] = m.Preprocessor.ImageMean
+	}
+	if len(m.Preprocessor.ImageStd) == 3 {
+		kv["glmocr.vision.image_std"] = m.Preprocessor.ImageStd
+	}
+
+	// Special tokens
+	kv["glmocr.image_token_id"] = m.ImageTokenID
+	kv["glmocr.image_start_token_id"] = m.ImageStartTokenID
+	kv["glmocr.image_end_token_id"] = m.ImageEndTokenID
+	kv["glmocr.video_token_id"] = m.VideoTokenID
+	kv["glmocr.video_start_token_id"] = m.VideoStartTokenID
+	kv["glmocr.video_end_token_id"] = m.VideoEndTokenID
+
+	return kv
+}
+
+func (m *glmOcrModel) Tensors(ts []Tensor) []*ggml.Tensor {
+	var out []*ggml.Tensor
+
+	// Skip layers >= num_hidden_layers (Multi-Token Prediction layers not needed for basic inference)
+	numLayers := int(cmp.Or(m.TextConfig.NumHiddenLayers, 16))
+	skipLayer := func(name string) bool {
+		// Tensor names are already replaced to "blk.N.xxx" format
+		re := regexp.MustCompile(`^blk\.(\d+)`)
+		matches := re.FindStringSubmatch(name)
+		if matches == nil {
+			return false
+		}
+		blkNum, err := strconv.Atoi(matches[1])
+		if err != nil {
+			return false
+		}
+		return blkNum >= numLayers
+	}
+
+	for _, t := range ts {
+		name := t.Name()
+
+		// Skip next-n prediction layers (layers >= num_hidden_layers)
+		if skipLayer(name) {
+			continue
+		}
+
+		// Split ffn_gate_up into separate gate and up projections
+		if strings.Contains(name, "ffn_gate_up") {
+			for t := range splitDim(t, 0,
+				split{Replacer: strings.NewReplacer("ffn_gate_up", "ffn_gate")},
+				split{Replacer: strings.NewReplacer("ffn_gate_up", "ffn_up")},
+			) {
+				out = append(out, t)
+			}
+			continue
+		}
+
+		if strings.HasSuffix(name, "patch_embd.weight") {
+			shape := t.Shape()
+			if len(shape) == 5 && shape[2] == 2 {
+				newShape := []uint64{shape[0], shape[1], shape[3], shape[4]}
+
+				t0 := t.Clone()
+				t0.SetRepacker(func(_ string, data []float32, shape []uint64) ([]float32, error) {
+					dims := make([]int, len(shape))
+					for i := range shape {
+						dims[i] = int(shape[i])
+					}
+					var tt tensor.Tensor = tensor.New(tensor.WithShape(dims...), tensor.WithBacking(data))
+					tt, err := tt.Slice(nil, nil, tensor.S(0, 1), nil, nil)
+					if err != nil {
+						return nil, err
+					}
+					tt = tensor.Materialize(tt)
+					newDims := []int{int(shape[0]), int(shape[1]), int(shape[3]), int(shape[4])}
+					if err := tt.Reshape(newDims...); err != nil {
+						return nil, err
+					}
+					if err := tt.Reshape(tt.Shape().TotalSize()); err != nil {
+						return nil, err
+					}
+					return native.VectorF32(tt.(*tensor.Dense))
+				})
+				out = append(out, &ggml.Tensor{
+					Name:     strings.Replace(name, "patch_embd.weight", "patch_embd_0.weight", 1),
+					Kind:     t.Kind(),
+					Shape:    newShape,
+					WriterTo: t0,
+				})
+
+				t1 := t.Clone()
+				t1.SetRepacker(func(_ string, data []float32, shape []uint64) ([]float32, error) {
+					dims := make([]int, len(shape))
+					for i := range shape {
+						dims[i] = int(shape[i])
+					}
+					var tt tensor.Tensor = tensor.New(tensor.WithShape(dims...), tensor.WithBacking(data))
+					tt, err := tt.Slice(nil, nil, tensor.S(1, 2), nil, nil)
+					if err != nil {
+						return nil, err
+					}
+					tt = tensor.Materialize(tt)
+					newDims := []int{int(shape[0]), int(shape[1]), int(shape[3]), int(shape[4])}
+					if err := tt.Reshape(newDims...); err != nil {
+						return nil, err
+					}
+					if err := tt.Reshape(tt.Shape().TotalSize()); err != nil {
+						return nil, err
+					}
+					return native.VectorF32(tt.(*tensor.Dense))
+				})
+				out = append(out, &ggml.Tensor{
+					Name:     strings.Replace(name, "patch_embd.weight", "patch_embd_1.weight", 1),
+					Kind:     t.Kind(),
+					Shape:    newShape,
+					WriterTo: t1,
+				})
+
+				continue
+			}
+
+			if len(shape) == 4 {
+				out = append(out, &ggml.Tensor{
+					Name:     strings.Replace(name, "patch_embd.weight", "patch_embd_0.weight", 1),
+					Kind:     t.Kind(),
+					Shape:    t.Shape(),
+					WriterTo: t,
+				})
+				continue
+			}
+
+			slog.Warn("glmocr: patch_embed weight has unexpected shape - not splitting", "shape", shape)
+			// Fall through to default handling
+		}
+
+		// Handle pre-split patch embedding weights
+		// Pattern 1: v.patch_embd.0.weight, v.patch_embd.1.weight -> patch_embd_0.weight, patch_embd_1.weight
+		// Pattern 2: v.patch_embd.weight.0, v.patch_embd.weight.1 -> patch_embd_0.weight, patch_embd_1.weight
+		if strings.Contains(name, "patch_embd.0.") {
+			out = append(out, &ggml.Tensor{
+				Name:     strings.Replace(name, "patch_embd.0.", "patch_embd_0.", 1),
+				Kind:     t.Kind(),
+				Shape:    t.Shape(),
+				WriterTo: t,
+			})
+			continue
+		}
+		if strings.Contains(name, "patch_embd.1.") {
+			out = append(out, &ggml.Tensor{
+				Name:     strings.Replace(name, "patch_embd.1.", "patch_embd_1.", 1),
+				Kind:     t.Kind(),
+				Shape:    t.Shape(),
+				WriterTo: t,
+			})
+			continue
+		}
+		// Handle .weight.0 and .weight.1 suffix patterns
+		if strings.HasSuffix(name, "patch_embd.weight.0") {
+			out = append(out, &ggml.Tensor{
+				Name:     strings.Replace(name, "patch_embd.weight.0", "patch_embd_0.weight", 1),
+				Kind:     t.Kind(),
+				Shape:    t.Shape(),
+				WriterTo: t,
+			})
+			continue
+		}
+		if strings.HasSuffix(name, "patch_embd.weight.1") {
+			out = append(out, &ggml.Tensor{
+				Name:     strings.Replace(name, "patch_embd.weight.1", "patch_embd_1.weight", 1),
+				Kind:     t.Kind(),
+				Shape:    t.Shape(),
+				WriterTo: t,
+			})
+			continue
+		}
+
+		// Permute Q/K weights for M-RoPE compatibility (interleaved -> NeoX ordering)
+		// GGML's M-RoPE kernel uses NeoX-style rotation, but GLM-OCR uses interleaved (LLaMA-style)
+		// We permute at conversion time so the weights work correctly with GGML's kernel
+		// This aligns Q/K rotary dimensions with GGML's NeoX-style rotation
+		if len(m.TextConfig.RopeParameters.MRopeSection) > 0 &&
+			strings.Contains(name, "blk.") && (strings.Contains(name, "attn_q.") || strings.Contains(name, "attn_k.")) {
+			// Get config values for permutation
+			nHeads := int(cmp.Or(m.TextConfig.NumAttentionHeads, 16))
+			nKVHeads := int(cmp.Or(m.TextConfig.NumKeyValueHeads, 8))
+			hiddenSize := int(cmp.Or(m.TextConfig.HiddenSize, 1536))
+			headDim := int(cmp.Or(m.TextConfig.HeadDim, uint32(hiddenSize/nHeads)))
+			partialRotaryFactor := cmp.Or(m.TextConfig.PartialRotaryFactor, m.TextConfig.RopeParameters.PartialRotaryFactor, float32(1.0))
+
+			// Use appropriate head count: nHeads for Q, nKVHeads for K
+			effectiveHeads := nHeads
+			if strings.Contains(name, "attn_k.") {
+				effectiveHeads = nKVHeads
+			}
+
+			permutedT := t.Clone()
+			permutedT.SetRepacker(normalToNeoXRepacker(effectiveHeads, headDim, partialRotaryFactor))
+			out = append(out, &ggml.Tensor{
+				Name:     name,
+				Kind:     t.Kind(),
+				Shape:    t.Shape(),
+				WriterTo: permutedT,
+			})
+			continue
+		}
+
+		out = append(out, &ggml.Tensor{
+			Name:     name,
+			Kind:     t.Kind(),
+			Shape:    t.Shape(),
+			WriterTo: t,
+		})
+	}
+
+	return out
+}
+
+func (m *glmOcrModel) Replacements() []string {
+	return []string{
+		// Vision encoder
+		"model.visual.patch_embed.proj_1", "v.patch_embd_1", // Second temporal split
+		"model.visual.patch_embed.proj", "v.patch_embd",
+		"model.visual.blocks", "v.blk",
+		"model.visual.post_layernorm", "v.post_ln",
+		"model.visual.downsample", "mm.patch_merger",
+
+		// Vision attention
+		"attn.qkv", "attn_qkv",
+		"attn.proj", "attn_out",
+		"attn.q_norm", "attn_q_norm",
+		"attn.k_norm", "attn_k_norm",
+
+		// Vision norms
+		"norm1", "ln1",
+		"norm2", "ln2",
+
+		// Vision MLP
+		"mlp.gate_proj", "ffn_gate",
+		"mlp.up_proj", "ffn_up",
+		"mlp.down_proj", "ffn_down",
+
+		// Merger (multimodal projector)
+		"model.visual.merger.proj", "mm.model.fc",
+		"model.visual.merger.post_projection_norm", "mm.post_norm",
+		"model.visual.merger.gate_proj", "mm.gate",
+		"model.visual.merger.up_proj", "mm.up",
+		"model.visual.merger.down_proj", "mm.down",
+
+		// Language model
+		"model.language_model.embed_tokens", "token_embd",
+		"model.language_model.layers", "blk",
+		"model.language_model.norm", "output_norm",
+		"lm_head", "output",
+
+		// Language model attention
+		"self_attn.q_proj", "attn_q",
+		"self_attn.k_proj", "attn_k",
+		"self_attn.v_proj", "attn_v",
+		"self_attn.o_proj", "attn_out",
+
+		// Language model norms
+		"input_layernorm", "attn_norm",
+		"post_attention_layernorm", "ffn_norm",
+		"post_self_attn_layernorm", "post_attn_norm",
+		"post_mlp_layernorm", "post_ffn_norm",
+
+		// Language model MLP (remove mlp. prefix so ffn_* names work)
+		"mlp.gate_up_proj", "ffn_gate_up",
+		"mlp.down_proj", "ffn_down",
+	}
+}

convert/reader_safetensors.go

@@ -99,6 +99,7 @@ func (st safetensor) Kind() uint32 {
 	if st.dtype == "BF16" &&
 		!strings.HasPrefix(st.name, "v.") &&
 		!strings.HasPrefix(st.name, "s.") &&
+		!strings.HasPrefix(st.name, "mm.") &&
 		kind != tensorKindFP32 {
 		kind = tensorKindBF16
 	}

docs/docs.json

@@ -71,6 +71,10 @@
     {
       "source": "/api",
       "destination": "/api/introduction"
+    },
+    {
+      "source": "/integrations/clawdbot",
+      "destination": "/integrations/openclaw"
     }
   ],
   "navigation": {
@@ -103,6 +107,7 @@
             "pages": [
               "/integrations/claude-code",
               "/integrations/cline",
+              "/integrations/openclaw",
               "/integrations/codex",
               "/integrations/droid",
               "/integrations/goose",

docs/gpu.mdx

@@ -10,6 +10,7 @@ Check your compute compatibility to see if your card is supported:
 
 | Compute Capability | Family              | Cards                                                                                                                          |
 | ------------------ | ------------------- | ------------------------------------------------------------------------------------------------------------------------------ |
+| 12.1               | NVIDIA              | `GB10 (DGX Spark)`                                                                                                             |
 | 12.0               | GeForce RTX 50xx    | `RTX 5060` `RTX 5060 Ti` `RTX 5070` `RTX 5070 Ti` `RTX 5080` `RTX 5090`                                                        |
 |                    | NVIDIA Professional | `RTX PRO 4000 Blackwell` `RTX PRO 4500 Blackwell` `RTX PRO 5000 Blackwell` `RTX PRO 6000 Blackwell`                            |
 | 9.0                | NVIDIA              | `H200` `H100`                                                                                                                  |
@@ -163,4 +164,4 @@ To select specific Vulkan GPU(s), you can set the environment variable
 `GGML_VK_VISIBLE_DEVICES` to one or more numeric IDs on the Ollama server as
 described in the [FAQ](faq#how-do-i-configure-ollama-server). If you
 encounter any problems with Vulkan based GPUs, you can disable all Vulkan GPUs
-by setting `GGML_VK_VISIBLE_DEVICES=-1` 
+by setting `GGML_VK_VISIBLE_DEVICES=-1` 

docs/import.mdx

@@ -134,22 +134,12 @@ success
 
 ### Supported Quantizations
 
-- `q4_0`
-- `q4_1`
-- `q5_0`
-- `q5_1`
 - `q8_0`
 
 #### K-means Quantizations
 
-- `q3_K_S`
-- `q3_K_M`
-- `q3_K_L`
 - `q4_K_S`
 - `q4_K_M`
-- `q5_K_S`
-- `q5_K_M`
-- `q6_K`
 
 ## Sharing your model on ollama.com
 

docs/integrations/openclaw.mdx

@@ -0,0 +1,50 @@
+---
+title: OpenClaw
+---
+
+OpenClaw is a personal AI assistant that runs on your own devices. It bridges messaging services (WhatsApp, Telegram, Slack, Discord, iMessage, and more) to AI coding agents through a centralized gateway.
+
+## Install
+
+Install [OpenClaw](https://openclaw.ai/) 
+
+```bash
+npm install -g openclaw@latest
+```
+
+Then run the onboarding wizard:
+
+```bash
+openclaw onboard --install-daemon
+```
+
+<Note>OpenClaw requires a larger context window. It is recommended to use a context window of at least 64k tokens. See [Context length](/context-length) for more information.</Note>
+
+## Usage with Ollama
+
+### Quick setup
+
+```bash
+ollama launch openclaw
+```
+
+<Note>Previously known as Clawdbot. `ollama launch clawdbot` still works as an alias.</Note>
+
+This configures OpenClaw to use Ollama and starts the gateway.
+If the gateway is already running, no changes need to be made as the gateway will auto-reload the changes. 
+
+
+To configure without launching:
+
+```shell
+ollama launch openclaw --config
+```
+
+## Recommended Models
+
+- `qwen3-coder`
+- `glm-4.7`
+- `gpt-oss:20b`
+- `gpt-oss:120b`
+
+Cloud models are also available at [ollama.com/search?c=cloud](https://ollama.com/search?c=cloud).

docs/integrations/opencode.mdx

@@ -9,7 +9,7 @@ OpenCode is an open-source AI coding assistant that runs in your terminal.
 Install the [OpenCode CLI](https://opencode.ai):
 
 ```bash
-curl -fsSL https://opencode.ai/install.sh | bash
+curl -fsSL https://opencode.ai/install | bash
 ```
 
 <Note>OpenCode requires a larger context window. It is recommended to use a context window of at least 64k tokens. See [Context length](/context-length) for more information.</Note>

envconfig/config.go

@@ -201,7 +201,7 @@ var (
 	// Enable the new Ollama engine
 	NewEngine = Bool("OLLAMA_NEW_ENGINE")
 	// ContextLength sets the default context length
-	ContextLength = Uint("OLLAMA_CONTEXT_LENGTH", 4096)
+	ContextLength = Uint("OLLAMA_CONTEXT_LENGTH", 0)
 	// Auth enables authentication between the Ollama client and server
 	UseAuth = Bool("OLLAMA_AUTH")
 	// Enable Vulkan backend
@@ -290,7 +290,7 @@ func AsMap() map[string]EnvVar {
 		"OLLAMA_ORIGINS":           {"OLLAMA_ORIGINS", AllowedOrigins(), "A comma separated list of allowed origins"},
 		"OLLAMA_SCHED_SPREAD":      {"OLLAMA_SCHED_SPREAD", SchedSpread(), "Always schedule model across all GPUs"},
 		"OLLAMA_MULTIUSER_CACHE":   {"OLLAMA_MULTIUSER_CACHE", MultiUserCache(), "Optimize prompt caching for multi-user scenarios"},
-		"OLLAMA_CONTEXT_LENGTH":    {"OLLAMA_CONTEXT_LENGTH", ContextLength(), "Context length to use unless otherwise specified (default: 4096)"},
+		"OLLAMA_CONTEXT_LENGTH":    {"OLLAMA_CONTEXT_LENGTH", ContextLength(), "Context length to use unless otherwise specified (default: 4k/32k/256k based on VRAM)"},
 		"OLLAMA_NEW_ENGINE":        {"OLLAMA_NEW_ENGINE", NewEngine(), "Enable the new Ollama engine"},
 		"OLLAMA_REMOTES":           {"OLLAMA_REMOTES", Remotes(), "Allowed hosts for remote models (default \"ollama.com\")"},
 

envconfig/config_test.go

@@ -282,7 +282,7 @@ func TestVar(t *testing.T) {
 
 func TestContextLength(t *testing.T) {
 	cases := map[string]uint{
-		"":     4096,
+		"":     0,
 		"2048": 2048,
 	}
 

fs/ggml/ggml.go

@@ -270,6 +270,7 @@ func (kv KV) OllamaEngineRequired() bool {
 		"qwen3", "qwen3moe",
 		"qwen3vl", "qwen3vlmoe",
 		"glm4moelite",
+		"glmocr",
 		"lfm2",
 	}, kv.Architecture())
 }
@@ -859,6 +860,7 @@ func (f GGML) FlashAttention() bool {
 		"bert",
 		"gemma3",
 		"glm4moelite",
+		"glmocr",
 		"gptoss", "gpt-oss",
 		"lfm2",
 		"mistral3",

go.mod

@@ -13,7 +13,7 @@ require (
 	github.com/mattn/go-sqlite3 v1.14.24
 	github.com/olekukonko/tablewriter v0.0.5
 	github.com/spf13/cobra v1.7.0
-	github.com/stretchr/testify v1.10.0
+	github.com/stretchr/testify v1.9.0
 	github.com/x448/float16 v0.8.4
 	golang.org/x/sync v0.17.0
 	golang.org/x/sys v0.37.0
@@ -21,15 +21,15 @@ require (
 
 require (
 	github.com/agnivade/levenshtein v1.1.1
+	github.com/charmbracelet/bubbletea v1.3.10
+	github.com/charmbracelet/lipgloss v1.1.0
 	github.com/d4l3k/go-bfloat16 v0.0.0-20211005043715-690c3bdd05f1
 	github.com/dlclark/regexp2 v1.11.4
 	github.com/emirpasic/gods/v2 v2.0.0-alpha
-	github.com/mattn/go-runewidth v0.0.14
+	github.com/mattn/go-runewidth v0.0.16
 	github.com/nlpodyssey/gopickle v0.3.0
 	github.com/pdevine/tensor v0.0.0-20240510204454-f88f4562727c
 	github.com/tkrajina/typescriptify-golang-structs v0.2.0
-	github.com/tree-sitter/go-tree-sitter v0.25.0
-	github.com/tree-sitter/tree-sitter-cpp v0.23.4
 	github.com/wk8/go-ordered-map/v2 v2.1.8
 	golang.org/x/image v0.22.0
 	golang.org/x/mod v0.30.0
@@ -39,23 +39,34 @@ require (
 
 require (
 	github.com/apache/arrow/go/arrow v0.0.0-20211112161151-bc219186db40 // indirect
+	github.com/aymanbagabas/go-osc52/v2 v2.0.1 // indirect
 	github.com/bahlo/generic-list-go v0.2.0 // indirect
 	github.com/buger/jsonparser v1.1.1 // indirect
 	github.com/bytedance/sonic/loader v0.1.1 // indirect
+	github.com/charmbracelet/colorprofile v0.2.3-0.20250311203215-f60798e515dc // indirect
+	github.com/charmbracelet/x/ansi v0.10.1 // indirect
+	github.com/charmbracelet/x/cellbuf v0.0.13-0.20250311204145-2c3ea96c31dd // indirect
+	github.com/charmbracelet/x/term v0.2.1 // indirect
 	github.com/chewxy/hm v1.0.0 // indirect
 	github.com/chewxy/math32 v1.11.0 // indirect
 	github.com/cloudwego/base64x v0.1.4 // indirect
 	github.com/cloudwego/iasm v0.2.0 // indirect
 	github.com/davecgh/go-spew v1.1.1 // indirect
+	github.com/erikgeiser/coninput v0.0.0-20211004153227-1c3628e74d0f // indirect
 	github.com/gogo/protobuf v1.3.2 // indirect
 	github.com/google/flatbuffers v24.3.25+incompatible // indirect
 	github.com/kr/text v0.2.0 // indirect
+	github.com/lucasb-eyer/go-colorful v1.2.0 // indirect
 	github.com/mailru/easyjson v0.7.7 // indirect
-	github.com/mattn/go-pointer v0.0.1 // indirect
+	github.com/mattn/go-localereader v0.0.1 // indirect
+	github.com/muesli/ansi v0.0.0-20230316100256-276c6243b2f6 // indirect
+	github.com/muesli/cancelreader v0.2.2 // indirect
+	github.com/muesli/termenv v0.16.0 // indirect
 	github.com/pkg/errors v0.9.1 // indirect
 	github.com/pmezard/go-difflib v1.0.0 // indirect
-	github.com/rivo/uniseg v0.2.0 // indirect
+	github.com/rivo/uniseg v0.4.7 // indirect
 	github.com/tkrajina/go-reflector v0.5.5 // indirect
+	github.com/xo/terminfo v0.0.0-20220910002029-abceb7e1c41e // indirect
 	github.com/xtgo/set v1.0.0 // indirect
 	go4.org/unsafe/assume-no-moving-gc v0.0.0-20231121144256-b99613f794b6 // indirect
 	golang.org/x/xerrors v0.0.0-20200804184101-5ec99f83aff1 // indirect

go.sum

@@ -14,6 +14,8 @@ github.com/apache/arrow/go/arrow v0.0.0-20211112161151-bc219186db40 h1:q4dksr6IC
 github.com/apache/arrow/go/arrow v0.0.0-20211112161151-bc219186db40/go.mod h1:Q7yQnSMnLvcXlZ8RV+jwz/6y1rQTqbX6C82SndT52Zs=
 github.com/arbovm/levenshtein v0.0.0-20160628152529-48b4e1c0c4d0 h1:jfIu9sQUG6Ig+0+Ap1h4unLjW6YQJpKZVmUzxsD4E/Q=
 github.com/arbovm/levenshtein v0.0.0-20160628152529-48b4e1c0c4d0/go.mod h1:t2tdKJDJF9BV14lnkjHmOQgcvEKgtqs5a1N3LNdJhGE=
+github.com/aymanbagabas/go-osc52/v2 v2.0.1 h1:HwpRHbFMcZLEVr42D4p7XBqjyuxQH5SMiErDT4WkJ2k=
+github.com/aymanbagabas/go-osc52/v2 v2.0.1/go.mod h1:uYgXzlJ7ZpABp8OJ+exZzJJhRNQ2ASbcXHWsFqH8hp8=
 github.com/bahlo/generic-list-go v0.2.0 h1:5sz/EEAK+ls5wF+NeqDpk5+iNdMDXrh3z3nPnH1Wvgk=
 github.com/bahlo/generic-list-go v0.2.0/go.mod h1:2KvAjgMlE5NNynlg/5iLrrCCZ2+5xWbdbCW3pNTGyYg=
 github.com/boombuler/barcode v1.0.0/go.mod h1:paBWMcWSl3LHKBqUq+rly7CNSldXjb2rDl3JlRe0mD8=
@@ -24,6 +26,18 @@ github.com/bytedance/sonic v1.11.6/go.mod h1:LysEHSvpvDySVdC2f87zGWf6CIKJcAvqab1
 github.com/bytedance/sonic/loader v0.1.1 h1:c+e5Pt1k/cy5wMveRDyk2X4B9hF4g7an8N3zCYjJFNM=
 github.com/bytedance/sonic/loader v0.1.1/go.mod h1:ncP89zfokxS5LZrJxl5z0UJcsk4M4yY2JpfqGeCtNLU=
 github.com/census-instrumentation/opencensus-proto v0.2.1/go.mod h1:f6KPmirojxKA12rnyqOA5BBL4O983OfeGPqjHWSTneU=
+github.com/charmbracelet/bubbletea v1.3.10 h1:otUDHWMMzQSB0Pkc87rm691KZ3SWa4KUlvF9nRvCICw=
+github.com/charmbracelet/bubbletea v1.3.10/go.mod h1:ORQfo0fk8U+po9VaNvnV95UPWA1BitP1E0N6xJPlHr4=
+github.com/charmbracelet/colorprofile v0.2.3-0.20250311203215-f60798e515dc h1:4pZI35227imm7yK2bGPcfpFEmuY1gc2YSTShr4iJBfs=
+github.com/charmbracelet/colorprofile v0.2.3-0.20250311203215-f60798e515dc/go.mod h1:X4/0JoqgTIPSFcRA/P6INZzIuyqdFY5rm8tb41s9okk=
+github.com/charmbracelet/lipgloss v1.1.0 h1:vYXsiLHVkK7fp74RkV7b2kq9+zDLoEU4MZoFqR/noCY=
+github.com/charmbracelet/lipgloss v1.1.0/go.mod h1:/6Q8FR2o+kj8rz4Dq0zQc3vYf7X+B0binUUBwA0aL30=
+github.com/charmbracelet/x/ansi v0.10.1 h1:rL3Koar5XvX0pHGfovN03f5cxLbCF2YvLeyz7D2jVDQ=
+github.com/charmbracelet/x/ansi v0.10.1/go.mod h1:3RQDQ6lDnROptfpWuUVIUG64bD2g2BgntdxH0Ya5TeE=
+github.com/charmbracelet/x/cellbuf v0.0.13-0.20250311204145-2c3ea96c31dd h1:vy0GVL4jeHEwG5YOXDmi86oYw2yuYUGqz6a8sLwg0X8=
+github.com/charmbracelet/x/cellbuf v0.0.13-0.20250311204145-2c3ea96c31dd/go.mod h1:xe0nKWGd3eJgtqZRaN9RjMtK7xUYchjzPr7q6kcvCCs=
+github.com/charmbracelet/x/term v0.2.1 h1:AQeHeLZ1OqSXhrAWpYUtZyX1T3zVxfpZuEQMIQaGIAQ=
+github.com/charmbracelet/x/term v0.2.1/go.mod h1:oQ4enTYFV7QN4m0i9mzHrViD7TQKvNEEkHUMCmsxdUg=
 github.com/chewxy/hm v1.0.0 h1:zy/TSv3LV2nD3dwUEQL2VhXeoXbb9QkpmdRAVUFiA6k=
 github.com/chewxy/hm v1.0.0/go.mod h1:qg9YI4q6Fkj/whwHR1D+bOGeF7SniIP40VweVepLjg0=
 github.com/chewxy/math32 v1.0.0/go.mod h1:Miac6hA1ohdDUTagnvJy/q+aNnEk16qWUdb8ZVhvCN0=
@@ -59,6 +73,8 @@ github.com/envoyproxy/go-control-plane v0.9.9-0.20201210154907-fd9021fe5dad/go.m
 github.com/envoyproxy/go-control-plane v0.9.9-0.20210217033140-668b12f5399d/go.mod h1:cXg6YxExXjJnVBQHBLXeUAgxn2UodCpnH306RInaBQk=
 github.com/envoyproxy/go-control-plane v0.9.9-0.20210512163311-63b5d3c536b0/go.mod h1:hliV/p42l8fGbc6Y9bQ70uLwIvmJyVE5k4iMKlh8wCQ=
 github.com/envoyproxy/protoc-gen-validate v0.1.0/go.mod h1:iSmxcyjqTsJpI2R4NaDN7+kN2VEUnK/pcBlmesArF7c=
+github.com/erikgeiser/coninput v0.0.0-20211004153227-1c3628e74d0f h1:Y/CXytFA4m6baUTXGLOoWe4PQhGxaX0KpnayAqC48p4=
+github.com/erikgeiser/coninput v0.0.0-20211004153227-1c3628e74d0f/go.mod h1:vw97MGsxSvLiUE2X8qFplwetxpGLQrlU1Q9AUEIzCaM=
 github.com/fogleman/gg v1.2.1-0.20190220221249-0403632d5b90/go.mod h1:R/bRT+9gY/C5z7JzPU0zXsXHKM4/ayA+zqcVNZzPa1k=
 github.com/fogleman/gg v1.3.0/go.mod h1:R/bRT+9gY/C5z7JzPU0zXsXHKM4/ayA+zqcVNZzPa1k=
 github.com/gabriel-vasile/mimetype v1.4.3 h1:in2uUcidCuFcDKtdcBxlR0rJ1+fsokWf+uqxgUFjbI0=
@@ -148,15 +164,17 @@ github.com/ledongthuc/pdf v0.0.0-20250511090121-5959a4027728 h1:QwWKgMY28TAXaDl+
 github.com/ledongthuc/pdf v0.0.0-20250511090121-5959a4027728/go.mod h1:1fEHWurg7pvf5SG6XNE5Q8UZmOwex51Mkx3SLhrW5B4=
 github.com/leodido/go-urn v1.4.0 h1:WT9HwE9SGECu3lg4d/dIA+jxlljEa1/ffXKmRjqdmIQ=
 github.com/leodido/go-urn v1.4.0/go.mod h1:bvxc+MVxLKB4z00jd1z+Dvzr47oO32F/QSNjSBOlFxI=
+github.com/lucasb-eyer/go-colorful v1.2.0 h1:1nnpGOrhyZZuNyfu1QjKiUICQ74+3FNCN69Aj6K7nkY=
+github.com/lucasb-eyer/go-colorful v1.2.0/go.mod h1:R4dSotOR9KMtayYi1e77YzuveK+i7ruzyGqttikkLy0=
 github.com/mailru/easyjson v0.7.7 h1:UGYAvKxe3sBsEDzO8ZeWOSlIQfWFlxbzLZe7hwFURr0=
 github.com/mailru/easyjson v0.7.7/go.mod h1:xzfreul335JAWq5oZzymOObrkdz5UnU4kGfJJLY9Nlc=
 github.com/mattn/go-isatty v0.0.20 h1:xfD0iDuEKnDkl03q4limB+vH+GxLEtL/jb4xVJSWWEY=
 github.com/mattn/go-isatty v0.0.20/go.mod h1:W+V8PltTTMOvKvAeJH7IuucS94S2C6jfK/D7dTCTo3Y=
-github.com/mattn/go-pointer v0.0.1 h1:n+XhsuGeVO6MEAp7xyEukFINEa+Quek5psIR/ylA6o0=
-github.com/mattn/go-pointer v0.0.1/go.mod h1:2zXcozF6qYGgmsG+SeTZz3oAbFLdD3OWqnUbNvJZAlc=
+github.com/mattn/go-localereader v0.0.1 h1:ygSAOl7ZXTx4RdPYinUpg6W99U8jWvWi9Ye2JC/oIi4=
+github.com/mattn/go-localereader v0.0.1/go.mod h1:8fBrzywKY7BI3czFoHkuzRoWE9C+EiG4R1k4Cjx5p88=
 github.com/mattn/go-runewidth v0.0.9/go.mod h1:H031xJmbD/WCDINGzjvQ9THkh0rPKHF+m2gUSrubnMI=
-github.com/mattn/go-runewidth v0.0.14 h1:+xnbZSEeDbOIg5/mE6JF0w6n9duR1l3/WmbinWVwUuU=
-github.com/mattn/go-runewidth v0.0.14/go.mod h1:Jdepj2loyihRzMpdS35Xk/zdY8IAYHsh153qUoGf23w=
+github.com/mattn/go-runewidth v0.0.16 h1:E5ScNMtiwvlvB5paMFdw9p4kSQzbXFikJ5SQO6TULQc=
+github.com/mattn/go-runewidth v0.0.16/go.mod h1:Jdepj2loyihRzMpdS35Xk/zdY8IAYHsh153qUoGf23w=
 github.com/mattn/go-sqlite3 v1.14.24 h1:tpSp2G2KyMnnQu99ngJ47EIkWVmliIizyZBfPrBWDRM=
 github.com/mattn/go-sqlite3 v1.14.24/go.mod h1:Uh1q+B4BYcTPb+yiD3kU8Ct7aC0hY9fxUwlHK0RXw+Y=
 github.com/modern-go/concurrent v0.0.0-20180228061459-e0a39a4cb421/go.mod h1:6dJC0mAP4ikYIbvyc7fijjWJddQyLn8Ig3JB5CqoB9Q=
@@ -164,6 +182,12 @@ github.com/modern-go/concurrent v0.0.0-20180306012644-bacd9c7ef1dd h1:TRLaZ9cD/w
 github.com/modern-go/concurrent v0.0.0-20180306012644-bacd9c7ef1dd/go.mod h1:6dJC0mAP4ikYIbvyc7fijjWJddQyLn8Ig3JB5CqoB9Q=
 github.com/modern-go/reflect2 v1.0.2 h1:xBagoLtFs94CBntxluKeaWgTMpvLxC4ur3nMaC9Gz0M=
 github.com/modern-go/reflect2 v1.0.2/go.mod h1:yWuevngMOJpCy52FWWMvUC8ws7m/LJsjYzDa0/r8luk=
+github.com/muesli/ansi v0.0.0-20230316100256-276c6243b2f6 h1:ZK8zHtRHOkbHy6Mmr5D264iyp3TiX5OmNcI5cIARiQI=
+github.com/muesli/ansi v0.0.0-20230316100256-276c6243b2f6/go.mod h1:CJlz5H+gyd6CUWT45Oy4q24RdLyn7Md9Vj2/ldJBSIo=
+github.com/muesli/cancelreader v0.2.2 h1:3I4Kt4BQjOR54NavqnDogx/MIoWBFa0StPA8ELUXHmA=
+github.com/muesli/cancelreader v0.2.2/go.mod h1:3XuTXfFS2VjM+HTLZY9Ak0l6eUKfijIfMUZ4EgX0QYo=
+github.com/muesli/termenv v0.16.0 h1:S5AlUN9dENB57rsbnkPyfdGuWIlkmzJjbFf0Tf5FWUc=
+github.com/muesli/termenv v0.16.0/go.mod h1:ZRfOIKPFDYQoDFF4Olj7/QJbW60Ol/kL1pU3VfY/Cnk=
 github.com/nlpodyssey/gopickle v0.3.0 h1:BLUE5gxFLyyNOPzlXxt6GoHEMMxD0qhsE4p0CIQyoLw=
 github.com/nlpodyssey/gopickle v0.3.0/go.mod h1:f070HJ/yR+eLi5WmM1OXJEGaTpuJEUiib19olXgYha0=
 github.com/olekukonko/tablewriter v0.0.5 h1:P2Ga83D34wi1o9J6Wh1mRuqd4mF/x/lgBS7N7AbDhec=
@@ -182,8 +206,9 @@ github.com/pkg/errors v0.9.1/go.mod h1:bwawxfHBFNV+L2hUp1rHADufV3IMtnDRdf1r5NINE
 github.com/pmezard/go-difflib v1.0.0 h1:4DBwDE0NGyQoBHbLQYPwSUPoCMWR5BEzIk/f1lZbAQM=
 github.com/pmezard/go-difflib v1.0.0/go.mod h1:iKH77koFhYxTK1pcRnkKkqfTogsbg7gZNVY4sRDYZ/4=
 github.com/prometheus/client_model v0.0.0-20190812154241-14fe0d1b01d4/go.mod h1:xMI15A0UPsDsEKsMN9yxemIoYk6Tm2C1GtYGdfGttqA=
-github.com/rivo/uniseg v0.2.0 h1:S1pD9weZBuJdFmowNwbpi7BJ8TNftyUImj/0WQi72jY=
 github.com/rivo/uniseg v0.2.0/go.mod h1:J6wj4VEh+S6ZtnVlnTBMWIodfgj8LQOQFoIToxlJtxc=
+github.com/rivo/uniseg v0.4.7 h1:WUdvkW8uEhrYfLC4ZzdpI2ztxP1I582+49Oc5Mq64VQ=
+github.com/rivo/uniseg v0.4.7/go.mod h1:FN3SvrM+Zdj16jyLfmOkMNblXMcoc8DfTHruCPUcx88=
 github.com/rogpeppe/fastuuid v1.2.0/go.mod h1:jVj6XXZzXRy/MSR5jhDC/2q6DgLz+nrA6LYCDYWNEvQ=
 github.com/rogpeppe/go-internal v1.8.0 h1:FCbCCtXNOY3UtUuHUYaghJg4y7Fd14rXifAYUAtL9R8=
 github.com/rogpeppe/go-internal v1.8.0/go.mod h1:WmiCO8CzOY8rg0OYDC4/i/2WRWAB6poM+XZ2dLUbcbE=
@@ -206,39 +231,12 @@ github.com/stretchr/testify v1.7.1/go.mod h1:6Fq8oRcR53rry900zMqJjRRixrwX3KX962/
 github.com/stretchr/testify v1.8.0/go.mod h1:yNjHg4UonilssWZ8iaSj1OCr/vHnekPRkoO+kdMU+MU=
 github.com/stretchr/testify v1.8.1/go.mod h1:w2LPCIKwWwSfY2zedu0+kehJoqGctiVI29o6fzry7u4=
 github.com/stretchr/testify v1.8.4/go.mod h1:sz/lmYIOXD/1dqDmKjjqLyZ2RngseejIcXlSw2iwfAo=
+github.com/stretchr/testify v1.9.0 h1:HtqpIVDClZ4nwg75+f6Lvsy/wHu+3BoSGCbBAcpTsTg=
 github.com/stretchr/testify v1.9.0/go.mod h1:r2ic/lqez/lEtzL7wO/rwa5dbSLXVDPFyf8C91i36aY=
-github.com/stretchr/testify v1.10.0 h1:Xv5erBjTwe/5IxqUQTdXv5kgmIvbHo3QQyRwhJsOfJA=
-github.com/stretchr/testify v1.10.0/go.mod h1:r2ic/lqez/lEtzL7wO/rwa5dbSLXVDPFyf8C91i36aY=
 github.com/tkrajina/go-reflector v0.5.5 h1:gwoQFNye30Kk7NrExj8zm3zFtrGPqOkzFMLuQZg1DtQ=
 github.com/tkrajina/go-reflector v0.5.5/go.mod h1:ECbqLgccecY5kPmPmXg1MrHW585yMcDkVl6IvJe64T4=
 github.com/tkrajina/typescriptify-golang-structs v0.2.0 h1:ZedWk82egydDspGTryAatbX0/1NZDQbdiZLoCbOk4f8=
 github.com/tkrajina/typescriptify-golang-structs v0.2.0/go.mod h1:sjU00nti/PMEOZb07KljFlR+lJ+RotsC0GBQMv9EKls=
-github.com/tree-sitter/go-tree-sitter v0.25.0 h1:sx6kcg8raRFCvc9BnXglke6axya12krCJF5xJ2sftRU=
-github.com/tree-sitter/go-tree-sitter v0.25.0/go.mod h1:r77ig7BikoZhHrrsjAnv8RqGti5rtSyvDHPzgTPsUuU=
-github.com/tree-sitter/tree-sitter-c v0.23.4 h1:nBPH3FV07DzAD7p0GfNvXM+Y7pNIoPenQWBpvM++t4c=
-github.com/tree-sitter/tree-sitter-c v0.23.4/go.mod h1:MkI5dOiIpeN94LNjeCp8ljXN/953JCwAby4bClMr6bw=
-github.com/tree-sitter/tree-sitter-cpp v0.23.4 h1:LaWZsiqQKvR65yHgKmnaqA+uz6tlDJTJFCyFIeZU/8w=
-github.com/tree-sitter/tree-sitter-cpp v0.23.4/go.mod h1:doqNW64BriC7WBCQ1klf0KmJpdEvfxyXtoEybnBo6v8=
-github.com/tree-sitter/tree-sitter-embedded-template v0.23.2 h1:nFkkH6Sbe56EXLmZBqHHcamTpmz3TId97I16EnGy4rg=
-github.com/tree-sitter/tree-sitter-embedded-template v0.23.2/go.mod h1:HNPOhN0qF3hWluYLdxWs5WbzP/iE4aaRVPMsdxuzIaQ=
-github.com/tree-sitter/tree-sitter-go v0.23.4 h1:yt5KMGnTHS+86pJmLIAZMWxukr8W7Ae1STPvQUuNROA=
-github.com/tree-sitter/tree-sitter-go v0.23.4/go.mod h1:Jrx8QqYN0v7npv1fJRH1AznddllYiCMUChtVjxPK040=
-github.com/tree-sitter/tree-sitter-html v0.23.2 h1:1UYDV+Yd05GGRhVnTcbP58GkKLSHHZwVaN+lBZV11Lc=
-github.com/tree-sitter/tree-sitter-html v0.23.2/go.mod h1:gpUv/dG3Xl/eebqgeYeFMt+JLOY9cgFinb/Nw08a9og=
-github.com/tree-sitter/tree-sitter-java v0.23.5 h1:J9YeMGMwXYlKSP3K4Us8CitC6hjtMjqpeOf2GGo6tig=
-github.com/tree-sitter/tree-sitter-java v0.23.5/go.mod h1:NRKlI8+EznxA7t1Yt3xtraPk1Wzqh3GAIC46wxvc320=
-github.com/tree-sitter/tree-sitter-javascript v0.23.1 h1:1fWupaRC0ArlHJ/QJzsfQ3Ibyopw7ZfQK4xXc40Zveo=
-github.com/tree-sitter/tree-sitter-javascript v0.23.1/go.mod h1:lmGD1EJdCA+v0S1u2fFgepMg/opzSg/4pgFym2FPGAs=
-github.com/tree-sitter/tree-sitter-json v0.24.8 h1:tV5rMkihgtiOe14a9LHfDY5kzTl5GNUYe6carZBn0fQ=
-github.com/tree-sitter/tree-sitter-json v0.24.8/go.mod h1:F351KK0KGvCaYbZ5zxwx/gWWvZhIDl0eMtn+1r+gQbo=
-github.com/tree-sitter/tree-sitter-php v0.23.11 h1:iHewsLNDmznh8kgGyfWfujsZxIz1YGbSd2ZTEM0ZiP8=
-github.com/tree-sitter/tree-sitter-php v0.23.11/go.mod h1:T/kbfi+UcCywQfUNAJnGTN/fMSUjnwPXA8k4yoIks74=
-github.com/tree-sitter/tree-sitter-python v0.23.6 h1:qHnWFR5WhtMQpxBZRwiaU5Hk/29vGju6CVtmvu5Haas=
-github.com/tree-sitter/tree-sitter-python v0.23.6/go.mod h1:cpdthSy/Yoa28aJFBscFHlGiU+cnSiSh1kuDVtI8YeM=
-github.com/tree-sitter/tree-sitter-ruby v0.23.1 h1:T/NKHUA+iVbHM440hFx+lzVOzS4dV6z8Qw8ai+72bYo=
-github.com/tree-sitter/tree-sitter-ruby v0.23.1/go.mod h1:kUS4kCCQloFcdX6sdpr8p6r2rogbM6ZjTox5ZOQy8cA=
-github.com/tree-sitter/tree-sitter-rust v0.23.2 h1:6AtoooCW5GqNrRpfnvl0iUhxTAZEovEmLKDbyHlfw90=
-github.com/tree-sitter/tree-sitter-rust v0.23.2/go.mod h1:hfeGWic9BAfgTrc7Xf6FaOAguCFJRo3RBbs7QJ6D7MI=
 github.com/twitchyliquid64/golang-asm v0.15.1 h1:SU5vSMR7hnwNxj24w34ZyCi/FmDZTkS4MhqMhdFk5YI=
 github.com/twitchyliquid64/golang-asm v0.15.1/go.mod h1:a1lVb/DtPvCB8fslRZhAngC2+aY1QWCk3Cedj/Gdt08=
 github.com/ugorji/go/codec v1.2.12 h1:9LC83zGrHhuUA9l16C9AHXAqEV/2wBQ4nkvumAE65EE=
@@ -247,6 +245,8 @@ github.com/wk8/go-ordered-map/v2 v2.1.8 h1:5h/BUHu93oj4gIdvHHHGsScSTMijfx5PeYkE/
 github.com/wk8/go-ordered-map/v2 v2.1.8/go.mod h1:5nJHM5DyteebpVlHnWMV0rPz6Zp7+xBAnxjb1X5vnTw=
 github.com/x448/float16 v0.8.4 h1:qLwI1I70+NjRFUR3zs1JPUCgaCXSh3SW62uAKT1mSBM=
 github.com/x448/float16 v0.8.4/go.mod h1:14CWIYCyZA/cWjXOioeEpHeN/83MdbZDRQHoFcYsOfg=
+github.com/xo/terminfo v0.0.0-20220910002029-abceb7e1c41e h1:JVG44RsyaB9T2KIHavMF/ppJZNG9ZpyihvCd0w101no=
+github.com/xo/terminfo v0.0.0-20220910002029-abceb7e1c41e/go.mod h1:RbqR21r5mrJuqunuUZ/Dhy/avygyECGrLceyNeo4LiM=
 github.com/xtgo/set v1.0.0 h1:6BCNBRv3ORNDQ7fyoJXRv+tstJz3m1JVFQErfeZz2pY=
 github.com/xtgo/set v1.0.0/go.mod h1:d3NHzGzSa0NmB2NhFyECA+QdRp29oEn2xbT+TpeFoM8=
 github.com/yuin/goldmark v1.1.27/go.mod h1:3hX8gzYuyVAZsxl0MRgGTJEmQBFcNTphYh9decYSb74=
@@ -333,6 +333,7 @@ golang.org/x/sys v0.0.0-20210330210617-4fbd30eecc44/go.mod h1:h1NjWce9XRLGQEsW7w
 golang.org/x/sys v0.0.0-20210423082822-04245dca01da/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
 golang.org/x/sys v0.0.0-20210510120138-977fb7262007/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
 golang.org/x/sys v0.0.0-20210630005230-0f9fa26af87c/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
+golang.org/x/sys v0.0.0-20210809222454-d867a43fc93e/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
 golang.org/x/sys v0.5.0/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
 golang.org/x/sys v0.6.0/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
 golang.org/x/sys v0.37.0 h1:fdNQudmxPjkdUTPnLn5mdQv7Zwvbvpaxqs831goi9kQ=

integration/embed_test.go

@@ -73,13 +73,18 @@ func manhattanDistance[V float32 | float64](v1, v2 []V) V {
 }
 
 func TestEmbedCosineDistanceCorrelation(t *testing.T) {
-	ctx, cancel := context.WithTimeout(context.Background(), 2*time.Minute)
+	softTimeout, hardTimeout := getTimeouts(t)
+	ctx, cancel := context.WithTimeout(context.Background(), hardTimeout)
 	defer cancel()
 	client, _, cleanup := InitServerConnection(ctx, t)
 	defer cleanup()
 
+	started := time.Now()
 	for _, model := range libraryEmbedModels {
 		t.Run(model, func(t *testing.T) {
+			if time.Since(started) > softTimeout {
+				t.Skip("skipping - soft timeout exceeded")
+			}
 			testCases := []struct {
 				a string
 				b string
@@ -489,14 +494,19 @@ func TestEmbedTruncation(t *testing.T) {
 
 // TestEmbedLargeInput tests that embedding models can handle large inputs that would exceed typical batch sizes.
 func TestEmbedLargeInput(t *testing.T) {
-	ctx, cancel := context.WithTimeout(context.Background(), 3*time.Minute)
+	softTimeout, hardTimeout := getTimeouts(t)
+	ctx, cancel := context.WithTimeout(context.Background(), hardTimeout)
 	defer cancel()
 	client, _, cleanup := InitServerConnection(ctx, t)
 	defer cleanup()
 
+	started := time.Now()
 	for _, model := range libraryEmbedModels {
 		model := model
 		t.Run(model, func(t *testing.T) {
+			if time.Since(started) > softTimeout {
+				t.Skip("skipping - soft timeout exceeded")
+			}
 			mctx, mcancel := context.WithTimeout(ctx, 2*time.Minute)
 			defer mcancel()
 

integration/tools_test.go

@@ -21,9 +21,10 @@ func testPropsMap(m map[string]api.ToolProperty) *api.ToolPropertiesMap {
 }
 
 func TestAPIToolCalling(t *testing.T) {
-	initialTimeout := 60 * time.Second
-	streamTimeout := 60 * time.Second
-	ctx, cancel := context.WithTimeout(context.Background(), 10*time.Minute)
+	initialTimeout := 90 * time.Second
+	streamTimeout := 90 * time.Second
+	softTimeout, hardTimeout := getTimeouts(t)
+	ctx, cancel := context.WithTimeout(context.Background(), hardTimeout)
 	defer cancel()
 
 	client, _, cleanup := InitServerConnection(ctx, t)
@@ -47,8 +48,12 @@ func TestAPIToolCalling(t *testing.T) {
 		"granite3.3":    7,
 	}
 
+	started := time.Now()
 	for _, model := range libraryToolsModels {
 		t.Run(model, func(t *testing.T) {
+			if time.Since(started) > softTimeout {
+				t.Skip("skipping - soft timeout exceeded")
+			}
 			if v, ok := minVRAM[model]; ok {
 				skipUnderMinVRAM(t, v)
 			}

llama/README.md

@@ -14,25 +14,28 @@ make -f Makefile.sync apply-patches
 
 ### Updating Base Commit
 
-**Pin to new base commit**
+To update to a new base commit:
 
-To change the base commit, update `FETCH_HEAD` in Makefile.sync.
+1. **Update FETCH_HEAD** in `Makefile.sync` to the new commit hash.
 
-When updating to a newer base commit, the existing patches may not apply cleanly and require manual merge resolution.
+2. **Check for upstreamed patches**: Before applying, review if any patches have been merged upstream. Remove those patches from `./patches/` to avoid conflicts.
 
-Start by applying the patches. If any of the patches have conflicts, the `git am` will stop at the first failure.
+3. **Apply patches**:
+   ```shell
+   make -f Makefile.sync apply-patches
+   ```
 
-```shell
-make -f Makefile.sync apply-patches
-```
+4. **Resolve conflicts** (if any): When `git am` fails on a patch:
+   - Fix conflicts in `./vendor/`
+   - Stage the resolved files: `git -C llama/vendor add <file>`
+   - Continue: `git -C llama/vendor am --continue`
+   - Re-run: `make -f Makefile.sync apply-patches`
+   - Repeat until all patches are applied.
 
-If there are conflicts, you will see an error message. Resolve the conflicts in `./vendor/`, and continue the patch series with `git am --continue` and rerun `make -f Makefile.sync apply-patches`. Repeat until all patches are successfully applied.
-
-Once all patches are applied, commit the changes to the tracking repository.
-
-```shell
-make -f Makefile.sync format-patches sync
-```
+5. **Regenerate patches and sync**:
+   ```shell
+   make -f Makefile.sync format-patches sync
+   ```
 
 ### Generating Patches
 

llama/build-info.cpp

@@ -1,4 +1,4 @@
 int LLAMA_BUILD_NUMBER = 0;
-char const *LLAMA_COMMIT = "ec98e2002";
+char const *LLAMA_COMMIT = "a5bb8ba4c50257437630c136210396810741bbf7";
 char const *LLAMA_COMPILER = "";
 char const *LLAMA_BUILD_TARGET = "";

llama/llama.cpp/common/common.cpp

@@ -251,7 +251,7 @@ bool set_process_priority(enum ggml_sched_priority prio) {
         case GGML_SCHED_PRIO_REALTIME: p = -20; break;
     }
 
-    if (!setpriority(PRIO_PROCESS, 0, p)) {
+    if (setpriority(PRIO_PROCESS, 0, p) != 0) {
         LOG_WRN("failed to set process priority %d : %s (%d)\n", prio, strerror(errno), errno);
         return false;
     }
@@ -1078,12 +1078,15 @@ struct common_init_result::impl {
     impl() = default;
     ~impl() = default;
 
+    // note: the order in which model, context, etc. are declared matters because their destructors will be called bottom-to-top
+
     llama_model_ptr   model;
     llama_context_ptr context;
 
     std::vector<llama_adapter_lora_ptr> lora;
 
     std::vector<common_sampler_ptr> samplers;
+    std::vector<llama_sampler_seq_config> samplers_seq_config;
 };
 
 common_init_result::common_init_result(common_params & params) :
@@ -1092,9 +1095,9 @@ common_init_result::common_init_result(common_params & params) :
     auto cparams = common_context_params_to_llama(params);
 
     if (params.fit_params) {
-        LOG_INF("%s: fitting params to device memory, to report bugs during this step use -fit off (or --verbose if you can't)\n", __func__);
+        LOG_INF("%s: fitting params to device memory, for bugs during this step try to reproduce them with -fit off, or provide --verbose logs if the bug only occurs with -fit on\n", __func__);
         llama_params_fit(params.model.path.c_str(), &mparams, &cparams,
-            params.tensor_split, params.tensor_buft_overrides.data(), params.fit_params_target, params.fit_params_min_ctx,
+            params.tensor_split, params.tensor_buft_overrides.data(), params.fit_params_target.data(), params.fit_params_min_ctx,
             params.verbosity >= 4 ? GGML_LOG_LEVEL_DEBUG : GGML_LOG_LEVEL_ERROR);
     }
 
@@ -1107,6 +1110,25 @@ common_init_result::common_init_result(common_params & params) :
 
     const llama_vocab * vocab = llama_model_get_vocab(model);
 
+    // load and optionally apply lora adapters (must be loaded before context creation)
+    for (auto & la : params.lora_adapters) {
+        llama_adapter_lora_ptr lora;
+        lora.reset(llama_adapter_lora_init(model, la.path.c_str()));
+        if (lora == nullptr) {
+            LOG_ERR("%s: failed to load lora adapter '%s'\n", __func__, la.path.c_str());
+            pimpl->model.reset(model);
+            return;
+        }
+
+        char buf[1024];
+        la.ptr = lora.get();
+        llama_adapter_meta_val_str(la.ptr, "adapter.lora.task_name", buf, sizeof(buf));
+        la.task_name = buf;
+        llama_adapter_meta_val_str(la.ptr, "adapter.lora.prompt_prefix", buf, sizeof(buf));
+        la.prompt_prefix = buf;
+        pimpl->lora.emplace_back(std::move(lora)); // copy to list of loaded adapters
+    }
+
     // updates params.sampling
     // TODO: fix naming
     common_init_sampler_from_model(model, params.sampling);
@@ -1141,10 +1163,18 @@ common_init_result::common_init_result(common_params & params) :
     //    params.sampling.dry_penalty_last_n = llama_n_ctx(lctx);
     //}
 
+    // init the backend samplers as part of the context creation
     pimpl->samplers.resize(cparams.n_seq_max);
+    pimpl->samplers_seq_config.resize(cparams.n_seq_max);
 
     for (int i = 0; i < (int) cparams.n_seq_max; ++i) {
         pimpl->samplers[i].reset(common_sampler_init(model, params.sampling));
+        pimpl->samplers_seq_config[i] = { i, common_sampler_get(pimpl->samplers[i].get()) };
+    }
+
+    if (params.sampling.backend_sampling) {
+        cparams.samplers   = pimpl->samplers_seq_config.data();
+        cparams.n_samplers = pimpl->samplers_seq_config.size();
     }
 
     llama_context * lctx = llama_init_from_model(model, cparams);
@@ -1168,6 +1198,12 @@ common_sampler * common_init_result::sampler(llama_seq_id seq_id) {
     return pimpl->samplers[seq_id].get();
 }
 
+void common_init_result::reset_samplers() {
+    for (int i = 0; i < (int) pimpl->samplers.size(); ++i) {
+        llama_sampler_reset(common_sampler_get(pimpl->samplers[i].get()));
+    }
+}
+
 std::vector<llama_adapter_lora_ptr> & common_init_result::lora() {
     return pimpl->lora;
 }
@@ -1243,24 +1279,6 @@ common_init_result_ptr common_init_from_params(common_params & params) {
         }
     }
 
-    // load and optionally apply lora adapters
-    for (auto & la : params.lora_adapters) {
-        llama_adapter_lora_ptr lora;
-        lora.reset(llama_adapter_lora_init(model, la.path.c_str()));
-        if (lora == nullptr) {
-            LOG_ERR("%s: failed to apply lora adapter '%s'\n", __func__, la.path.c_str());
-            return res;
-        }
-
-        char buf[1024];
-        la.ptr = lora.get();
-        llama_adapter_meta_val_str(la.ptr, "adapter.lora.task_name", buf, sizeof(buf));
-        la.task_name = buf;
-        llama_adapter_meta_val_str(la.ptr, "adapter.lora.prompt_prefix", buf, sizeof(buf));
-        la.prompt_prefix = buf;
-        res->lora().emplace_back(std::move(lora)); // copy to list of loaded adapters
-    }
-
     if (!params.lora_init_without_apply) {
         common_set_adapter_lora(lctx, params.lora_adapters);
     }
@@ -1301,6 +1319,9 @@ common_init_result_ptr common_init_from_params(common_params & params) {
         llama_synchronize(lctx);
         llama_perf_context_reset(lctx);
         llama_set_warmup(lctx, false);
+
+        // reset samplers to reset RNG state after warmup to the seeded state
+        res->reset_samplers();
     }
 
     return res;
@@ -1339,14 +1360,12 @@ struct llama_model_params common_model_params_to_llama(common_params & params) {
         mparams.devices = params.devices.data();
     }
 
-    if (params.n_gpu_layers != -1) {
-        mparams.n_gpu_layers = params.n_gpu_layers;
-    }
-
+    mparams.n_gpu_layers    = params.n_gpu_layers;
     mparams.main_gpu        = params.main_gpu;
     mparams.split_mode      = params.split_mode;
     mparams.tensor_split    = params.tensor_split;
     mparams.use_mmap        = params.use_mmap;
+    mparams.use_direct_io   = params.use_direct_io;
     mparams.use_mlock       = params.use_mlock;
     mparams.check_tensors   = params.check_tensors;
     mparams.use_extra_bufts = !params.no_extra_bufts;

llama/llama.cpp/common/common.h

@@ -57,6 +57,8 @@ extern const char * LLAMA_COMMIT;
 extern const char * LLAMA_COMPILER;
 extern const char * LLAMA_BUILD_TARGET;
 
+const static std::string build_info("b" + std::to_string(LLAMA_BUILD_NUMBER) + "-" + LLAMA_COMMIT);
+
 struct common_control_vector_load_info;
 
 //
@@ -80,6 +82,8 @@ int32_t cpu_get_num_math();
 //
 
 enum llama_example {
+    LLAMA_EXAMPLE_BATCHED,
+    LLAMA_EXAMPLE_DEBUG,
     LLAMA_EXAMPLE_COMMON,
     LLAMA_EXAMPLE_SPECULATIVE,
     LLAMA_EXAMPLE_COMPLETION,
@@ -117,6 +121,7 @@ enum common_sampler_type {
     COMMON_SAMPLER_TYPE_INFILL      = 9,
     COMMON_SAMPLER_TYPE_PENALTIES   = 10,
     COMMON_SAMPLER_TYPE_TOP_N_SIGMA = 11,
+    COMMON_SAMPLER_TYPE_ADAPTIVE_P  = 12,
 };
 
 // dimensionality reduction methods, used by cvector-generator
@@ -164,32 +169,34 @@ enum common_params_sampling_config : uint64_t {
 struct common_params_sampling {
     uint32_t seed = LLAMA_DEFAULT_SEED; // the seed used to initialize llama_sampler
 
-    int32_t n_prev             = 64;    // number of previous tokens to remember
-    int32_t n_probs            = 0;     // if greater than 0, output the probabilities of top n_probs tokens.
-    int32_t min_keep           = 0;     // 0 = disabled, otherwise samplers should return at least min_keep tokens
-    int32_t top_k              = 40;    // <= 0 to use vocab size
-    float   top_p              = 0.95f; // 1.0 = disabled
-    float   min_p              = 0.05f; // 0.0 = disabled
-    float   xtc_probability    = 0.00f; // 0.0 = disabled
-    float   xtc_threshold      = 0.10f; // > 0.5 disables XTC
-    float   typ_p              = 1.00f; // typical_p, 1.0 = disabled
-    float   temp               = 0.80f; // <= 0.0 to sample greedily, 0.0 to not output probabilities
-    float   dynatemp_range     = 0.00f; // 0.0 = disabled
-    float   dynatemp_exponent  = 1.00f; // controls how entropy maps to temperature in dynamic temperature sampler
-    int32_t penalty_last_n     = 64;    // last n tokens to penalize (0 = disable penalty, -1 = context size)
-    float   penalty_repeat     = 1.00f; // 1.0 = disabled
-    float   penalty_freq       = 0.00f; // 0.0 = disabled
-    float   penalty_present    = 0.00f; // 0.0 = disabled
-    float   dry_multiplier     = 0.0f;  // 0.0 = disabled;      DRY repetition penalty for tokens extending repetition:
-    float   dry_base           = 1.75f; // 0.0 = disabled;      multiplier * base ^ (length of sequence before token - allowed length)
-    int32_t dry_allowed_length = 2;     // tokens extending repetitions beyond this receive penalty
-    int32_t dry_penalty_last_n = -1;    // how many tokens to scan for repetitions (0 = disable penalty, -1 = context size)
-    int32_t mirostat           = 0;     // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
-    float   top_n_sigma        = -1.00f;// -1.0 = disabled
-    float   mirostat_tau       = 5.00f; // target entropy
-    float   mirostat_eta       = 0.10f; // learning rate
+    int32_t n_prev             = 64;     // number of previous tokens to remember
+    int32_t n_probs            = 0;      // if greater than 0, output the probabilities of top n_probs tokens.
+    int32_t min_keep           = 0;      // 0 = disabled, otherwise samplers should return at least min_keep tokens
+    int32_t top_k              = 40;     // <= 0 to use vocab size
+    float   top_p              = 0.95f;  // 1.0 = disabled
+    float   min_p              = 0.05f;  // 0.0 = disabled
+    float   xtc_probability    = 0.00f;  // 0.0 = disabled
+    float   xtc_threshold      = 0.10f;  // > 0.5 disables XTC
+    float   typ_p              = 1.00f;  // typical_p, 1.0 = disabled
+    float   temp               = 0.80f;  // <= 0.0 to sample greedily, 0.0 to not output probabilities
+    float   dynatemp_range     = 0.00f;  // 0.0 = disabled
+    float   dynatemp_exponent  = 1.00f;  // controls how entropy maps to temperature in dynamic temperature sampler
+    int32_t penalty_last_n     = 64;     // last n tokens to penalize (0 = disable penalty, -1 = context size)
+    float   penalty_repeat     = 1.00f;  // 1.0 = disabled
+    float   penalty_freq       = 0.00f;  // 0.0 = disabled
+    float   penalty_present    = 0.00f;  // 0.0 = disabled
+    float   dry_multiplier     = 0.0f;   // 0.0 = disabled;      DRY repetition penalty for tokens extending repetition:
+    float   dry_base           = 1.75f;  // 0.0 = disabled;      multiplier * base ^ (length of sequence before token - allowed length)
+    int32_t dry_allowed_length = 2;      // tokens extending repetitions beyond this receive penalty
+    int32_t dry_penalty_last_n = -1;     // how many tokens to scan for repetitions (0 = disable penalty, -1 = context size)
+    float   adaptive_target    = -1.0f;  // select tokens near this probability (valid range 0.0 to 1.0; negative = disabled)
+    float   adaptive_decay     = 0.90f;  // EMA decay for adaptation; history ≈ 1/(1-decay) tokens (0.0 - 0.99)
+    int32_t mirostat           = 0;      // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
+    float   top_n_sigma        = -1.00f; // -1.0 = disabled
+    float   mirostat_tau       = 5.00f;  // target entropy
+    float   mirostat_eta       = 0.10f;  // learning rate
     bool    ignore_eos         = false;
-    bool    no_perf            = false; // disable performance metrics
+    bool    no_perf            = false;  // disable performance metrics
     bool    timing_per_token   = false;
 
     uint64_t user_sampling_config = 0; // bitfield to track user-specified samplers
@@ -216,6 +223,8 @@ struct common_params_sampling {
     std::vector<llama_logit_bias> logit_bias;     // logit biases to apply
     std::vector<llama_logit_bias> logit_bias_eog; // pre-calculated logit biases for EOG tokens
 
+    bool backend_sampling = false;
+
     bool has_logit_bias() const {
         return !logit_bias.empty();
     }
@@ -277,6 +286,7 @@ struct common_params_diffusion {
 };
 
 // reasoning API response format (not to be confused as chat template's reasoning format)
+// only used by server
 enum common_reasoning_format {
     COMMON_REASONING_FORMAT_NONE,
     COMMON_REASONING_FORMAT_AUTO,            // Same as deepseek, using `message.reasoning_content`
@@ -329,12 +339,14 @@ struct common_params {
     // offload params
     std::vector<ggml_backend_dev_t> devices; // devices to use for offloading
 
-    int32_t n_gpu_layers       = -1;               // number of layers to store in VRAM (-1 - use default)
-    int32_t main_gpu           = 0;                // the GPU that is used for scratch and small tensors
-    float   tensor_split[128]  = {0};              // how split tensors should be distributed across GPUs
-    bool    fit_params         = true;             // whether to fit unset model/context parameters to free device memory
-    size_t  fit_params_target  = 1024 * 1024*1024; // margin per device in bytes for fitting parameters to free memory
-    int32_t fit_params_min_ctx = 4096;             // minimum context size to set when trying to reduce memory use
+    int32_t n_gpu_layers       = -1;   // number of layers to store in VRAM, -1 is auto, <= -2 is all
+    int32_t main_gpu           = 0;    // the GPU that is used for scratch and small tensors
+    float   tensor_split[128]  = {0};  // how split tensors should be distributed across GPUs
+    bool    fit_params         = true; // whether to fit unset model/context parameters to free device memory
+    int32_t fit_params_min_ctx = 4096; // minimum context size to set when trying to reduce memory use
+
+    // margin per device in bytes for fitting parameters to free memory:
+    std::vector<size_t> fit_params_target = std::vector<size_t>(llama_max_devices(), 1024 * 1024*1024);
 
     enum llama_split_mode split_mode = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs
 
@@ -370,6 +382,11 @@ struct common_params {
     std::string lookup_cache_dynamic = ""; // path of dynamic ngram cache file for lookup decoding          // NOLINT
     std::string logits_file          = ""; // file for saving *all* logits                                  // NOLINT
 
+    // llama-debug specific options
+    std::string logits_output_dir = "data"; // directory for saving logits output files                     // NOLINT
+    bool        save_logits       = false;  // whether to save logits to files                              // NOLINT
+    std::vector<std::string> tensor_filter; // filter tensor names for debug output (regex)                 // NOLINT
+
     std::vector<std::string> in_files;   // all input files
     std::vector<std::string> antiprompt; // strings upon which more user input is prompted (a.k.a. reverse prompts)
     std::vector<llama_model_kv_override> kv_overrides;
@@ -420,7 +437,8 @@ struct common_params {
     bool kv_unified        = false; // enable unified KV cache
 
     bool input_prefix_bos  = false; // prefix BOS to user inputs, preceding input_prefix
-    bool use_mmap          = true;  // use mmap for faster loads
+    bool use_mmap          = true;  // enable mmap to use filesystem cache
+    bool use_direct_io     = true;  // read from disk without buffering for faster model loading
     bool use_mlock         = false; // use mlock to keep model in memory
     bool verbose_prompt    = false; // print prompt tokens before generation
     bool display_prompt    = true;  // print prompt before generation
@@ -464,6 +482,7 @@ struct common_params {
     int32_t timeout_write     = timeout_read; // http write timeout in seconds
     int32_t n_threads_http    = -1;           // number of threads to process HTTP requests (TODO: support threadpool)
     int32_t n_cache_reuse     = 0;            // min chunk size to reuse from the cache via KV shifting
+    bool    cache_prompt      = true;         // whether to enable prompt caching
     int32_t n_ctx_checkpoints = 8;            // max number of context checkpoints per slot
     int32_t cache_ram_mib     = 8192;         // -1 = no limit, 0 - disable, 1 = 1 MiB, etc.
 
@@ -475,7 +494,8 @@ struct common_params {
     bool enable_chat_template = true;
     common_reasoning_format reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK;
     int reasoning_budget = -1;
-    bool prefill_assistant = true;                                                                          // if true, any trailing assistant message will be prefilled into the response
+    bool prefill_assistant = true; // if true, any trailing assistant message will be prefilled into the response
+    int sleep_idle_seconds = -1;   // if >0, server will sleep after this many seconds of idle time
 
     std::vector<std::string> api_keys;
 
@@ -484,8 +504,11 @@ struct common_params {
 
     std::map<std::string, std::string> default_template_kwargs;
 
+    // webui configs
+    bool webui = true;
+    std::string webui_config_json;
+
     // "advanced" endpoints are disabled by default for better security
-    bool webui            = true;
     bool endpoint_slots   = true;
     bool endpoint_props   = false; // only control POST requests, not GET
     bool endpoint_metrics = false;
@@ -685,7 +708,9 @@ struct common_init_result {
 
     llama_model * model();
     llama_context * context();
+
     common_sampler * sampler(llama_seq_id seq_id);
+    void reset_samplers();
 
     std::vector<llama_adapter_lora_ptr> & lora();
 

llama/llama.cpp/common/sampling.cpp

@@ -104,10 +104,9 @@ struct ring_buffer {
 struct common_sampler {
     common_params_sampling params;
 
+    struct llama_sampler * grmr;
     struct llama_sampler * chain;
 
-    bool grammar;
-
     ring_buffer<llama_token> prev;
 
     std::vector<llama_token_data> cur;
@@ -121,17 +120,34 @@ struct common_sampler {
     }
 
     void set_logits(struct llama_context * ctx, int idx) {
-        const auto * logits = llama_get_logits_ith(ctx, idx);
+        const float *       sampled_probs  = llama_get_sampled_probs_ith     (ctx, idx);
+        const float *       sampled_logits = llama_get_sampled_logits_ith    (ctx, idx);
+        const llama_token * sampled_ids    = llama_get_sampled_candidates_ith(ctx, idx);
 
         const llama_model * model = llama_get_model(ctx);
         const llama_vocab * vocab = llama_model_get_vocab(model);
 
         const int n_vocab = llama_vocab_n_tokens(vocab);
 
-        cur.resize(n_vocab);
-
-        for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
-            cur[token_id] = llama_token_data{token_id, logits[token_id], 0.0f};
+        if (sampled_probs) {
+            const uint32_t sampled_probs_count = llama_get_sampled_probs_count_ith(ctx, idx);
+            cur.resize(sampled_probs_count);
+            for (uint32_t i = 0; i < sampled_probs_count; ++i) {
+                cur[i] = llama_token_data{sampled_ids[i], sampled_logits[i], sampled_probs[i]};
+            }
+        } else if (sampled_logits) {
+            const uint32_t sampled_logits_count = llama_get_sampled_logits_count_ith(ctx, idx);
+            cur.resize(sampled_logits_count);
+            for (uint32_t i = 0; i < sampled_logits_count; i++) {
+                cur[i] = llama_token_data{sampled_ids[i], sampled_logits[i], 0.0f};
+            }
+        } else {
+            const auto * logits = llama_get_logits_ith(ctx, idx);
+            GGML_ASSERT(logits != nullptr);
+            cur.resize(n_vocab);
+            for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
+                cur[token_id] = llama_token_data{token_id, logits[token_id], 0.0f};
+            }
         }
 
         cur_p = { cur.data(), cur.size(), -1, false };
@@ -151,54 +167,59 @@ std::string common_params_sampling::print() const {
             "\trepeat_last_n = %d, repeat_penalty = %.3f, frequency_penalty = %.3f, presence_penalty = %.3f\n"
             "\tdry_multiplier = %.3f, dry_base = %.3f, dry_allowed_length = %d, dry_penalty_last_n = %d\n"
             "\ttop_k = %d, top_p = %.3f, min_p = %.3f, xtc_probability = %.3f, xtc_threshold = %.3f, typical_p = %.3f, top_n_sigma = %.3f, temp = %.3f\n"
-            "\tmirostat = %d, mirostat_lr = %.3f, mirostat_ent = %.3f",
+            "\tmirostat = %d, mirostat_lr = %.3f, mirostat_ent = %.3f, adaptive_target = %.3f, adaptive_decay = %.3f",
             penalty_last_n, penalty_repeat, penalty_freq, penalty_present,
             dry_multiplier, dry_base, dry_allowed_length, dry_penalty_last_n,
             top_k, top_p, min_p, xtc_probability, xtc_threshold, typ_p, top_n_sigma, temp,
-            mirostat, mirostat_eta, mirostat_tau);
+            mirostat, mirostat_eta, mirostat_tau, adaptive_target, adaptive_decay);
 
     return std::string(result);
 }
 
-struct common_sampler * common_sampler_init(const struct llama_model * model, const struct common_params_sampling & params) {
+struct common_sampler * common_sampler_init(const struct llama_model * model, struct common_params_sampling & params) {
     const llama_vocab * vocab = llama_model_get_vocab(model);
 
     llama_sampler_chain_params lparams = llama_sampler_chain_default_params();
 
     lparams.no_perf = params.no_perf;
 
+    llama_sampler * grmr = nullptr;
     llama_sampler * chain = llama_sampler_chain_init(lparams);
 
-    bool grammar = false;
     std::vector<llama_sampler *> samplers;
 
     if (params.grammar.compare(0, 11, "%llguidance") == 0) {
 #ifdef LLAMA_USE_LLGUIDANCE
-        samplers.push_back(llama_sampler_init_llg(vocab, "lark", params.grammar.c_str()));
-        grammar = true;
+        grmr = llama_sampler_init_llg(vocab, "lark", params.grammar.c_str());
 #else
         GGML_ABORT("llguidance (cmake -DLLAMA_LLGUIDANCE=ON) is not enabled");
 #endif // LLAMA_USE_LLGUIDANCE
     } else {
         std::vector<std::string> trigger_patterns;
-        std::vector<std::string> patterns_anywhere;
         std::vector<llama_token> trigger_tokens;
         for (const auto & trigger : params.grammar_triggers) {
             switch (trigger.type) {
                 case COMMON_GRAMMAR_TRIGGER_TYPE_WORD:
                 {
                     const auto & word = trigger.value;
-                    patterns_anywhere.push_back(regex_escape(word));
+                    trigger_patterns.push_back(regex_escape(word));
                     break;
                 }
                 case COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN:
                 {
-                    patterns_anywhere.push_back(trigger.value);
+                    trigger_patterns.push_back(trigger.value);
                     break;
                 }
                 case COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_FULL:
                 {
-                    trigger_patterns.push_back(trigger.value);
+                    const auto & pattern = trigger.value;
+                    std::string anchored = "^$";
+                    if (!pattern.empty()) {
+                        anchored = (pattern.front() != '^' ? "^" : "")
+                            + pattern
+                            + (pattern.back() != '$' ? "$" : "");
+                    }
+                    trigger_patterns.push_back(anchored);
                     break;
                 }
                 case COMMON_GRAMMAR_TRIGGER_TYPE_TOKEN:
@@ -212,10 +233,6 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
             }
         }
 
-        if (!patterns_anywhere.empty()) {
-            trigger_patterns.push_back("^[\\s\\S]*?(" + string_join(patterns_anywhere, "|") + ")[\\s\\S]*");
-        }
-
         std::vector<const char *> trigger_patterns_c;
         trigger_patterns_c.reserve(trigger_patterns.size());
         for (const auto & regex : trigger_patterns) {
@@ -224,15 +241,12 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
 
         if (!params.grammar.empty()) {
              if (params.grammar_lazy) {
-                 samplers.push_back(
-                         llama_sampler_init_grammar_lazy_patterns(vocab, params.grammar.c_str(), "root",
-                             trigger_patterns_c.data(), trigger_patterns_c.size(),
-                             trigger_tokens.data(),     trigger_tokens.size()));
+                 grmr = llama_sampler_init_grammar_lazy_patterns(vocab, params.grammar.c_str(), "root",
+                         trigger_patterns_c.data(), trigger_patterns_c.size(),
+                         trigger_tokens.data(), trigger_tokens.size());
              } else {
-                 samplers.push_back(llama_sampler_init_grammar(vocab, params.grammar.c_str(), "root"));
+                 grmr = llama_sampler_init_grammar(vocab, params.grammar.c_str(), "root");
              }
-
-             grammar = true;
         }
     }
 
@@ -241,6 +255,9 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
     }
 
     if (params.mirostat == 0) {
+
+        bool use_adaptive_p = false; // see below
+
         for (const auto & cnstr : params.samplers) {
             switch (cnstr) {
                 case COMMON_SAMPLER_TYPE_DRY:
@@ -250,43 +267,54 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
                         for (const auto & str : params.dry_sequence_breakers) {
                             c_breakers.push_back(str.c_str());
                         }
-
-                        samplers.push_back(llama_sampler_init_dry    (vocab, llama_model_n_ctx_train(model), params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size()));
+                        samplers.push_back(llama_sampler_init_dry(vocab, llama_model_n_ctx_train(model), params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size()));
                     }
                     break;
                 case COMMON_SAMPLER_TYPE_TOP_K:
-                    samplers.push_back(llama_sampler_init_top_k      (params.top_k));
+                    samplers.push_back(llama_sampler_init_top_k(params.top_k));
                     break;
                 case COMMON_SAMPLER_TYPE_TOP_P:
-                    samplers.push_back(llama_sampler_init_top_p      (params.top_p, params.min_keep));
+                    samplers.push_back(llama_sampler_init_top_p(params.top_p, params.min_keep));
                     break;
                 case COMMON_SAMPLER_TYPE_TOP_N_SIGMA:
                     samplers.push_back(llama_sampler_init_top_n_sigma(params.top_n_sigma));
                     break;
                 case COMMON_SAMPLER_TYPE_MIN_P:
-                    samplers.push_back(llama_sampler_init_min_p      (params.min_p, params.min_keep));
+                    samplers.push_back(llama_sampler_init_min_p(params.min_p, params.min_keep));
                     break;
                 case COMMON_SAMPLER_TYPE_XTC:
-                    samplers.push_back(llama_sampler_init_xtc        (params.xtc_probability, params.xtc_threshold, params.min_keep, params.seed));
+                    samplers.push_back(llama_sampler_init_xtc(params.xtc_probability, params.xtc_threshold, params.min_keep, params.seed));
                     break;
                 case COMMON_SAMPLER_TYPE_TYPICAL_P:
-                    samplers.push_back(llama_sampler_init_typical    (params.typ_p, params.min_keep));
+                    samplers.push_back(llama_sampler_init_typical(params.typ_p, params.min_keep));
                     break;
                 case COMMON_SAMPLER_TYPE_TEMPERATURE:
-                    samplers.push_back(llama_sampler_init_temp_ext   (params.temp, params.dynatemp_range, params.dynatemp_exponent));
+                    samplers.push_back(llama_sampler_init_temp_ext(params.temp, params.dynatemp_range, params.dynatemp_exponent));
                     break;
                 case COMMON_SAMPLER_TYPE_INFILL:
-                    samplers.push_back(llama_sampler_init_infill     (vocab));
+                    samplers.push_back(llama_sampler_init_infill(vocab));
                     break;
                 case COMMON_SAMPLER_TYPE_PENALTIES:
-                    samplers.push_back(llama_sampler_init_penalties  (params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present));
+                    samplers.push_back(llama_sampler_init_penalties(params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present));
+                    break;
+                case COMMON_SAMPLER_TYPE_ADAPTIVE_P:
+                    // the `adaptive-p` sampler is like `dist` and `mirostat` in that it selects
+                    // a single token, so we will add `dist` at the end of the chain by default,
+                    // unless the user specifically included `adaptive-p`. we set this flag here
+                    // so we know to add the sampler at the very end.
+                    use_adaptive_p = true;
                     break;
                 default:
                     GGML_ASSERT(false && "unknown sampler type");
             }
         }
-
-        samplers.push_back(llama_sampler_init_dist(params.seed));
+        if (use_adaptive_p) {
+            // only if user explicitly included adaptive-p sampler
+            samplers.push_back(llama_sampler_init_adaptive_p(params.adaptive_target, params.adaptive_decay, params.seed));
+        } else {
+            // default: sample from distribution
+            samplers.push_back(llama_sampler_init_dist(params.seed));
+        }
     } else if (params.mirostat == 1) {
         samplers.push_back(llama_sampler_init_temp(params.temp));
         samplers.push_back(llama_sampler_init_mirostat(llama_vocab_n_tokens(vocab), params.seed, params.mirostat_tau, params.mirostat_eta, 100));
@@ -301,10 +329,16 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
         llama_sampler_chain_add(chain, smpl);
     }
 
+    if (grmr && params.backend_sampling) {
+        LOG_WRN("%s: backend sampling is not compatible with grammar, disabling\n", __func__);
+
+        params.backend_sampling = false;
+    }
+
     auto * result = new common_sampler {
         /* .params  = */ params,
+        /* .grmr    = */ grmr,
         /* .chain   = */ chain,
-        /* .grammar = */ grammar,
         /* .prev    = */ ring_buffer<llama_token>(std::max(32, params.n_prev)),
         /* .cur     = */ {},
         /* .cur_p   = */ {},
@@ -314,47 +348,45 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
 }
 
 void common_sampler_free(struct common_sampler * gsmpl) {
-    if (gsmpl) {
-        llama_sampler_free(gsmpl->chain);
-
-        delete gsmpl;
+    if (!gsmpl) {
+        return;
     }
+
+    llama_sampler_free(gsmpl->grmr);
+    llama_sampler_free(gsmpl->chain);
+
+    delete gsmpl;
 }
 
 void common_sampler_accept(struct common_sampler * gsmpl, llama_token token, bool accept_grammar) {
+    if (!gsmpl) {
+        return;
+    }
+
     const auto tm = gsmpl->tm();
 
-    if (gsmpl->grammar) {
-        const int n_smpl = llama_sampler_chain_n(gsmpl->chain);
-
-        for (int i = 0; i < n_smpl; i++) {
-            auto * smpl = llama_sampler_chain_get(gsmpl->chain, i);
-
-            // the grammar sampler is always the first one
-            if (i == 0) {
-                if (accept_grammar) {
-                    llama_sampler_accept(smpl, token);
-                }
-            } else {
-                llama_sampler_accept(smpl, token);
-            }
-        }
-    } else {
-        llama_sampler_accept(gsmpl->chain, token);
+    if (gsmpl->grmr && accept_grammar) {
+        llama_sampler_accept(gsmpl->grmr, token);
     }
 
+    llama_sampler_accept(gsmpl->chain, token);
+
     gsmpl->prev.push_back(token);
 }
 
 void common_sampler_reset(struct common_sampler * gsmpl) {
+    if (!gsmpl) {
+        return;
+    }
+
     gsmpl->reset();
 }
 
 struct common_sampler * common_sampler_clone(common_sampler * gsmpl) {
     return new common_sampler {
         /* .params  = */ gsmpl->params,
+        /* .grmr    = */ llama_sampler_clone(gsmpl->grmr),
         /* .chain   = */ llama_sampler_clone(gsmpl->chain),
-        /* .grammar = */ gsmpl->grammar,
         /* .prev    = */ gsmpl->prev,
         /* .cur     = */ gsmpl->cur,
         /* .cur_p   = */ gsmpl->cur_p,
@@ -407,10 +439,14 @@ void common_perf_print(const struct llama_context * ctx, const struct common_sam
 }
 
 struct llama_sampler * common_sampler_get(const struct common_sampler * gsmpl) {
+    if (!gsmpl) {
+        return nullptr;
+    }
+
     return gsmpl->chain;
 }
 
-llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_context * ctx, int idx) {
+llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_context * ctx, int idx, bool grammar_first) {
     llama_synchronize(ctx);
 
     // start measuring sampling time after the llama_context synchronization in order to not measure any ongoing async operations
@@ -418,11 +454,61 @@ llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_co
 
     llama_token id = LLAMA_TOKEN_NULL;
 
+    auto & grmr  = gsmpl->grmr;
     auto & chain = gsmpl->chain;
     auto & cur_p = gsmpl->cur_p; // initialized by set_logits
 
+    // Check if a backend sampler has already sampled a token in which case we
+    // return that token id directly.
+    {
+        id = llama_get_sampled_token_ith(ctx, idx);
+
+        if (id != LLAMA_TOKEN_NULL) {
+            LOG_DBG("%s: Backend sampler selected token: '%d'. Will not run any CPU samplers\n", __func__, id);
+
+            GGML_ASSERT(!gsmpl->grmr && "using grammar in combination with backend sampling is not supported");
+
+            // TODO: simplify
+            gsmpl->cur.resize(1);
+            gsmpl->cur[0] = { id, 0.0f, 1.0f };
+            cur_p = { gsmpl->cur.data(), gsmpl->cur.size(), 0, true };
+
+            return id;
+        }
+    }
+
     gsmpl->set_logits(ctx, idx);
 
+    if (grammar_first) {
+        llama_sampler_apply(grmr, &cur_p);
+    }
+
+    llama_sampler_apply(chain, &cur_p);
+
+    id = cur_p.data[cur_p.selected].id;
+
+    if (grammar_first) {
+        return id;
+    }
+
+    // check if it the sampled token fits the grammar (grammar-based rejection sampling)
+    {
+        llama_token_data       single_token_data       = { id, 1.0f, 0.0f };
+        llama_token_data_array single_token_data_array = { &single_token_data, 1, -1, false };
+
+        llama_sampler_apply(grmr, &single_token_data_array);
+
+        const bool is_valid = single_token_data_array.data[0].logit != -INFINITY;
+        if (is_valid) {
+            return id;
+        }
+    }
+
+    // resampling:
+    // if the token is not valid, sample again, but first apply the grammar sampler and then the sampling chain
+    gsmpl->set_logits(ctx, idx);
+
+    llama_sampler_apply(grmr,  &cur_p);
     llama_sampler_apply(chain, &cur_p);
 
     GGML_ASSERT(cur_p.selected != -1 && "no selected token during sampling - check your sampling configuration");
@@ -432,7 +518,7 @@ llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_co
     return id;
 }
 
-std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const std::vector<int> & idxs, const llama_tokens & draft) {
+std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const std::vector<int> & idxs, const llama_tokens & draft, bool grammar_first) {
     GGML_ASSERT(idxs.size() == draft.size() + 1 && "idxs.size() must be draft.size() + 1");
 
     std::vector<llama_token> result;
@@ -440,7 +526,7 @@ std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sample
 
     size_t i = 0;
     for (; i < draft.size(); i++) {
-        const llama_token id = common_sampler_sample(gsmpl, ctx, idxs[i]);
+        const llama_token id = common_sampler_sample(gsmpl, ctx, idxs[i], grammar_first);
 
         common_sampler_accept(gsmpl, id, true);
 
@@ -452,7 +538,7 @@ std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sample
     }
 
     if (i == draft.size()) {
-        const llama_token id = common_sampler_sample(gsmpl, ctx, idxs[i]);
+        const llama_token id = common_sampler_sample(gsmpl, ctx, idxs[i], grammar_first);
 
         common_sampler_accept(gsmpl, id, true);
 
@@ -462,13 +548,13 @@ std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sample
     return result;
 }
 
-std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const llama_tokens & draft) {
+std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const llama_tokens & draft, bool grammar_first) {
     std::vector<int> idxs(draft.size() + 1);
     for (size_t i = 0; i < idxs.size(); ++i) {
         idxs[i] = i;
     }
 
-    return common_sampler_sample_and_accept_n(gsmpl, ctx, idxs, draft);
+    return common_sampler_sample_and_accept_n(gsmpl, ctx, idxs, draft, grammar_first);
 }
 
 uint32_t common_sampler_get_seed(const struct common_sampler * gsmpl) {
@@ -553,6 +639,7 @@ char common_sampler_type_to_chr(enum common_sampler_type cnstr) {
         case COMMON_SAMPLER_TYPE_XTC:         return 'x';
         case COMMON_SAMPLER_TYPE_INFILL:      return 'i';
         case COMMON_SAMPLER_TYPE_PENALTIES:   return 'e';
+        case COMMON_SAMPLER_TYPE_ADAPTIVE_P:  return 'a';
         default : return '?';
     }
 }
@@ -569,6 +656,7 @@ std::string common_sampler_type_to_str(enum common_sampler_type cnstr) {
         case COMMON_SAMPLER_TYPE_XTC:         return "xtc";
         case COMMON_SAMPLER_TYPE_INFILL:      return "infill";
         case COMMON_SAMPLER_TYPE_PENALTIES:   return "penalties";
+        case COMMON_SAMPLER_TYPE_ADAPTIVE_P:  return "adaptive_p";
         default : return "";
     }
 }
@@ -585,6 +673,7 @@ std::vector<common_sampler_type> common_sampler_types_from_names(const std::vect
         { "xtc",         COMMON_SAMPLER_TYPE_XTC },
         { "infill",      COMMON_SAMPLER_TYPE_INFILL },
         { "penalties",   COMMON_SAMPLER_TYPE_PENALTIES },
+        { "adaptive_p",  COMMON_SAMPLER_TYPE_ADAPTIVE_P },
     };
 
     // since samplers names are written multiple ways
@@ -600,6 +689,7 @@ std::vector<common_sampler_type> common_sampler_types_from_names(const std::vect
         { "typ",         COMMON_SAMPLER_TYPE_TYPICAL_P },
         { "min-p",       COMMON_SAMPLER_TYPE_MIN_P },
         { "temp",        COMMON_SAMPLER_TYPE_TEMPERATURE },
+        { "adaptive-p",  COMMON_SAMPLER_TYPE_ADAPTIVE_P },
     };
 
     std::vector<common_sampler_type> samplers;
@@ -636,6 +726,7 @@ std::vector<common_sampler_type> common_sampler_types_from_chars(const std::stri
         { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_XTC),         COMMON_SAMPLER_TYPE_XTC },
         { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_INFILL),      COMMON_SAMPLER_TYPE_INFILL },
         { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_PENALTIES),   COMMON_SAMPLER_TYPE_PENALTIES },
+        { common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_ADAPTIVE_P),  COMMON_SAMPLER_TYPE_ADAPTIVE_P },
     };
 
     std::vector<common_sampler_type> samplers;

llama/llama.cpp/common/sampling.h

@@ -36,7 +36,8 @@ struct common_sampler;
 
 // llama_sampler API overloads
 
-struct common_sampler * common_sampler_init(const struct llama_model * model, const struct common_params_sampling & params);
+// note: can mutate params in some cases
+struct common_sampler * common_sampler_init(const struct llama_model * model, struct common_params_sampling & params);
 
 void common_sampler_free(struct common_sampler * gsmpl);
 
@@ -48,6 +49,7 @@ struct common_sampler * common_sampler_clone (struct common_sampler * gsmpl);
 // arguments can be nullptr to skip printing
 void common_perf_print(const struct llama_context * ctx, const struct common_sampler * gsmpl);
 
+// get the underlying llama_sampler_chain
 struct llama_sampler * common_sampler_get(const struct common_sampler * gsmpl);
 
 // extended sampling implementation:
@@ -57,7 +59,10 @@ struct llama_sampler * common_sampler_get(const struct common_sampler * gsmpl);
 // - check if the token fits the grammar (if any)
 // - if not: resample by first applying the grammar constraints and then sampling again (slower path)
 //
-llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_context * ctx, int idx);
+// if grammar_first is true, the grammar is applied before the samplers (slower)
+// useful in cases where all the resulting candidates (not just the sampled one) must fit the grammar
+//
+llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_context * ctx, int idx, bool grammar_first = false);
 
 // generalized version of common_sampler_sample
 //
@@ -75,10 +80,10 @@ llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_co
 //
 // returns at least 1 token, up to idxs.size()
 //
-std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const std::vector<int> & idxs, const llama_tokens & draft);
+std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const std::vector<int> & idxs, const llama_tokens & draft, bool grammar_first = false);
 
 // assume idxs == [ 0, 1, 2, ..., draft.size() ]
-std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const llama_tokens & draft);
+std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const llama_tokens & draft, bool grammar_first = false);
 
 uint32_t common_sampler_get_seed(const struct common_sampler * gsmpl);
 

llama/llama.cpp/include/llama-cpp.h

@@ -21,7 +21,9 @@ struct llama_sampler_deleter {
 };
 
 struct llama_adapter_lora_deleter {
-    void operator()(llama_adapter_lora * adapter) { llama_adapter_lora_free(adapter); }
+    void operator()(llama_adapter_lora *) {
+        // llama_adapter_lora_free is deprecated
+    }
 };
 
 typedef std::unique_ptr<llama_model, llama_model_deleter> llama_model_ptr;

llama/llama.cpp/include/llama.h

@@ -286,7 +286,7 @@ extern "C" {
         // NULL-terminated list of buffer types to use for tensors that match a pattern
         const struct llama_model_tensor_buft_override * tensor_buft_overrides;
 
-        int32_t n_gpu_layers; // number of layers to store in VRAM
+        int32_t n_gpu_layers; // number of layers to store in VRAM, a negative value means all layers
         enum llama_split_mode split_mode; // how to split the model across multiple GPUs
 
         // the GPU that is used for the entire model when split_mode is LLAMA_SPLIT_MODE_NONE
@@ -309,6 +309,7 @@ extern "C" {
         // Keep the booleans together to avoid misalignment during copy-by-value.
         bool vocab_only;      // only load the vocabulary, no weights
         bool use_mmap;        // use mmap if possible
+        bool use_direct_io;   // use direct io, takes precedence over use_mmap
         bool use_mlock;       // force system to keep model in RAM
         bool check_tensors;   // validate model tensor data
         bool use_extra_bufts; // use extra buffer types (used for weight repacking)
@@ -316,6 +317,11 @@ extern "C" {
         bool no_alloc;        // only load metadata and simulate memory allocations
     };
 
+    struct llama_sampler_seq_config {
+        llama_seq_id           seq_id;
+        struct llama_sampler * sampler;
+    };
+
     // NOTE: changing the default values of parameters marked as [EXPERIMENTAL] may cause crashes or incorrect results in certain configurations
     //       https://github.com/ggml-org/llama.cpp/pull/7544
     struct llama_context_params {
@@ -364,6 +370,12 @@ extern "C" {
         bool kv_unified;  // use a unified buffer across the input sequences when computing the attention
                           // try to disable when n_seq_max > 1 for improved performance when the sequences do not share a large prefix
                           // ref: https://github.com/ggml-org/llama.cpp/pull/14363
+
+        // [EXPERIMENTAL]
+        // backend sampler chain configuration (make sure the caller keeps the sampler chains alive)
+        // note: the samplers must be sampler chains (i.e. use llama_sampler_chain_init)
+        struct llama_sampler_seq_config * samplers;
+        size_t                            n_samplers;
     };
 
     // model quantization parameters
@@ -467,16 +479,24 @@ extern "C" {
     // Frees all allocated memory
     LLAMA_API void llama_free(struct llama_context * ctx);
 
+    enum llama_params_fit_status {
+        LLAMA_PARAMS_FIT_STATUS_SUCCESS = 0, // found allocations that are projected to fit
+        LLAMA_PARAMS_FIT_STATUS_FAILURE = 1, // could not find allocations that are projected to fit
+        LLAMA_PARAMS_FIT_STATUS_ERROR   = 2, // a hard error occured, e.g. because no model could be found at the specified path
+    };
+
     // fits mparams and cparams to free device memory (assumes system memory is unlimited)
-    // returns true if the parameters could be successfully modified to fit device memory
-    // this function is NOT thread safe because it modifies the global llama logger state
-    LLAMA_API bool llama_params_fit(
+    //   - returns true if the parameters could be successfully modified to fit device memory
+    //   - this function is NOT thread safe because it modifies the global llama logger state
+    //   - only parameters that have the same value as in llama_default_model_params are modified
+    //     with the exception of the context size which is modified if and only if equal to 0
+    LLAMA_API enum llama_params_fit_status llama_params_fit(
                                    const char   * path_model,
                     struct llama_model_params   * mparams,
                     struct llama_context_params * cparams,
                                           float * tensor_split,          // writable buffer for tensor split, needs at least llama_max_devices elements
         struct llama_model_tensor_buft_override * tensor_buft_overrides, // writable buffer for overrides, needs at least llama_max_tensor_buft_overrides elements
-                                         size_t   margin,                // margin of memory to leave per device in bytes
+                                         size_t * margins,               // margins of memory to leave per device in bytes
                                        uint32_t   n_ctx_min,             // minimum context size to set when trying to reduce memory use
                             enum ggml_log_level   log_level);            // minimum log level to print during fitting, lower levels go to debug log
 
@@ -517,6 +537,7 @@ extern "C" {
     LLAMA_API int32_t llama_model_n_ctx_train(const struct llama_model * model);
     LLAMA_API int32_t llama_model_n_embd     (const struct llama_model * model);
     LLAMA_API int32_t llama_model_n_embd_inp (const struct llama_model * model);
+    LLAMA_API int32_t llama_model_n_embd_out (const struct llama_model * model);
     LLAMA_API int32_t llama_model_n_layer    (const struct llama_model * model);
     LLAMA_API int32_t llama_model_n_head     (const struct llama_model * model);
     LLAMA_API int32_t llama_model_n_head_kv  (const struct llama_model * model);
@@ -600,6 +621,8 @@ extern "C" {
     //
 
     // Load a LoRA adapter from file
+    // The adapter is valid as long as the associated model is not freed
+    // All adapters must be loaded before context creation
     LLAMA_API struct llama_adapter_lora * llama_adapter_lora_init(
             struct llama_model * model,
             const char * path_lora);
@@ -624,7 +647,8 @@ extern "C" {
 
     // Manually free a LoRA adapter
     // NOTE: loaded adapters will be free when the associated model is deleted
-    LLAMA_API void llama_adapter_lora_free(struct llama_adapter_lora * adapter);
+    LLAMA_API DEPRECATED(void llama_adapter_lora_free(struct llama_adapter_lora * adapter),
+            "adapters are now freed together with the associated model");
 
     // Get the invocation tokens if the current lora is an alora
     LLAMA_API uint64_t            llama_adapter_get_alora_n_invocation_tokens(const struct llama_adapter_lora * adapter);
@@ -983,6 +1007,32 @@ extern "C" {
     // otherwise: float[n_embd] (1-dimensional)
     LLAMA_API float * llama_get_embeddings_seq(struct llama_context * ctx, llama_seq_id seq_id);
 
+    //
+    // backend sampling API [EXPERIMENTAL]
+    // note: use only if the llama_context was created with at least one llama_sampler_seq_config
+    //
+
+    // Get the backend sampled token for the ith token.
+    // Returns LLAMA_TOKEN_NULL if no token was sampled.
+    LLAMA_API llama_token llama_get_sampled_token_ith(struct llama_context * ctx, int32_t i);
+
+    // Get the backend sampled probabilites for the ith token
+    // The index matches llama_get_sampled_token_ith().
+    // Returns NULL if no probabilites were generated.
+    LLAMA_API float *  llama_get_sampled_probs_ith      (struct llama_context * ctx, int32_t i);
+    LLAMA_API uint32_t llama_get_sampled_probs_count_ith(struct llama_context * ctx, int32_t i);
+
+    // Get the backend sampled logits for the ith token
+    // Returns NULL if no logits were sampled.
+    LLAMA_API float *  llama_get_sampled_logits_ith      (struct llama_context * ctx, int32_t i);
+    LLAMA_API uint32_t llama_get_sampled_logits_count_ith(struct llama_context * ctx, int32_t i);
+
+    // Get the backend sampled candidates (token ids) for the ith token
+    // These are needed to map probability/logit indices to vocab token ids.
+    // Returns NULL if no candidates were sampled.
+    LLAMA_API llama_token * llama_get_sampled_candidates_ith      (struct llama_context * ctx, int32_t i);
+    LLAMA_API uint32_t      llama_get_sampled_candidates_count_ith(struct llama_context * ctx, int32_t i);
+
     //
     // Vocab
     //
@@ -1154,11 +1204,16 @@ extern "C" {
     //
     //    llama_sampler_free(smpl);
     //
-    // TODO: In the future, llama_sampler will be utilized to offload the sampling to the backends (e.g. GPU).
-    //
 
     typedef void * llama_sampler_context_t;
 
+    struct llama_sampler_data {
+        struct ggml_tensor * logits;
+        struct ggml_tensor * probs;
+        struct ggml_tensor * sampled;
+        struct ggml_tensor * candidates;
+    };
+
     // user code can implement the interface below in order to create custom llama_sampler
     struct llama_sampler_i {
         const char *           (*name)  (const struct llama_sampler * smpl);                                 // can be NULL
@@ -1168,17 +1223,44 @@ extern "C" {
         struct llama_sampler * (*clone) (const struct llama_sampler * smpl);                                 // can be NULL if ctx is NULL
         void                   (*free)  (      struct llama_sampler * smpl);                                 // can be NULL if ctx is NULL
 
-        // TODO: API for internal libllama usage for appending the sampling to an existing ggml_cgraph
-        //void (*apply_ggml) (struct llama_sampler * smpl, ...);
+        // [EXPERIMENTAL]
+        // backend sampling interface:
+
+        // return true if the backend supports all ops needed by the sampler
+        // note: call once per sampler
+        bool (*backend_init)(struct llama_sampler * smpl, ggml_backend_buffer_type_t buft);
+
+        // call after .backend_apply()
+        void (*backend_accept)(
+                struct llama_sampler * smpl,
+                struct ggml_context  * ctx,
+                struct ggml_cgraph   * gf,
+                struct ggml_tensor   * selected_token);
+
+        // call after .backend_init()
+        void (*backend_apply)(
+                struct llama_sampler      * smpl,
+                struct ggml_context       * ctx,
+                struct ggml_cgraph        * gf,
+                struct llama_sampler_data * data);
+
+        // called before graph execution to set inputs for the current ubatch
+        void (*backend_set_input)(struct llama_sampler * smpl);
     };
 
     struct llama_sampler {
-        const struct llama_sampler_i * iface;
-        llama_sampler_context_t        ctx;
+        struct llama_sampler_i * iface;
+
+        llama_sampler_context_t ctx;
     };
 
+    // [EXPERIMENTAL]
+    // attach a sampler to the context
+    // note: prefer initializing the context with llama_context_params.samplers when possible
+    LLAMA_API bool llama_set_sampler(struct llama_context * ctx, llama_seq_id seq_id, struct llama_sampler * smpl);
+
     // mirror of llama_sampler_i:
-    LLAMA_API struct llama_sampler * llama_sampler_init  (const struct llama_sampler_i * iface, llama_sampler_context_t ctx);
+    LLAMA_API struct llama_sampler * llama_sampler_init  (      struct llama_sampler_i * iface, llama_sampler_context_t ctx);
     LLAMA_API const char *           llama_sampler_name  (const struct llama_sampler * smpl);
     LLAMA_API void                   llama_sampler_accept(      struct llama_sampler * smpl, llama_token token);
     LLAMA_API void                   llama_sampler_apply (      struct llama_sampler * smpl, llama_token_data_array * cur_p);
@@ -1194,7 +1276,15 @@ extern "C" {
 
     // important: takes ownership of the sampler object and will free it when llama_sampler_free is called
     LLAMA_API void                   llama_sampler_chain_add(      struct llama_sampler * chain, struct llama_sampler * smpl);
-    LLAMA_API struct llama_sampler * llama_sampler_chain_get(const struct llama_sampler * chain, int32_t i);
+
+    // return NULL if:
+    //   - the sampler is NULL
+    //   - the sampler is not a llama_sampler_chain
+    //   - the index is out of bounds, unless i == -1
+    //   - if i == -1, returns the chain itself (can be used to check if the sampler is a chain)
+    LLAMA_API struct llama_sampler * llama_sampler_chain_get(      struct llama_sampler * chain, int32_t i);
+
+    // the total number of samplers in the chain
     LLAMA_API int                    llama_sampler_chain_n  (const struct llama_sampler * chain);
 
     // after removing a sampler, the chain will no longer own it, and it will not be freed when the chain is freed
@@ -1203,7 +1293,9 @@ extern "C" {
     // available samplers:
 
     LLAMA_API struct llama_sampler * llama_sampler_init_greedy(void);
-    LLAMA_API struct llama_sampler * llama_sampler_init_dist  (uint32_t seed);
+
+    /// seed == LLAMA_DEFAULT_SEED to use a random seed.
+    LLAMA_API struct llama_sampler * llama_sampler_init_dist(uint32_t seed);
 
     /// @details Top-K sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
     /// Setting k <= 0 makes this a noop
@@ -1304,6 +1396,33 @@ extern "C" {
                           const char ** seq_breakers,
                               size_t    num_breakers);
 
+    /// adaptive-p: select tokens near a configurable target probability over time.
+    ///
+    /// the adaptive-p sampler transforms the token probability distribution to favor tokens
+    /// that fall near a user-configurable probability target.
+    ///
+    /// internally, the sampler maintains an exponential moving average of the *ORIGINAL*
+    /// probabilities of selected tokens at each sampling step. it uses this EMA to compute an
+    /// adapted target probability at each sampling step, thus maintaining the desired target
+    /// probability over time.
+    ///
+    /// adaptive-p selects a token ID rather than just mutating candidates, so it must be last
+    /// in the sampler chain (like mirostat, dist, greedy).
+    ///
+    /// only mild truncation before this sampler is recommended. we suggest applying min-p
+    /// before adaptive-p as the only other active sampler in the chain.
+    ///
+    /// @param target select tokens near this probability (valid range 0.0 to 1.0; negative = disabled)
+    /// @param decay  EMA decay for adaptation; history ≈ 1/(1-decay) tokens (valid range 0.0 - 0.99)
+    /// @param seed   RNG seed
+    ///
+    /// ref: https://github.com/ggml-org/llama.cpp/pull/17927
+    ///
+    LLAMA_API struct llama_sampler * llama_sampler_init_adaptive_p(
+                               float   target,
+                               float   decay,
+                            uint32_t   seed);
+
     LLAMA_API struct llama_sampler * llama_sampler_init_logit_bias(
                              int32_t   n_vocab,
                              int32_t   n_logit_bias,
@@ -1357,12 +1476,12 @@ extern "C" {
     /// @details Build a split GGUF final path for this chunk.
     ///          llama_split_path(split_path, sizeof(split_path), "/models/ggml-model-q4_0", 2, 4) => split_path = "/models/ggml-model-q4_0-00002-of-00004.gguf"
     //  Returns the split_path length.
-    LLAMA_API int llama_split_path(char * split_path, size_t maxlen, const char * path_prefix, int split_no, int split_count);
+    LLAMA_API int32_t llama_split_path(char * split_path, size_t maxlen, const char * path_prefix, int32_t split_no, int32_t split_count);
 
     /// @details Extract the path prefix from the split_path if and only if the split_no and split_count match.
     ///          llama_split_prefix(split_prefix, 64, "/models/ggml-model-q4_0-00002-of-00004.gguf", 2, 4) => split_prefix = "/models/ggml-model-q4_0"
     //  Returns the split_prefix length.
-    LLAMA_API int llama_split_prefix(char * split_prefix, size_t maxlen, const char * split_path, int split_no, int split_count);
+    LLAMA_API int32_t llama_split_prefix(char * split_prefix, size_t maxlen, const char * split_path, int32_t split_no, int32_t split_count);
 
     // Print system information
     LLAMA_API const char * llama_print_system_info(void);

llama/llama.cpp/src/llama-adapter.cpp

@@ -411,6 +411,9 @@ static void llama_adapter_lora_init_impl(llama_model & model, const char * path_
         }
     }
 
+    // register adapter with model
+    model.loras.insert(&adapter);
+
     LLAMA_LOG_INFO("%s: loaded %zu tensors from lora file\n", __func__, adapter.ab_map.size()*2);
 }
 
@@ -468,8 +471,8 @@ int32_t llama_adapter_meta_val_str_by_index(const llama_adapter_lora * adapter,
     return snprintf(buf, buf_size, "%s", it->second.c_str());
 }
 
-void llama_adapter_lora_free(llama_adapter_lora * adapter) {
-    delete adapter;
+void llama_adapter_lora_free(llama_adapter_lora *) {
+    // deprecated: adapters are freed by llama_model's destructor
 }
 
 uint64_t llama_adapter_get_alora_n_invocation_tokens(const struct llama_adapter_lora * adapter) {

llama/llama.cpp/src/llama-adapter.h

@@ -77,6 +77,10 @@ struct llama_adapter_lora {
     ~llama_adapter_lora() = default;
 
     llama_adapter_lora_weight * get_weight(ggml_tensor * w);
+
+    uint32_t get_n_nodes() const {
+        return ab_map.size() * 6u; // a, b, scale, add, 2 x mul_mat
+    }
 };
 
 using llama_adapter_loras = std::unordered_map<llama_adapter_lora *, float>;

llama/llama.cpp/src/llama-arch.cpp

@@ -20,6 +20,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_STARCODER,        "starcoder"        },
     { LLM_ARCH_REFACT,           "refact"           },
     { LLM_ARCH_BERT,             "bert"             },
+    { LLM_ARCH_MODERN_BERT,      "modern-bert"      },
     { LLM_ARCH_NOMIC_BERT,       "nomic-bert"       },
     { LLM_ARCH_NOMIC_BERT_MOE,   "nomic-bert-moe"   },
     { LLM_ARCH_NEO_BERT,         "neo-bert"         },
@@ -41,6 +42,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_PHIMOE,           "phimoe"           },
     { LLM_ARCH_PLAMO,            "plamo"            },
     { LLM_ARCH_PLAMO2,           "plamo2"           },
+    { LLM_ARCH_PLAMO3,           "plamo3"           },
     { LLM_ARCH_CODESHELL,        "codeshell"        },
     { LLM_ARCH_ORION,            "orion"            },
     { LLM_ARCH_INTERNLM2,        "internlm2"        },
@@ -79,6 +81,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_NEMOTRON_H_MOE,   "nemotron_h_moe"   },
     { LLM_ARCH_EXAONE,           "exaone"           },
     { LLM_ARCH_EXAONE4,          "exaone4"          },
+    { LLM_ARCH_EXAONE_MOE,       "exaone-moe"       },
     { LLM_ARCH_RWKV6,            "rwkv6"            },
     { LLM_ARCH_RWKV6QWEN2,       "rwkv6qwen2"       },
     { LLM_ARCH_RWKV7,            "rwkv7"            },
@@ -115,6 +118,9 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_RND1,             "rnd1"             },
     { LLM_ARCH_PANGU_EMBED,      "pangu-embedded"   },
     { LLM_ARCH_MISTRAL3,         "mistral3"         },
+    { LLM_ARCH_MIMO2,            "mimo2"           },
+    { LLM_ARCH_LLAMA_EMBED,      "llama-embed"      },
+    { LLM_ARCH_MAINCODER,        "maincoder"        },
     { LLM_ARCH_UNKNOWN,          "(unknown)"        },
 };
 
@@ -148,6 +154,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_VOCAB_SIZE,                        "%s.vocab_size"                        },
     { LLM_KV_CONTEXT_LENGTH,                    "%s.context_length"                    },
     { LLM_KV_EMBEDDING_LENGTH,                  "%s.embedding_length"                  },
+    { LLM_KV_EMBEDDING_LENGTH_OUT,              "%s.embedding_length_out"              },
     { LLM_KV_FEATURES_LENGTH,                   "%s.features_length"                   },
     { LLM_KV_BLOCK_COUNT,                       "%s.block_count"                       },
     { LLM_KV_LEADING_DENSE_BLOCK_COUNT,         "%s.leading_dense_block_count"         },
@@ -205,6 +212,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_ATTENTION_GATE_LORA_RANK,               "%s.attention.gate_lora_rank"               },
     { LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT,       "%s.attention.relative_buckets_count"       },
     { LLM_KV_ATTENTION_SLIDING_WINDOW,               "%s.attention.sliding_window"               },
+    { LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN,       "%s.attention.sliding_window_pattern"       },
     { LLM_KV_ATTENTION_SCALE,                        "%s.attention.scale"                        },
     { LLM_KV_ATTENTION_OUTPUT_SCALE,                 "%s.attention.output_scale"                 },
     { LLM_KV_ATTENTION_TEMPERATURE_LENGTH,           "%s.attention.temperature_length"           },
@@ -216,6 +224,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_ROPE_DIMENSION_COUNT,          "%s.rope.dimension_count"                 },
     { LLM_KV_ROPE_DIMENSION_SECTIONS,       "%s.rope.dimension_sections"              },
     { LLM_KV_ROPE_FREQ_BASE,                "%s.rope.freq_base"                       },
+    { LLM_KV_ROPE_FREQ_BASE_SWA,            "%s.rope.freq_base_swa"                   },
     { LLM_KV_ROPE_SCALE_LINEAR,             "%s.rope.scale_linear"                    },
     { LLM_KV_ROPE_SCALING_TYPE,             "%s.rope.scaling.type"                    },
     { LLM_KV_ROPE_SCALING_FACTOR,           "%s.rope.scaling.factor"                  },
@@ -500,6 +509,7 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
         case LLM_ARCH_LLAMA:
         case LLM_ARCH_DECI:
         case LLM_ARCH_MISTRAL3:
+        case LLM_ARCH_LLAMA_EMBED:
             return {
                 LLM_TENSOR_TOKEN_EMBD,
                 LLM_TENSOR_OUTPUT_NORM,
@@ -781,6 +791,20 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
                 LLM_TENSOR_CLS,
                 LLM_TENSOR_CLS_OUT,
             };
+        case LLM_ARCH_MODERN_BERT:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_TOKEN_EMBD_NORM,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_ATTN_QKV,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_CLS,
+                LLM_TENSOR_CLS_OUT,
+            };
         case LLM_ARCH_JINA_BERT_V2:
             return {
                 LLM_TENSOR_TOKEN_EMBD,
@@ -930,6 +954,8 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
                 LLM_TENSOR_ATTN_K_NORM,
                 LLM_TENSOR_ATTN_V,
                 LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_ATTN_QKV,
+                LLM_TENSOR_ATTN_GATE,
                 LLM_TENSOR_FFN_NORM,
                 LLM_TENSOR_FFN_GATE_INP,
                 LLM_TENSOR_FFN_GATE_EXPS,
@@ -1060,6 +1086,22 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
                 LLM_TENSOR_ATTN_POST_NORM,
                 LLM_TENSOR_FFN_POST_NORM,
             };
+        case LLM_ARCH_PLAMO3:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_QKV,
+                LLM_TENSOR_ATTN_Q_NORM,
+                LLM_TENSOR_ATTN_K_NORM,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_ATTN_POST_NORM,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_POST_NORM,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+            };
         case LLM_ARCH_CODESHELL:
             return {
                 LLM_TENSOR_TOKEN_EMBD,
@@ -1690,6 +1732,38 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
                 LLM_TENSOR_FFN_UP,
                 LLM_TENSOR_FFN_POST_NORM,
             };
+        case LLM_ARCH_EXAONE_MOE:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ROPE_FREQS,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_Q_NORM,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_K_NORM,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+                LLM_TENSOR_FFN_GATE_INP,
+                LLM_TENSOR_FFN_GATE_EXPS,
+                LLM_TENSOR_FFN_DOWN_EXPS,
+                LLM_TENSOR_FFN_UP_EXPS,
+                LLM_TENSOR_FFN_GATE_SHEXP,
+                LLM_TENSOR_FFN_UP_SHEXP,
+                LLM_TENSOR_FFN_DOWN_SHEXP,
+                LLM_TENSOR_FFN_EXP_PROBS_B,
+                LLM_TENSOR_NEXTN_EH_PROJ,
+                LLM_TENSOR_NEXTN_EMBED_TOKENS,
+                LLM_TENSOR_NEXTN_ENORM,
+                LLM_TENSOR_NEXTN_HNORM,
+                LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD,
+                LLM_TENSOR_NEXTN_SHARED_HEAD_NORM,
+            };
         case LLM_ARCH_RWKV6:
             return {
                 LLM_TENSOR_TOKEN_EMBD,
@@ -2040,6 +2114,7 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
                 LLM_TENSOR_TOKEN_EMBD,
                 LLM_TENSOR_OUTPUT_NORM_LFM2,
                 LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_DENSE_2_OUT,
             };
         case LLM_ARCH_LFM2MOE:
             return {
@@ -2058,7 +2133,7 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
                 LLM_TENSOR_SHORTCONV_INPROJ,
                 LLM_TENSOR_SHORTCONV_OUTPROJ,
                 LLM_TENSOR_TOKEN_EMBD,
-                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT_NORM_LFM2,
                 LLM_TENSOR_FFN_GATE_INP,
                 LLM_TENSOR_FFN_GATE_EXPS,
                 LLM_TENSOR_FFN_DOWN_EXPS,
@@ -2174,11 +2249,49 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
                 LLM_TENSOR_VISEXP_FFN_DOWN,
                 LLM_TENSOR_VISEXP_FFN_UP,
             };
+        case LLM_ARCH_MIMO2:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_SINKS,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+                LLM_TENSOR_FFN_GATE_INP,
+                LLM_TENSOR_FFN_GATE_EXPS,
+                LLM_TENSOR_FFN_DOWN_EXPS,
+                LLM_TENSOR_FFN_UP_EXPS,
+                LLM_TENSOR_FFN_EXP_PROBS_B,
+            };
         case LLM_ARCH_GPTJ:
         case LLM_ARCH_UNKNOWN:
             return {
                 LLM_TENSOR_TOKEN_EMBD,
             };
+        case LLM_ARCH_MAINCODER:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_Q_NORM,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_K_NORM,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+            };
         case LLM_ARCH_SOLAR:
             return {
                 LLM_TENSOR_TOKEN_EMBD,

llama/llama.cpp/src/llama-arch.h

@@ -24,6 +24,7 @@ enum llm_arch {
     LLM_ARCH_STARCODER,
     LLM_ARCH_REFACT,
     LLM_ARCH_BERT,
+    LLM_ARCH_MODERN_BERT,
     LLM_ARCH_NOMIC_BERT,
     LLM_ARCH_NOMIC_BERT_MOE,
     LLM_ARCH_NEO_BERT,
@@ -45,6 +46,7 @@ enum llm_arch {
     LLM_ARCH_PHIMOE,
     LLM_ARCH_PLAMO,
     LLM_ARCH_PLAMO2,
+    LLM_ARCH_PLAMO3,
     LLM_ARCH_CODESHELL,
     LLM_ARCH_ORION,
     LLM_ARCH_INTERNLM2,
@@ -83,6 +85,7 @@ enum llm_arch {
     LLM_ARCH_NEMOTRON_H_MOE,
     LLM_ARCH_EXAONE,
     LLM_ARCH_EXAONE4,
+    LLM_ARCH_EXAONE_MOE,
     LLM_ARCH_RWKV6,
     LLM_ARCH_RWKV6QWEN2,
     LLM_ARCH_RWKV7,
@@ -119,6 +122,9 @@ enum llm_arch {
     LLM_ARCH_RND1,
     LLM_ARCH_PANGU_EMBED,
     LLM_ARCH_MISTRAL3,
+    LLM_ARCH_MIMO2,
+    LLM_ARCH_LLAMA_EMBED,
+    LLM_ARCH_MAINCODER,
     LLM_ARCH_UNKNOWN,
 };
 
@@ -152,6 +158,7 @@ enum llm_kv {
     LLM_KV_VOCAB_SIZE,
     LLM_KV_CONTEXT_LENGTH,
     LLM_KV_EMBEDDING_LENGTH,
+    LLM_KV_EMBEDDING_LENGTH_OUT,
     LLM_KV_FEATURES_LENGTH,
     LLM_KV_BLOCK_COUNT,
     LLM_KV_LEADING_DENSE_BLOCK_COUNT,
@@ -209,6 +216,7 @@ enum llm_kv {
     LLM_KV_ATTENTION_GATE_LORA_RANK,
     LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT,
     LLM_KV_ATTENTION_SLIDING_WINDOW,
+    LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN,
     LLM_KV_ATTENTION_SCALE,
     LLM_KV_ATTENTION_OUTPUT_SCALE,
     LLM_KV_ATTENTION_TEMPERATURE_LENGTH,
@@ -220,6 +228,7 @@ enum llm_kv {
     LLM_KV_ROPE_DIMENSION_COUNT,
     LLM_KV_ROPE_DIMENSION_SECTIONS,
     LLM_KV_ROPE_FREQ_BASE,
+    LLM_KV_ROPE_FREQ_BASE_SWA,
     LLM_KV_ROPE_SCALE_LINEAR,
     LLM_KV_ROPE_SCALING_TYPE,
     LLM_KV_ROPE_SCALING_FACTOR,

llama/llama.cpp/src/llama-chat.cpp

@@ -57,6 +57,7 @@ static const std::map<std::string, llm_chat_template> LLM_CHAT_TEMPLATES = {
     { "minicpm",           LLM_CHAT_TEMPLATE_MINICPM           },
     { "exaone3",           LLM_CHAT_TEMPLATE_EXAONE_3          },
     { "exaone4",           LLM_CHAT_TEMPLATE_EXAONE_4          },
+    { "exaone-moe",        LLM_CHAT_TEMPLATE_EXAONE_MOE        },
     { "rwkv-world",        LLM_CHAT_TEMPLATE_RWKV_WORLD        },
     { "granite",           LLM_CHAT_TEMPLATE_GRANITE           },
     { "gigachat",          LLM_CHAT_TEMPLATE_GIGACHAT          },
@@ -74,6 +75,7 @@ static const std::map<std::string, llm_chat_template> LLM_CHAT_TEMPLATES = {
     { "seed_oss",          LLM_CHAT_TEMPLATE_SEED_OSS          },
     { "grok-2",            LLM_CHAT_TEMPLATE_GROK_2            },
     { "pangu-embedded",    LLM_CHAT_TEMPLATE_PANGU_EMBED       },
+    { "solar-open",        LLM_CHAT_TEMPLATE_SOLAR_OPEN        },
 };
 
 llm_chat_template llm_chat_template_from_str(const std::string & name) {
@@ -136,6 +138,9 @@ llm_chat_template llm_chat_detect_template(const std::string & tmpl) {
     } else if (tmpl_contains("[gMASK]<sop>")) {
         return LLM_CHAT_TEMPLATE_CHATGLM_4;
     } else if (tmpl_contains("<|assistant|>") && tmpl_contains("<|user|>")) {
+        if (tmpl_contains("<|tool_declare|>")) {
+            return LLM_CHAT_TEMPLATE_EXAONE_MOE;
+        }
         return tmpl_contains("</s>") ? LLM_CHAT_TEMPLATE_FALCON_3 : LLM_CHAT_TEMPLATE_GLMEDGE;
     } else if (tmpl_contains("<|{{ item['role'] }}|>") && tmpl_contains("<|begin_of_image|>")) {
         return LLM_CHAT_TEMPLATE_GLMEDGE;
@@ -216,6 +221,8 @@ llm_chat_template llm_chat_detect_template(const std::string & tmpl) {
         return LLM_CHAT_TEMPLATE_GROK_2;
     } else if (tmpl_contains(LU8("[unused9]系统：[unused10]"))) {
         return LLM_CHAT_TEMPLATE_PANGU_EMBED;
+    } else if (tmpl_contains("<|begin|>") && tmpl_contains("<|end|>") && tmpl_contains("<|content|>")) {
+        return LLM_CHAT_TEMPLATE_SOLAR_OPEN;
     }
     return LLM_CHAT_TEMPLATE_UNKNOWN;
 }
@@ -573,6 +580,22 @@ int32_t llm_chat_apply_template(
         if (add_ass) {
             ss << "[|assistant|]";
         }
+    } else if (tmpl == LLM_CHAT_TEMPLATE_EXAONE_MOE) {
+        for (auto message : chat) {
+            std::string role(message->role);
+            if (role == "system") {
+                ss << "<|system|>\n" << trim(message->content) << "<|endofturn|>\n";
+            } else if (role == "user") {
+                ss << "<|user|>\n" << trim(message->content) << "<|endofturn|>\n";
+            } else if (role == "assistant") {
+                ss << "<|assistant|>\n" << trim(message->content) << "<|endofturn|>\n";
+            } else if (role == "tool") {
+                ss << "<|tool|>\n" << trim(message->content) << "<|endofturn|>\n";
+            }
+        }
+        if (add_ass) {
+            ss << "<|assistant|>\n";
+        }
     } else if (tmpl == LLM_CHAT_TEMPLATE_RWKV_WORLD) {
         // this template requires the model to have "\n\n" as EOT token
         for (size_t i = 0; i < chat.size(); i++) {
@@ -845,6 +868,14 @@ int32_t llm_chat_apply_template(
         if (add_ass) {
             ss << "[unused9]助手：";
         }
+    } else if (tmpl == LLM_CHAT_TEMPLATE_SOLAR_OPEN) {
+        for (auto message : chat) {
+            std::string role(message->role);
+            ss << "<|begin|>" << role << "<|content|>" << message->content << "<|end|>";
+        }
+        if (add_ass) {
+            ss << "<|begin|>assistant";
+        }
     } else {
         // template not supported
         return -1;

llama/llama.cpp/src/llama-chat.h

@@ -36,6 +36,7 @@ enum llm_chat_template {
     LLM_CHAT_TEMPLATE_MINICPM,
     LLM_CHAT_TEMPLATE_EXAONE_3,
     LLM_CHAT_TEMPLATE_EXAONE_4,
+    LLM_CHAT_TEMPLATE_EXAONE_MOE,
     LLM_CHAT_TEMPLATE_RWKV_WORLD,
     LLM_CHAT_TEMPLATE_GRANITE,
     LLM_CHAT_TEMPLATE_GIGACHAT,
@@ -54,6 +55,7 @@ enum llm_chat_template {
     LLM_CHAT_TEMPLATE_SEED_OSS,
     LLM_CHAT_TEMPLATE_GROK_2,
     LLM_CHAT_TEMPLATE_PANGU_EMBED,
+    LLM_CHAT_TEMPLATE_SOLAR_OPEN,
     LLM_CHAT_TEMPLATE_UNKNOWN,
 };
 

llama/llama.cpp/src/llama-context.h

@@ -40,6 +40,14 @@ struct llama_context {
 
     ~llama_context();
 
+    // reserve a new backend scheduler (if needed)
+    // for example, when:
+    //   - changing loras
+    //   - changing samplers
+    //   - changing attention type
+    //   - etc.
+    void sched_reserve();
+
     void synchronize();
 
     const llama_model   & get_model()   const;
@@ -70,6 +78,18 @@ struct llama_context {
     float * get_embeddings_ith(int32_t i);
     float * get_embeddings_seq(llama_seq_id seq_id);
 
+    llama_token * get_sampled_tokens() const;
+    llama_token   get_sampled_token_ith(int32_t idx);
+
+    float * get_sampled_logits_ith(int32_t idx);
+    size_t  get_sampled_logits_count(int32_t idx);
+
+    float * get_sampled_probs_ith(int32_t idx);
+    size_t  get_sampled_probs_count(int32_t idx);
+
+    const llama_token * get_sampled_candidates_ith(int32_t idx);
+    size_t get_sampled_candidates_count(int32_t idx);
+
     void attach_threadpool(
             ggml_threadpool_t threadpool,
             ggml_threadpool_t threadpool_batch);
@@ -192,10 +212,13 @@ private:
 
     // Make sure enough space is available for outputs.
     // Returns max number of outputs for which space was reserved.
-    uint32_t output_reserve(int32_t n_outputs);
+    uint32_t output_reserve(int32_t n_outputs, const llama_batch & batch);
 
     void output_reorder();
 
+    // map the output row index `i` to batch index
+    int64_t output_resolve_row(int32_t i) const;
+
     //
     // graph
     //
@@ -213,6 +236,8 @@ public:
     ggml_cgraph * graph_reserve(
         uint32_t n_tokens, uint32_t n_seqs, uint32_t n_outputs, const llama_memory_context_i * mctx, bool split_only = false, size_t * sizes = nullptr);
 
+    bool set_sampler(llama_seq_id seq_id, llama_sampler * sampler);
+
 private:
     llm_graph_params graph_params(
                         llm_graph_result * res,
@@ -252,6 +277,31 @@ private:
     size_t  embd_size = 0; // capacity (of floats) for embeddings
     float * embd      = nullptr;
 
+    // TODO: simplify
+    struct sampling_info {
+        std::map<llama_seq_id, llama_sampler *> samplers;
+
+        float       * logits      = nullptr;
+        size_t        logits_size = 0;
+
+        llama_token * sampled      = nullptr;
+        size_t        sampled_size = 0;
+
+        float       * probs        = nullptr;
+        size_t        probs_size   = 0;
+
+        llama_token * candidates   = nullptr;
+        size_t        candidates_size = 0;
+
+        std::vector<uint32_t> logits_count;
+        std::vector<uint32_t> probs_count;
+        std::vector<uint32_t> candidates_count;
+
+        std::vector<llama_token> token_ids_full_vocab;
+    };
+
+    sampling_info sampling;
+
     // sequence embeddings output (map of [n_embd] vectors)
     // populated only when pooling_type != LLAMA_POOLING_TYPE_NONE
     std::map<llama_seq_id, std::vector<float>> embd_seq;
@@ -272,6 +322,8 @@ private:
 
     ggml_backend_sched_ptr sched;
 
+    bool sched_need_reserve = true;
+
     ggml_backend_t backend_cpu = nullptr;
     std::vector<ggml_backend_ptr> backends;
 

llama/llama.cpp/src/llama-cparams.h

@@ -30,10 +30,12 @@ struct llama_cparams {
     bool causal_attn;
     bool offload_kqv;
     bool flash_attn;
+    bool auto_fa;
     bool no_perf;
     bool warmup;
     bool op_offload;
     bool kv_unified;
+    bool pipeline_parallel;
 
     enum llama_pooling_type pooling_type;
 

llama/llama.cpp/src/llama-grammar.cpp

@@ -369,6 +369,44 @@ static void print_rule(
     fprintf(file, "\n");
 }
 
+//
+// Regex utilities
+//
+
+size_t llama_grammar_trigger_pattern::find(const std::string & input) const {
+    auto find_start_pos = [](const std::smatch & match) {
+        // get from the first matched capturing group to the end of the string
+        size_t start = std::string::npos;
+        for (auto i = 1u; i < match.size(); i++) {
+            if (match.length(i) > 0) {
+                start = match.position(i);
+                break;
+            }
+        }
+        if (start == std::string::npos) {
+            start = match.position(0);
+        }
+        return start;
+    };
+
+    if (!pattern.empty() && pattern.front() == '^' && pattern.back() == '$') {
+        // match against the entire input
+        std::smatch match;
+        if (std::regex_match(input, match, regex)) {
+            return find_start_pos(match);
+        }
+    }
+
+    // search anywhere
+    std::smatch match;
+    if (std::regex_search(input, match, regex)) {
+        return find_start_pos(match);
+    }
+
+    return std::string::npos;
+}
+
+
 //
 // implementation
 //
@@ -1321,21 +1359,10 @@ void llama_grammar_accept_impl(struct llama_grammar & grammar, llama_token token
             grammar.trigger_buffer_positions.push_back(std::make_pair(token, position));
             grammar.trigger_buffer += piece;
 
-            std::smatch match;
             for (const auto & trigger_pattern : grammar.trigger_patterns) {
-                if (std::regex_match(grammar.trigger_buffer, match, trigger_pattern.regex)) {
+                auto start = trigger_pattern.find(grammar.trigger_buffer);
+                if (start != std::string::npos) {
                     grammar.awaiting_trigger = false;
-                    // get from the first matched capturing group to the end of the string
-                    size_t start = std::string::npos;
-                    for (auto i = 1u; i < match.size(); i++) {
-                        if (match.length(i) > 0) {
-                            start = match.position(i);
-                            break;
-                        }
-                    }
-                    if (start == std::string::npos) {
-                        start = match.position(0);
-                    }
 
                     // replay tokens that overlap with [start, end)
                     for (const auto & [tok, tok_pos] : grammar.trigger_buffer_positions) {

llama/llama.cpp/src/llama-grammar.h

@@ -130,6 +130,8 @@ struct llama_grammar_parser {
 struct llama_grammar_trigger_pattern {
     std::string pattern;
     std::regex  regex;
+
+    size_t find(const std::string & input) const;
 };
 
 struct llama_grammar {

llama/llama.cpp/src/llama-graph.cpp

@@ -7,11 +7,13 @@
 #include "llama-kv-cache.h"
 #include "llama-kv-cache-iswa.h"
 #include "llama-memory-hybrid.h"
+#include "llama-memory-hybrid-iswa.h"
 #include "llama-memory-recurrent.h"
 
 #include <cassert>
 #include <cmath>
 #include <cstring>
+#include <unordered_set>
 
 void llm_graph_input_embd::set_input(const llama_ubatch * ubatch) {
     if (ubatch->token) {
@@ -21,7 +23,8 @@ void llm_graph_input_embd::set_input(const llama_ubatch * ubatch) {
     }
 
     if (ubatch->embd) {
-        const int64_t n_embd   = embd->ne[0];
+        GGML_ASSERT(n_embd == embd->ne[0]);
+
         const int64_t n_tokens = ubatch->n_tokens;
 
         ggml_backend_tensor_set(embd, ubatch->embd, 0, n_tokens*n_embd*ggml_element_size(embd));
@@ -31,8 +34,8 @@ void llm_graph_input_embd::set_input(const llama_ubatch * ubatch) {
 bool llm_graph_input_embd::can_reuse(const llm_graph_params & params) {
     bool res = true;
 
-    res &= (!tokens && !params.ubatch.token) || (tokens && tokens->ne[0] == params.ubatch.n_tokens);
-    res &= (!embd   && !params.ubatch.embd)  || (embd   &&   embd->ne[0] == params.ubatch.n_tokens);
+    res &= (!params.ubatch.token) || (tokens && tokens->ne[0] == params.ubatch.n_tokens);
+    res &= (!params.ubatch.embd)  || (embd   &&   embd->ne[1] == params.ubatch.n_tokens);
 
     return res;
 }
@@ -62,7 +65,7 @@ void llm_graph_input_pos::set_input(const llama_ubatch * ubatch) {
 bool llm_graph_input_pos::can_reuse(const llm_graph_params & params) {
     bool res = true;
 
-    res &= pos->ne[0] == params.ubatch.n_tokens;
+    res &= pos->ne[0] == params.ubatch.n_tokens*n_pos_per_embd;
 
     return res;
 }
@@ -95,11 +98,9 @@ void llm_graph_input_pos_bucket::set_input(const llama_ubatch * ubatch) {
 
         int32_t * data = (int32_t *) pos_bucket->data;
 
-        for (int h = 0; h < 1; ++h) {
-            for (int j = 0; j < n_tokens; ++j) {
-                for (int i = 0; i < n_tokens; ++i) {
-                    data[h*(n_tokens*n_tokens) + j*n_tokens + i] = llama_relative_position_bucket(ubatch->pos[i], ubatch->pos[j], hparams.n_rel_attn_bkts, true);
-                }
+        for (int j = 0; j < n_tokens; ++j) {
+            for (int i = 0; i < n_tokens; ++i) {
+                data[j*n_tokens + i] = llama_relative_position_bucket(ubatch->pos[i], ubatch->pos[j], hparams.n_rel_attn_bkts, true);
             }
         }
     }
@@ -322,34 +323,32 @@ void llm_graph_input_attn_no_cache::set_input(const llama_ubatch * ubatch) {
     const int64_t n_tokens = ubatch->n_tokens;
 
     const auto fill_mask = [&](float * data, int n_swa, llama_swa_type swa_type) {
-        for (int h = 0; h < 1; ++h) {
-            for (int i1 = 0; i1 < n_tokens; ++i1) {
-                const llama_seq_id s1 = ubatch->seq_id[i1][0];
-                const llama_pos    p1 = ubatch->pos[i1];
+        for (int i1 = 0; i1 < n_tokens; ++i1) {
+            const llama_seq_id s1 = ubatch->seq_id[i1][0];
+            const llama_pos    p1 = ubatch->pos[i1];
 
-                const uint64_t idst = h*(n_kv*n_tokens) + i1*n_kv;
+            const uint64_t idst = i1*n_kv;
 
-                for (int i0 = 0; i0 < n_tokens; ++i0) {
-                    const llama_seq_id s0 = ubatch->seq_id[i0][0];
-                    const llama_pos p0    = ubatch->pos[i0];
+            for (int i0 = 0; i0 < n_tokens; ++i0) {
+                const llama_seq_id s0 = ubatch->seq_id[i0][0];
+                const llama_pos p0    = ubatch->pos[i0];
 
-                    // mask different sequences
-                    if (s0 != s1) {
-                        continue;
-                    }
-
-                    // mask future tokens
-                    if (cparams.causal_attn && p0 > p1) {
-                        continue;
-                    }
-
-                    // apply SWA if any
-                    if (llama_hparams::is_masked_swa(n_swa, swa_type, p0, p1)) {
-                        continue;
-                    }
-
-                    data[idst + i0] = hparams.use_alibi ? -std::abs(p0 - p1) : 0.0f;
+                // mask different sequences
+                if (s0 != s1) {
+                    continue;
                 }
+
+                // mask future tokens
+                if (cparams.causal_attn && p0 > p1) {
+                    continue;
+                }
+
+                // apply SWA if any
+                if (llama_hparams::is_masked_swa(n_swa, swa_type, p0, p1)) {
+                    continue;
+                }
+
+                data[idst + i0] = hparams.use_alibi ? -std::abs(p0 - p1) : 0.0f;
             }
         }
     };
@@ -408,6 +407,27 @@ bool llm_graph_input_attn_kv::can_reuse(const llm_graph_params & params) {
     return res;
 }
 
+void llm_graph_input_attn_k::set_input(const llama_ubatch * ubatch) {
+    mctx->set_input_k_idxs(self_k_idxs, ubatch);
+
+    mctx->set_input_kq_mask(self_kq_mask, ubatch, cparams.causal_attn);
+}
+
+bool llm_graph_input_attn_k::can_reuse(const llm_graph_params & params) {
+    const auto * mctx = static_cast<const llama_kv_cache_context *>(params.mctx);
+
+    this->mctx = mctx;
+
+    bool res = true;
+
+    res &= self_k_idxs->ne[0] == params.ubatch.n_tokens;
+
+    res &= self_kq_mask->ne[0] == mctx->get_n_kv();
+    res &= self_kq_mask->ne[1] == params.ubatch.n_tokens;
+
+    return res;
+}
+
 void llm_graph_input_attn_kv_iswa::set_input(const llama_ubatch * ubatch) {
     mctx->get_base()->set_input_k_idxs(self_k_idxs, ubatch);
     mctx->get_base()->set_input_v_idxs(self_v_idxs, ubatch);
@@ -453,27 +473,19 @@ void llm_graph_input_attn_cross::set_input(const llama_ubatch * ubatch) {
 
     float * data = (float *) cross_kq_mask->data;
 
-    for (int h = 0; h < 1; ++h) {
-        for (int i = 0; i < n_tokens; ++i) {
-            for (int j = 0; j < n_enc; ++j) {
-                float f = -INFINITY;
+    for (int i = 0; i < n_tokens; ++i) {
+        for (int j = 0; j < n_enc; ++j) {
+            float f = -INFINITY;
 
-                for (int s = 0; s < ubatch->n_seq_id[i]; ++s) {
-                    const llama_seq_id seq_id = ubatch->seq_id[i][s];
+            for (int s = 0; s < ubatch->n_seq_id[i]; ++s) {
+                const llama_seq_id seq_id = ubatch->seq_id[i][s];
 
-                    if (cross->seq_ids_enc[j].find(seq_id) != cross->seq_ids_enc[j].end()) {
-                        f = 0.0f;
-                    }
+                if (cross->seq_ids_enc[j].find(seq_id) != cross->seq_ids_enc[j].end()) {
+                    f = 0.0f;
                 }
-
-                data[h*(n_enc*n_tokens) + i*n_enc + j] = f;
             }
-        }
 
-        for (int i = n_tokens; i < n_tokens; ++i) {
-            for (int j = 0; j < n_enc; ++j) {
-                data[h*(n_enc*n_tokens) + i*n_enc + j] = -INFINITY;
-            }
+            data[i*n_enc + j] = f;
         }
     }
 }
@@ -521,6 +533,113 @@ bool llm_graph_input_mem_hybrid::can_reuse(const llm_graph_params & params) {
     return res;
 }
 
+void llm_graph_input_mem_hybrid_iswa::set_input(const llama_ubatch * ubatch) {
+    const auto * attn_ctx = mctx->get_attn();
+
+    // base tensors may not be allocated if there are no non-SWA attention layers
+    if (inp_attn->self_k_idxs && inp_attn->self_k_idxs->buffer) {
+        attn_ctx->get_base()->set_input_k_idxs(inp_attn->self_k_idxs, ubatch);
+        attn_ctx->get_base()->set_input_v_idxs(inp_attn->self_v_idxs, ubatch);
+
+        attn_ctx->get_base()->set_input_kq_mask(inp_attn->self_kq_mask, ubatch, cparams.causal_attn);
+    }
+
+    // swa tensors may not be allocated if there are no SWA attention layers
+    if (inp_attn->self_k_idxs_swa && inp_attn->self_k_idxs_swa->buffer) {
+        attn_ctx->get_swa()->set_input_k_idxs(inp_attn->self_k_idxs_swa, ubatch);
+        attn_ctx->get_swa()->set_input_v_idxs(inp_attn->self_v_idxs_swa, ubatch);
+
+        attn_ctx->get_swa()->set_input_kq_mask(inp_attn->self_kq_mask_swa, ubatch, cparams.causal_attn);
+    }
+
+    const int64_t n_rs = mctx->get_recr()->get_n_rs();
+
+    if (inp_rs->s_copy) {
+        GGML_ASSERT(ggml_backend_buffer_is_host(inp_rs->s_copy->buffer));
+        int32_t * data = (int32_t *) inp_rs->s_copy->data;
+
+        // assuming copy destinations ALWAYS happen ONLY on the cells between head and head+n
+        for (uint32_t i = 0; i < n_rs; ++i) {
+            data[i] = mctx->get_recr()->s_copy(i);
+        }
+    }
+}
+
+bool llm_graph_input_mem_hybrid_iswa::can_reuse(const llm_graph_params & params) {
+    const auto * mctx = static_cast<const llama_memory_hybrid_iswa_context *>(params.mctx);
+
+    this->mctx = mctx;
+
+    bool res = true;
+
+    const auto * attn_ctx = mctx->get_attn();
+
+    // base tensors may not be allocated if there are no non-SWA attention layers
+    if (inp_attn->self_k_idxs && inp_attn->self_k_idxs->buffer) {
+        res &= inp_attn->self_k_idxs->ne[0] == params.ubatch.n_tokens;
+      //res &= inp_attn->self_v_idxs->ne[0] == params.ubatch.n_tokens; // TODO: need to move this to the unified cache and check there
+
+        res &= inp_attn->self_kq_mask->ne[0] == attn_ctx->get_base()->get_n_kv();
+        res &= inp_attn->self_kq_mask->ne[1] == params.ubatch.n_tokens;
+    }
+
+    // swa tensors may not be allocated if there are no SWA attention layers
+    if (inp_attn->self_k_idxs_swa && inp_attn->self_k_idxs_swa->buffer) {
+        res &= inp_attn->self_k_idxs_swa->ne[0] == params.ubatch.n_tokens;
+      //res &= inp_attn->self_v_idxs_swa->ne[0] == params.ubatch.n_tokens; // TODO: need to move this to the unified cache and check there
+
+        res &= inp_attn->self_kq_mask_swa->ne[0] == attn_ctx->get_swa()->get_n_kv();
+        res &= inp_attn->self_kq_mask_swa->ne[1] == params.ubatch.n_tokens;
+    }
+
+    res &= inp_rs->s_copy->ne[0] == mctx->get_recr()->get_n_rs();
+
+    res &= inp_rs->s_copy_main->ne[0]  == params.ubatch.n_seqs;
+    res &= inp_rs->s_copy_extra->ne[0] == mctx->get_recr()->get_n_rs() - params.ubatch.n_seqs;
+
+    res &= inp_rs->head == mctx->get_recr()->get_head();
+    res &= inp_rs->rs_z == mctx->get_recr()->get_rs_z();
+
+    return res;
+}
+
+void llm_graph_input_sampling::set_input(const llama_ubatch * ubatch) {
+    // set the inputs only for the active samplers in the current ubatch
+    std::unordered_set<llama_seq_id> active_samplers;
+    for (uint32_t i = 0; i < ubatch->n_tokens; i++) {
+        if (ubatch->output[i]) {
+            llama_seq_id seq_id = ubatch->seq_id[i][0];
+            active_samplers.insert(seq_id);
+        }
+    }
+
+    for (auto seq_id : active_samplers) {
+        if (samplers.find(seq_id) == samplers.end()) {
+            continue;
+        }
+
+        auto & sampler = samplers[seq_id];
+
+        if (sampler->iface->backend_set_input) {
+            sampler->iface->backend_set_input(sampler);
+        }
+    }
+}
+
+bool llm_graph_input_sampling::can_reuse(const llm_graph_params & params) {
+    if (samplers.size() != params.samplers.size()) {
+        return false;
+    }
+
+    for (const auto & [seq_id, sampler] : params.samplers) {
+        if (samplers[seq_id] != sampler) {
+            return false;
+        }
+    }
+
+    return true;
+}
+
 //
 // llm_graph_result
 //
@@ -537,10 +656,15 @@ int64_t llm_graph_result::get_max_nodes() const {
 }
 
 void llm_graph_result::reset() {
-    t_tokens      = nullptr;
+    t_inp_tokens  = nullptr;
+    t_inp_embd    = nullptr;
     t_logits      = nullptr;
     t_embd        = nullptr;
     t_embd_pooled = nullptr;
+    t_sampled.clear();
+    t_sampled_probs.clear();
+    t_sampled_logits.clear();
+    t_candidates.clear();
 
     params = {};
 
@@ -565,6 +689,38 @@ void llm_graph_result::set_inputs(const llama_ubatch * ubatch) {
     }
 }
 
+void llm_graph_result::set_outputs() {
+    if (t_logits != nullptr) {
+        ggml_set_output(t_logits);
+    }
+    if (t_embd != nullptr) {
+        ggml_set_output(t_embd);
+    }
+    if (t_embd_pooled != nullptr) {
+        ggml_set_output(t_embd_pooled);
+    }
+    for (auto & [seq_id, t] : t_sampled) {
+        if (t != nullptr) {
+            ggml_set_output(t);
+        }
+    }
+    for (auto & [seq_id, t] : t_sampled_probs) {
+        if (t != nullptr) {
+            ggml_set_output(t);
+        }
+    }
+    for (auto & [seq_id, t] : t_sampled_logits) {
+        if (t != nullptr) {
+            ggml_set_output(t);
+        }
+    }
+    for (auto & [seq_id, t] : t_candidates) {
+        if (t != nullptr) {
+            ggml_set_output(t);
+        }
+    }
+}
+
 bool llm_graph_result::can_reuse(const llm_graph_params & params) {
     if (!this->params.allow_reuse(params)) {
         if (debug > 1) {
@@ -646,6 +802,7 @@ llm_graph_context::llm_graph_context(const llm_graph_params & params) :
     loras            (params.loras),
     mctx             (params.mctx),
     cross            (params.cross),
+    samplers         (params.samplers),
     cb_func          (params.cb),
     res              (params.res),
     ctx0             (res->get_ctx()),
@@ -1204,17 +1361,29 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
 
 // input embeddings with optional lora
 ggml_tensor * llm_graph_context::build_inp_embd(ggml_tensor * tok_embd) const {
-    const int64_t n_embd = hparams.n_embd_inp();
+    const int64_t n_embd_inp = hparams.n_embd_inp();
+    const int64_t n_embd     = hparams.n_embd;
 
-    auto inp = std::make_unique<llm_graph_input_embd>();
+    assert(n_embd_inp >= n_embd);
 
-    ggml_tensor * cur = nullptr;
+    auto inp = std::make_unique<llm_graph_input_embd>(n_embd_inp);
 
-    if (ubatch.token) {
-        inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ubatch.n_tokens);
-        //cb(inp->tokens, "inp_tokens", -1);
-        ggml_set_input(inp->tokens);
-        res->t_tokens = inp->tokens;
+    inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ubatch.n_tokens);
+    cb(inp->tokens, "inp_tokens", -1);
+    ggml_set_input(inp->tokens);
+    res->t_inp_tokens = inp->tokens;
+
+    inp->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd_inp, ubatch.n_tokens);
+    cb(inp->embd, "inp_embd", -1);
+    ggml_set_input(inp->embd);
+
+    // select one of the 2 inputs, based on the batch contents
+    // ref: https://github.com/ggml-org/llama.cpp/pull/18550
+    std::array<ggml_tensor *, 2> inps;
+
+    // token embeddings path (ubatch.token != nullptr)
+    {
+        auto & cur = inps[0];
 
         cur = ggml_get_rows(ctx0, tok_embd, inp->tokens);
 
@@ -1235,22 +1404,43 @@ ggml_tensor * llm_graph_context::build_inp_embd(ggml_tensor * tok_embd) const {
 
             cur = ggml_add(ctx0, cur, inpL_delta);
         }
-    } else {
-        inp->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, ubatch.n_tokens);
-        ggml_set_input(inp->embd);
+
+        if (n_embd_inp != n_embd) {
+            cur = ggml_pad(ctx0, cur, hparams.n_embd_inp() - n_embd, 0, 0, 0);
+        }
+    }
+
+    // vector embeddings path (ubatch.embd != nullptr)
+    {
+        auto & cur = inps[1];
 
         cur = inp->embd;
     }
 
+    assert(ggml_are_same_shape (inps[0], inps[1]));
+    assert(ggml_are_same_stride(inps[0], inps[1]));
+
+    ggml_tensor * cur = ggml_build_forward_select(gf, inps.data(), inps.size(), ubatch.token ? 0 : 1);
+
+    if (n_embd_inp != n_embd) {
+        cur = ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0);
+    }
+
+    res->t_inp_embd = cur;
+
     // For Granite architecture
     if (hparams.f_embedding_scale != 0.0f) {
         cur = ggml_scale(ctx0, cur, hparams.f_embedding_scale);
     }
 
-    cb(cur, "inp_embd", -1);
+    cb(cur, "embd", -1);
 
     res->add_input(std::move(inp));
 
+    // make sure the produced embeddings are immediately materialized in the ggml graph
+    // ref: https://github.com/ggml-org/llama.cpp/pull/18599
+    ggml_build_forward_expand(gf, cur);
+
     return cur;
 }
 
@@ -1342,7 +1532,7 @@ ggml_tensor * llm_graph_context::build_inp_cross_embd() const {
     //}
 
     const auto n_embd = !cross->v_embd.empty() ? cross->n_embd : hparams.n_embd_inp();
-    const auto n_enc  = !cross->v_embd.empty() ? cross->n_enc : hparams.n_ctx_train;
+    const auto n_enc  = !cross->v_embd.empty() ? cross->n_enc  : hparams.n_ctx_train;
 
     cur = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_enc);
     ggml_set_input(cur);
@@ -1440,6 +1630,11 @@ ggml_tensor * llm_graph_context::build_attn_mha(
                                   hparams.attn_soft_cap ? hparams.f_attn_logit_softcapping : 0.0f);
         cb(cur, LLAMA_TENSOR_NAME_FATTN, il);
 
+        if (!cparams.offload_kqv) {
+            // all nodes between the KV store and the attention output are run on the CPU
+            ggml_backend_sched_set_tensor_backend(sched, cur, backend_cpu);
+        }
+
         ggml_flash_attn_ext_add_sinks(cur, sinks);
         ggml_flash_attn_ext_set_prec (cur, GGML_PREC_F32);
 
@@ -1649,9 +1844,11 @@ ggml_tensor * llm_graph_context::build_attn(
         ggml_tensor * v_cur,
         ggml_tensor * kq_b,
         ggml_tensor * sinks,
-        ggml_tensor * v_mla,
+        ggml_tensor * v_mla, // TODO: remove
             float     kq_scale,
             int       il) const {
+    GGML_ASSERT(v_mla == nullptr);
+
     // these nodes are added to the graph together so that they are not reordered
     // by doing so, the number of splits in the graph is reduced
     // expand k later to enable rope fusion which directly writes into k-v cache
@@ -1694,6 +1891,93 @@ ggml_tensor * llm_graph_context::build_attn(
     return cur;
 }
 
+static std::unique_ptr<llm_graph_input_attn_k> build_attn_inp_k_impl(
+           ggml_context * ctx0,
+     const llama_ubatch & ubatch,
+    const llama_hparams & hparams,
+    const llama_cparams & cparams,
+    const llama_kv_cache_context * mctx_cur) {
+
+    auto inp = std::make_unique<llm_graph_input_attn_k>(hparams, cparams, mctx_cur);
+
+    {
+        GGML_ASSERT(hparams.swa_type == LLAMA_SWA_TYPE_NONE && "Use llama_kv_cache_iswa for SWA");
+
+        const auto n_kv     = mctx_cur->get_n_kv();
+        const auto n_tokens = ubatch.n_tokens;
+        const auto n_stream = cparams.kv_unified ? 1 : ubatch.n_seqs_unq;
+
+        inp->self_k_idxs = mctx_cur->build_input_k_idxs(ctx0, ubatch);
+
+        inp->self_kq_mask = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_kv, n_tokens/n_stream, 1, n_stream);
+        ggml_set_input(inp->self_kq_mask);
+
+        inp->self_kq_mask_cnv = cparams.flash_attn ? ggml_cast(ctx0, inp->self_kq_mask, GGML_TYPE_F16) : inp->self_kq_mask;
+    }
+
+    return inp;
+}
+
+llm_graph_input_attn_k * llm_graph_context::build_attn_inp_k() const {
+    const auto * mctx_cur = static_cast<const llama_kv_cache_context *>(mctx);
+
+    auto inp = build_attn_inp_k_impl(ctx0, ubatch, hparams, cparams, mctx_cur);
+
+    return (llm_graph_input_attn_k *) res->add_input(std::move(inp));
+}
+
+ggml_tensor * llm_graph_context::build_attn(
+        llm_graph_input_attn_k * inp,
+        ggml_tensor * wo,
+        ggml_tensor * wo_b,
+        ggml_tensor * q_cur,
+        ggml_tensor * k_cur,
+        ggml_tensor * v_cur,
+        ggml_tensor * kq_b,
+        ggml_tensor * sinks,
+        ggml_tensor * v_mla,
+            float     kq_scale,
+            int       il) const {
+    // these nodes are added to the graph together so that they are not reordered
+    // by doing so, the number of splits in the graph is reduced
+    // expand k later to enable rope fusion which directly writes into k-v cache
+    ggml_build_forward_expand(gf, q_cur);
+    ggml_build_forward_expand(gf, v_cur);
+    ggml_build_forward_expand(gf, k_cur);
+
+    const auto * mctx_cur = inp->mctx;
+
+    // store to KV cache
+    {
+        const auto & k_idxs = inp->get_k_idxs();
+
+        ggml_build_forward_expand(gf, mctx_cur->cpy_k(ctx0, k_cur, k_idxs, il));
+    }
+
+    const auto & kq_mask = inp->get_kq_mask();
+
+    ggml_tensor * q = q_cur;
+    ggml_tensor * k = mctx_cur->get_k(ctx0, il);
+    ggml_tensor * v = ggml_view_4d(ctx0, k, v_cur->ne[0], k->ne[1], k->ne[2], k->ne[3], k->nb[1], k->nb[2], k->nb[3], 0);
+
+    ggml_tensor * cur = build_attn_mha(q, k, v, kq_b, kq_mask, sinks, v_mla, kq_scale, il);
+    cb(cur, "kqv_out", il);
+
+    if (wo) {
+        cur = build_lora_mm(wo, cur);
+        if (arch == LLM_ARCH_GLM4 || arch == LLM_ARCH_GLM4_MOE) {
+            // GLM4 and GLM4_MOE seem to have numerical issues with half-precision accumulators
+            ggml_mul_mat_set_prec(cur, GGML_PREC_F32);
+        }
+    }
+
+    if (wo_b) {
+        cur = ggml_add(ctx0, cur, wo_b);
+    }
+
+    return cur;
+}
+
 ggml_tensor * llm_graph_context::build_attn(
         llm_graph_input_attn_kv_iswa * inp,
         ggml_tensor * wo,
@@ -1834,8 +2118,10 @@ llm_graph_input_attn_kv_iswa * llm_graph_context::build_attn_inp_kv_iswa() const
 
         inp->self_kq_mask = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_kv, n_tokens/n_stream, 1, n_stream);
         ggml_set_input(inp->self_kq_mask);
+        ggml_set_name(inp->self_kq_mask, "self_kq_mask");
 
         inp->self_kq_mask_cnv = cparams.flash_attn ? ggml_cast(ctx0, inp->self_kq_mask, GGML_TYPE_F16) : inp->self_kq_mask;
+        ggml_set_name(inp->self_kq_mask_cnv, "self_kq_mask_cnv");
     }
 
     {
@@ -1848,8 +2134,10 @@ llm_graph_input_attn_kv_iswa * llm_graph_context::build_attn_inp_kv_iswa() const
 
         inp->self_kq_mask_swa = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_kv, n_tokens/n_stream, 1, n_stream);
         ggml_set_input(inp->self_kq_mask_swa);
+        ggml_set_name(inp->self_kq_mask_swa, "self_kq_mask_swa");
 
         inp->self_kq_mask_swa_cnv = cparams.flash_attn ? ggml_cast(ctx0, inp->self_kq_mask_swa, GGML_TYPE_F16) : inp->self_kq_mask_swa;
+        ggml_set_name(inp->self_kq_mask_swa_cnv, "self_kq_mask_swa_cnv");
     }
 
     return (llm_graph_input_attn_kv_iswa *) res->add_input(std::move(inp));
@@ -1985,17 +2273,62 @@ llm_graph_input_mem_hybrid * llm_graph_context::build_inp_mem_hybrid() const {
     return (llm_graph_input_mem_hybrid *) res->add_input(std::move(inp));
 }
 
+llm_graph_input_mem_hybrid_iswa * llm_graph_context::build_inp_mem_hybrid_iswa() const {
+    const auto * mctx_cur = static_cast<const llama_memory_hybrid_iswa_context *>(mctx);
+
+    auto inp_rs = build_rs_inp_impl(ctx0, ubatch, mctx_cur->get_recr());
+
+    // build iswa attention input
+    const auto * attn_ctx = mctx_cur->get_attn();
+
+    auto inp_attn = std::make_unique<llm_graph_input_attn_kv_iswa>(hparams, cparams, attn_ctx);
+
+    const auto n_stream = cparams.kv_unified ? 1 : ubatch.n_seqs_unq;
+
+    {
+        const auto n_kv = attn_ctx->get_base()->get_n_kv();
+
+        inp_attn->self_k_idxs = attn_ctx->get_base()->build_input_k_idxs(ctx0, ubatch);
+        inp_attn->self_v_idxs = attn_ctx->get_base()->build_input_v_idxs(ctx0, ubatch);
+
+        inp_attn->self_kq_mask = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_kv, n_tokens/n_stream, 1, n_stream);
+        ggml_set_input(inp_attn->self_kq_mask);
+
+        inp_attn->self_kq_mask_cnv = cparams.flash_attn ? ggml_cast(ctx0, inp_attn->self_kq_mask, GGML_TYPE_F16) : inp_attn->self_kq_mask;
+    }
+
+    {
+        const auto n_kv = attn_ctx->get_swa()->get_n_kv();
+
+        inp_attn->self_k_idxs_swa = attn_ctx->get_swa()->build_input_k_idxs(ctx0, ubatch);
+        inp_attn->self_v_idxs_swa = attn_ctx->get_swa()->build_input_v_idxs(ctx0, ubatch);
+
+        inp_attn->self_kq_mask_swa = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_kv, n_tokens/n_stream, 1, n_stream);
+        ggml_set_input(inp_attn->self_kq_mask_swa);
+
+        inp_attn->self_kq_mask_swa_cnv = cparams.flash_attn ? ggml_cast(ctx0, inp_attn->self_kq_mask_swa, GGML_TYPE_F16) : inp_attn->self_kq_mask_swa;
+    }
+
+    auto inp = std::make_unique<llm_graph_input_mem_hybrid_iswa>(cparams, std::move(inp_attn), std::move(inp_rs), mctx_cur);
+
+    return (llm_graph_input_mem_hybrid_iswa *) res->add_input(std::move(inp));
+}
+
 void llm_graph_context::build_dense_out(
     ggml_tensor * dense_2,
     ggml_tensor * dense_3) const {
-    if (!cparams.embeddings || dense_2 == nullptr || dense_3 == nullptr) {
+    if (!cparams.embeddings || !(dense_2 || dense_3)) {
         return;
     }
     ggml_tensor * cur = res->t_embd_pooled != nullptr ? res->t_embd_pooled : res->t_embd;
     GGML_ASSERT(cur != nullptr && "missing t_embd_pooled/t_embd");
 
-    cur = ggml_mul_mat(ctx0, dense_2, cur);
-    cur = ggml_mul_mat(ctx0, dense_3, cur);
+    if (dense_2) {
+        cur = ggml_mul_mat(ctx0, dense_2, cur);
+    }
+    if (dense_3) {
+        cur = ggml_mul_mat(ctx0, dense_3, cur);
+    }
     cb(cur, "result_embd_pooled", -1);
     res->t_embd_pooled = cur;
     ggml_build_forward_expand(gf, cur);
@@ -2086,6 +2419,87 @@ void llm_graph_context::build_pooling(
     ggml_build_forward_expand(gf, cur);
 }
 
+void llm_graph_context::build_sampling() const {
+    if (samplers.empty() || !res->t_logits) {
+        return;
+    }
+
+    auto inp_sampling = std::make_unique<llm_graph_input_sampling>(samplers);
+    res->add_input(std::move(inp_sampling));
+
+    std::map<llama_seq_id, int32_t> seq_to_logit_row;
+    int32_t logit_row_idx = 0;
+
+    for (uint32_t i = 0; i < ubatch.n_tokens; i++) {
+        if (ubatch.output[i]) {
+            llama_seq_id seq_id = ubatch.seq_id[i][0];
+            seq_to_logit_row[seq_id] = logit_row_idx;
+            logit_row_idx++;
+        }
+    }
+
+    // res->t_logits will contain logits for all tokens that want the logits calculated (logits=1 or output=1)
+    GGML_ASSERT(res->t_logits != nullptr && "missing t_logits tensor");
+
+    // add a dummy row of logits
+    // this trick makes the graph static, regardless of which samplers are activated
+    // this is important in order to minimize graph reallocations
+    // TODO: use `ggml_build_forward_select()` when available (https://github.com/ggml-org/llama.cpp/pull/18550)
+    ggml_tensor * logits_t = ggml_pad(ctx0, res->t_logits, 0, 1, 0, 0);
+
+    for (const auto & [seq_id, sampler] : samplers) {
+        const auto it = seq_to_logit_row.find(seq_id);
+
+        // inactive samplers always work on the first row
+        const auto row_idx = seq_to_logit_row.find(seq_id) != seq_to_logit_row.end() ? it->second : 0;
+
+        ggml_tensor * logits_seq = ggml_view_1d(ctx0, logits_t, logits_t->ne[0], row_idx * logits_t->nb[1]);
+        ggml_format_name(logits_seq, "logits_seq_%d", seq_id);
+
+        struct llama_sampler_data data = {
+            /*.logits      =*/ logits_seq,
+            /*.probs       =*/ nullptr,
+            /*.sampled     =*/ nullptr,
+            /*.candidates  =*/ nullptr,
+        };
+
+        assert(sampler->iface->backend_apply);
+        sampler->iface->backend_apply(sampler, ctx0, gf, &data);
+
+        if (data.sampled != nullptr) {
+            res->t_sampled[seq_id] = data.sampled;
+            ggml_build_forward_expand(gf, data.sampled);
+        }
+
+        if (data.probs != nullptr) {
+            res->t_sampled_probs[seq_id] = data.probs;
+            ggml_build_forward_expand(gf, data.probs);
+        }
+
+        if (data.logits != nullptr) {
+            res->t_sampled_logits[seq_id] = data.logits;
+            ggml_build_forward_expand(gf, data.logits);
+        }
+
+        if (data.candidates != nullptr) {
+            res->t_candidates[seq_id] = data.candidates;
+            ggml_build_forward_expand(gf, data.candidates);
+        }
+    }
+
+    // TODO: Call llama_sampler_accept_ggml after all samplers have been applied.
+    /*
+    for (const auto & [seq_id, sampler] : samplers) {
+        if (auto it = res->t_sampled.find(seq_id); it != res->t_sampled.end()) {
+            ggml_tensor * selected_token = it->second;
+            if (selected_token != nullptr) {
+                llama_sampler_accept_ggml(sampler, ctx0, gf, selected_token);
+            }
+        }
+    }
+    */
+}
+
 int32_t llama_relative_position_bucket(llama_pos x, llama_pos y, uint64_t n_buckets, bool bidirectional) {
     // TODO move to hparams if a T5 variant appears that uses a different value
     const int64_t max_distance = 128;

llama/llama.cpp/src/llama-graph.h

@@ -10,6 +10,7 @@
 #include <memory>
 #include <set>
 #include <functional>
+#include <map>
 
 struct ggml_cgraph;
 struct ggml_context;
@@ -23,6 +24,7 @@ class llama_kv_cache_context;
 class llama_kv_cache_iswa_context;
 class llama_memory_recurrent_context;
 class llama_memory_hybrid_context;
+class llama_memory_hybrid_iswa_context;
 
 // certain models (typically multi-modal) can produce different types of graphs
 enum llm_graph_type {
@@ -104,7 +106,7 @@ using llm_graph_input_ptr = std::unique_ptr<llm_graph_input_i>;
 
 class llm_graph_input_embd : public llm_graph_input_i {
 public:
-    llm_graph_input_embd()          = default;
+    llm_graph_input_embd(int64_t n_embd) : n_embd(n_embd) {}
     virtual ~llm_graph_input_embd() = default;
 
     void set_input(const llama_ubatch * ubatch) override;
@@ -113,6 +115,8 @@ public:
 
     ggml_tensor * tokens = nullptr; // I32 [n_batch]
     ggml_tensor * embd   = nullptr; // F32 [n_embd, n_batch]
+
+    const int64_t n_embd = 0;
 };
 
 class llm_graph_input_pos : public llm_graph_input_i {
@@ -313,6 +317,39 @@ public:
     const llama_kv_cache_context * mctx;
 };
 
+// V-less input for the KV cache
+// ref: https://github.com/ggml-org/llama.cpp/pull/19067
+class llm_graph_input_attn_k : public llm_graph_input_i {
+public:
+    llm_graph_input_attn_k(
+            const llama_hparams & hparams,
+            const llama_cparams & cparams,
+            const llama_kv_cache_context * mctx) :
+        hparams(hparams),
+        cparams(cparams),
+        mctx(mctx) {
+    }
+    ~llm_graph_input_attn_k() = default;
+
+    void set_input(const llama_ubatch * ubatch) override;
+
+    bool can_reuse(const llm_graph_params & params) override;
+
+    ggml_tensor * get_k_idxs() const { return self_k_idxs; }
+
+    ggml_tensor * get_kq_mask() const { return self_kq_mask_cnv; }
+
+    ggml_tensor * self_k_idxs = nullptr; // I64 [n_batch]
+
+    ggml_tensor * self_kq_mask     = nullptr; // F32 [n_kv, n_batch/n_stream, 1, n_stream]
+    ggml_tensor * self_kq_mask_cnv = nullptr; //     [n_kv, n_batch/n_stream, 1, n_stream]
+
+    const llama_hparams hparams;
+    const llama_cparams cparams;
+
+    const llama_kv_cache_context * mctx;
+};
+
 class llm_graph_input_attn_kv_iswa : public llm_graph_input_i {
 public:
     llm_graph_input_attn_kv_iswa(
@@ -396,6 +433,46 @@ public:
     const llama_memory_hybrid_context * mctx;
 };
 
+class llm_graph_input_mem_hybrid_iswa : public llm_graph_input_i {
+public:
+    llm_graph_input_mem_hybrid_iswa(
+            const llama_cparams & cparams,
+            std::unique_ptr<llm_graph_input_attn_kv_iswa> inp_attn,
+            std::unique_ptr<llm_graph_input_rs>          inp_rs,
+            const llama_memory_hybrid_iswa_context *     mctx) :
+        inp_attn(std::move(inp_attn)),
+        inp_rs(std::move(inp_rs)),
+        cparams(cparams),
+        mctx(mctx) { }
+    virtual ~llm_graph_input_mem_hybrid_iswa() = default;
+
+    void set_input(const llama_ubatch * ubatch) override;
+
+    bool can_reuse(const llm_graph_params & params) override;
+
+    std::unique_ptr<llm_graph_input_attn_kv_iswa> inp_attn;
+    std::unique_ptr<llm_graph_input_rs>          inp_rs;
+
+    llm_graph_input_attn_kv_iswa * get_attn() const { return inp_attn.get(); }
+    llm_graph_input_rs           * get_recr() const { return inp_rs.get(); }
+
+    const llama_cparams cparams;
+
+    const llama_memory_hybrid_iswa_context * mctx;
+};
+
+class llm_graph_input_sampling : public llm_graph_input_i {
+public:
+    llm_graph_input_sampling(std::map<llama_seq_id, llama_sampler *> samplers) :
+        samplers(std::move(samplers)) { }
+    virtual ~llm_graph_input_sampling() = default;
+
+    void set_input(const llama_ubatch * ubatch) override;
+    bool can_reuse(const llm_graph_params & params) override;
+
+    std::map<llama_seq_id, llama_sampler *> samplers;
+};
+
 //
 // llm_graph_result
 //
@@ -429,6 +506,23 @@ struct llm_graph_params {
     const llama_memory_context_i * mctx;
     const llama_cross            * cross;
 
+    std::map<llama_seq_id, llama_sampler *> samplers;
+
+    static bool samplers_equal(
+          const std::map<llama_seq_id, llama_sampler *> & lhs,
+          const std::map<llama_seq_id, llama_sampler *> & rhs) {
+        if (lhs.size() != rhs.size()) {
+            return false;
+        }
+        for (const auto & [seq_id, sampler] : lhs) {
+            auto it = rhs.find(seq_id);
+            if (it == rhs.end() || it->second != sampler) {
+                return false;
+            }
+        }
+        return true;
+    }
+
     uint32_t n_outputs;
 
     llm_graph_cb cb;
@@ -468,15 +562,36 @@ struct llm_graph_params {
             return false;
         }
 
+        if (n_outputs != other.n_outputs) {
+            return false;
+        }
+
+        if (!samplers_equal(samplers, other.samplers)) {
+            return false;
+        }
+
+        if (samplers.size() > 0) {
+            if (!ubatch.data || !other.ubatch.data) {
+                return false;
+            }
+
+            // check that the outputs are the same for all samplers
+            for (uint32_t i = 0; i < ubatch.n_tokens; ++i) {
+                if (ubatch.output[i]    != other.ubatch.output[i] ||
+                    ubatch.seq_id[i][0] != other.ubatch.seq_id[i][0]) {
+                    return false;
+                }
+            }
+        }
+
         return
             cparams.embeddings  == other.cparams.embeddings  &&
             cparams.causal_attn == other.cparams.causal_attn &&
-            arch      == other.arch  &&
-            gtype     == other.gtype &&
-            cvec      == other.cvec  &&
-            loras     == other.loras &&
-            cross     == other.cross &&
-            n_outputs == other.n_outputs;
+            arch  == other.arch  &&
+            gtype == other.gtype &&
+            cvec  == other.cvec  &&
+            loras == other.loras &&
+            cross == other.cross;
     }
 };
 
@@ -486,7 +601,7 @@ public:
 
     virtual ~llm_graph_result() = default;
 
-    ggml_tensor * get_tokens()      const { return t_tokens; }
+    ggml_tensor * get_inp_tokens()  const { return t_inp_tokens; }
     ggml_tensor * get_logits()      const { return t_logits; }
     ggml_tensor * get_embd()        const { return t_embd; }
     ggml_tensor * get_embd_pooled() const { return t_embd_pooled; }
@@ -499,6 +614,7 @@ public:
     void reset();
 
     void set_inputs(const llama_ubatch * ubatch);
+    void set_outputs();
 
     // try to update the existing graph result using the new graph parameters in order to reuse it
     // this can only be done if we determine that the resulting graph using the new graph parameters
@@ -512,11 +628,17 @@ public:
     void set_params(const llm_graph_params & params);
 
     // important graph nodes
-    ggml_tensor * t_tokens      = nullptr;
+    ggml_tensor * t_inp_tokens  = nullptr;
+    ggml_tensor * t_inp_embd    = nullptr; // [n_embd_inp, n_tokens]
     ggml_tensor * t_logits      = nullptr;
     ggml_tensor * t_embd        = nullptr;
     ggml_tensor * t_embd_pooled = nullptr;
 
+    std::map<llama_seq_id, ggml_tensor*> t_sampled_logits;
+    std::map<llama_seq_id, ggml_tensor*> t_candidates;
+    std::map<llama_seq_id, ggml_tensor*> t_sampled;
+    std::map<llama_seq_id, ggml_tensor*> t_sampled_probs;
+
     std::vector<llm_graph_input_ptr> inputs;
 
     ggml_context_ptr ctx_compute;
@@ -592,6 +714,8 @@ struct llm_graph_context {
     const llama_memory_context_i * mctx;
     const llama_cross            * cross;
 
+    std::map<llama_seq_id, llama_sampler *> samplers;
+
     const llm_graph_cb & cb_func;
 
     llm_graph_result * res;
@@ -742,6 +866,21 @@ struct llm_graph_context {
             ggml_tensor * v_cur, // [n_embd_head_v, n_head_v, n_tokens]
             ggml_tensor * kq_b,
             ggml_tensor * sinks, // [n_head_q]
+            ggml_tensor * v_mla, // [n_embd_head_v_mla, n_embd_head_v, n_head_v] // TODO: remove
+                  float   kq_scale,
+                    int   il) const;
+
+    llm_graph_input_attn_k  * build_attn_inp_k() const;
+
+    ggml_tensor * build_attn(
+            llm_graph_input_attn_k * inp,
+            ggml_tensor * wo,
+            ggml_tensor * wo_b,
+            ggml_tensor * q_cur, // [n_embd_head_q, n_head_q, n_tokens]
+            ggml_tensor * k_cur, // [n_embd_head_k, n_head_k, n_tokens]
+            ggml_tensor * v_cur, // [n_embd_head_v, n_head_v, n_tokens]
+            ggml_tensor * kq_b,
+            ggml_tensor * sinks, // [n_head_q]
             ggml_tensor * v_mla, // [n_embd_head_v_mla, n_embd_head_v, n_head_v]
                   float   kq_scale,
                     int   il) const;
@@ -822,6 +961,8 @@ struct llm_graph_context {
 
     llm_graph_input_mem_hybrid * build_inp_mem_hybrid() const;
 
+    llm_graph_input_mem_hybrid_iswa * build_inp_mem_hybrid_iswa() const;
+
     //
     // pooling
     //
@@ -832,6 +973,12 @@ struct llm_graph_context {
             ggml_tensor * cls_out,
             ggml_tensor * cls_out_b) const;
 
+    //
+    // sampling (backend sampling)
+    //
+
+    void build_sampling() const;
+
     //
     // dense (out)
     //

llama/llama.cpp/src/llama-hparams.cpp

@@ -72,6 +72,10 @@ uint32_t llama_hparams::n_embd_inp() const {
     return n_embd_inp;
 }
 
+uint32_t llama_hparams::n_embd_out() const {
+    return n_embd_out_impl > 0 ? n_embd_out_impl : n_embd;
+}
+
 uint32_t llama_hparams::n_embd_k_gqa(uint32_t il) const {
     const uint32_t n_head_kv = this->n_head_kv(il);
 
@@ -179,6 +183,21 @@ bool llama_hparams::is_swa(uint32_t il) const {
     GGML_ABORT("fatal error");
 }
 
+bool llama_hparams::is_mla() const {
+    assert((n_embd_head_k_mla_impl == 0 && n_embd_head_v_mla_impl == 0) ||
+           (n_embd_head_k_mla_impl != 0 && n_embd_head_v_mla_impl != 0));
+
+    return n_embd_head_k_mla_impl != 0 && n_embd_head_v_mla_impl != 0;
+}
+
+uint32_t llama_hparams::n_embd_head_k_mla() const {
+    return is_mla() ? n_embd_head_k_mla_impl : n_embd_head_k;
+}
+
+uint32_t llama_hparams::n_embd_head_v_mla() const {
+    return is_mla() ? n_embd_head_v_mla_impl : n_embd_head_v;
+}
+
 bool llama_hparams::has_kv(uint32_t il) const {
     if (n_layer_kv_from_start >= 0) {
         if (il < (uint32_t) n_layer_kv_from_start) {
@@ -204,42 +223,6 @@ uint32_t llama_hparams::n_layer_kv() const {
     return res;
 }
 
-bool llama_hparams::is_masked_swa(uint32_t n_swa, llama_swa_type swa_type, llama_pos p0, llama_pos p1) {
-    assert(p0 >= 0 && p1 >= 0);
-
-    switch (swa_type) {
-        case LLAMA_SWA_TYPE_NONE:
-            {
-            } break;
-        case LLAMA_SWA_TYPE_STANDARD:
-            {
-                if (p1 - p0 >= (int32_t) n_swa) {
-                    return true;
-                }
-            } break;
-        case LLAMA_SWA_TYPE_CHUNKED:
-            {
-                const llama_pos pos_chunk_start = (p1 / n_swa) * n_swa;
-
-                if (p0 < pos_chunk_start) {
-                    return true;
-                }
-            } break;
-        case LLAMA_SWA_TYPE_SYMMETRIC:
-            {
-                const int32_t half_n_swa = (int32_t) n_swa / 2;
-                const int32_t pos_diff = p1 - p0;
-
-                // Mask if outside the symmetric window
-                if (pos_diff < -half_n_swa || pos_diff > half_n_swa) {
-                    return true;
-                }
-            } break;
-    }
-
-    return false;
-}
-
 bool llama_hparams::use_mrope() const {
     return rope_sections[0] > 0 && rope_sections[1] > 0;
 }

llama/llama.cpp/src/llama-hparams.h

@@ -3,6 +3,7 @@
 #include "llama.h"
 
 #include <array>
+#include <cassert>
 
 // bump if necessary
 #define LLAMA_MAX_LAYERS  512
@@ -52,8 +53,8 @@ struct llama_hparams {
     uint32_t n_rel_attn_bkts = 0;
 
     // note: deepseek2 using MLA converts into MQA with larger heads, then decompresses to MHA
-    uint32_t n_embd_head_k_mla = 0;
-    uint32_t n_embd_head_v_mla = 0;
+    uint32_t n_embd_head_k_mla_impl = 0;
+    uint32_t n_embd_head_v_mla_impl = 0;
 
     // for WavTokenizer
     struct llama_hparams_posnet   posnet;
@@ -107,9 +108,9 @@ struct llama_hparams {
 
     float    rope_attn_factor = 1.0f;
     float    rope_freq_base_train;
-    float    rope_freq_base_train_swa;
+    float    rope_freq_base_train_swa  = 10000.0f;
     float    rope_freq_scale_train;
-    float    rope_freq_scale_train_swa;
+    float    rope_freq_scale_train_swa = 1.0f;
 
     uint32_t n_ctx_orig_yarn;
     float    rope_yarn_log_mul = 0.0f;
@@ -125,10 +126,11 @@ struct llama_hparams {
     llama_swa_type swa_type = LLAMA_SWA_TYPE_NONE;
     // the size of the sliding window (0 - no SWA)
     uint32_t n_swa = 0;
-    // if swa_layers[il] == true, then layer il is SWA
-    // if swa_layers[il] == false, then layer il is dense (i.e. non-SWA)
+    // if swa_layers[il] == 1, then layer il is SWA
+    // if swa_layers[il] == 0, then layer il is dense (i.e. non-SWA)
     // by default, all layers are dense
-    std::array<bool, LLAMA_MAX_LAYERS> swa_layers;
+    // note: using uint32_t type for compatibility reason
+    std::array<uint32_t, LLAMA_MAX_LAYERS> swa_layers;
 
     // for State Space Models
     uint32_t ssm_d_conv  = 0;
@@ -163,6 +165,9 @@ struct llama_hparams {
     // for Classifiers
     uint32_t n_cls_out = 1;
 
+    // output embedding dimension (0 = use n_embd)
+    uint32_t n_embd_out_impl = 0;
+
     // llama4 smallthinker
     uint32_t n_moe_layer_step        = 0;
     uint32_t n_no_rope_layer_step    = 4;
@@ -235,6 +240,9 @@ struct llama_hparams {
     // dimension of main + auxiliary input embeddings
     uint32_t n_embd_inp() const;
 
+    // dimension of output embeddings
+    uint32_t n_embd_out() const;
+
     // dimension of key embeddings across all k-v heads
     uint32_t n_embd_k_gqa(uint32_t il = 0) const;
 
@@ -266,15 +274,57 @@ struct llama_hparams {
 
     bool is_swa(uint32_t il) const;
 
+    // note: currently only support if either all or none of the layers are MLA
+    bool is_mla() const;
+
+    uint32_t n_embd_head_k_mla() const;
+    uint32_t n_embd_head_v_mla() const;
+
     bool has_kv(uint32_t il) const;
 
     // number of layers for which has_kv() returns true
     uint32_t n_layer_kv() const;
 
     // note that this function uses different SWA parameters from those in the hparams
+    // note: inlined on purpose for performance reasons
     // TODO: think of a better place for this function
     // TODO: pack the SWA params in a struct?
-    static bool is_masked_swa(uint32_t n_swa, llama_swa_type swa_type, llama_pos p0, llama_pos p1);
+    static bool is_masked_swa(uint32_t n_swa, llama_swa_type swa_type, llama_pos p0, llama_pos p1) {
+        assert(p0 >= 0 && p1 >= 0);
+
+        switch (swa_type) {
+            case LLAMA_SWA_TYPE_NONE:
+                {
+                } break;
+            case LLAMA_SWA_TYPE_STANDARD:
+                {
+                    if (p1 - p0 >= (int32_t) n_swa) {
+                        return true;
+                    }
+                } break;
+            case LLAMA_SWA_TYPE_CHUNKED:
+                {
+                    const llama_pos pos_chunk_start = (p1 / n_swa) * n_swa;
+
+                    if (p0 < pos_chunk_start) {
+                        return true;
+                    }
+                } break;
+            case LLAMA_SWA_TYPE_SYMMETRIC:
+                {
+                    const int32_t half_n_swa = (int32_t) n_swa / 2;
+                    const int32_t pos_diff = p1 - p0;
+
+                    // Mask if outside the symmetric window
+                    if (pos_diff < -half_n_swa || pos_diff > half_n_swa) {
+                        return true;
+                    }
+                } break;
+        }
+
+        return false;
+    }
+
 
     bool use_mrope() const;
 };

llama/llama.cpp/src/llama-kv-cache.cpp

@@ -97,6 +97,8 @@ llama_kv_cache::llama_kv_cache(
                 __func__, hparams.n_embd_v_gqa_max());
     }
 
+    const bool is_mla = hparams.is_mla();
+
     for (uint32_t il = 0; il < hparams.n_layer; il++) {
         if (!hparams.has_kv(il)) {
             LLAMA_LOG_DEBUG("%s: layer %3d: does not have KV cache\n", __func__, il);
@@ -130,18 +132,21 @@ llama_kv_cache::llama_kv_cache(
             throw std::runtime_error("failed to create ggml context for kv cache");
         }
 
-        ggml_tensor * k = ggml_new_tensor_3d(ctx, type_k, n_embd_k_gqa, kv_size, n_stream);
-        ggml_tensor * v = ggml_new_tensor_3d(ctx, type_v, n_embd_v_gqa, kv_size, n_stream);
+        const bool has_k = true;
+        const bool has_v = !is_mla;
 
-        ggml_format_name(k, "cache_k_l%d", il);
-        ggml_format_name(v, "cache_v_l%d", il);
+        ggml_tensor * k = has_k ? ggml_new_tensor_3d(ctx, type_k, n_embd_k_gqa, kv_size, n_stream) : nullptr;
+        ggml_tensor * v = has_v ? ggml_new_tensor_3d(ctx, type_v, n_embd_v_gqa, kv_size, n_stream) : nullptr;
+
+        has_k && ggml_format_name(k, "cache_k_l%d", il);
+        has_v && ggml_format_name(v, "cache_v_l%d", il);
 
         std::vector<ggml_tensor *> k_stream;
         std::vector<ggml_tensor *> v_stream;
 
         for (uint32_t s = 0; s < n_stream; ++s) {
-            k_stream.push_back(ggml_view_2d(ctx, k, n_embd_k_gqa, kv_size, k->nb[1], s*k->nb[2]));
-            v_stream.push_back(ggml_view_2d(ctx, v, n_embd_v_gqa, kv_size, v->nb[1], s*v->nb[2]));
+            k_stream.push_back(has_k ? ggml_view_2d(ctx, k, n_embd_k_gqa, kv_size, k->nb[1], s*k->nb[2]) : nullptr);
+            v_stream.push_back(has_v ? ggml_view_2d(ctx, v, n_embd_v_gqa, kv_size, v->nb[1], s*v->nb[2]) : nullptr);
         }
 
         map_layer_ids[il] = layers.size();
@@ -647,7 +652,10 @@ bool llama_kv_cache::update(llama_context * lctx, bool do_shift, const stream_co
                 const auto & layer = layers[il];
 
                 ggml_backend_tensor_copy(layer.k_stream[ssrc], layer.k_stream[sdst]);
-                ggml_backend_tensor_copy(layer.v_stream[ssrc], layer.v_stream[sdst]);
+
+                if (layer.v_stream[ssrc]) {
+                    ggml_backend_tensor_copy(layer.v_stream[ssrc], layer.v_stream[sdst]);
+                }
             }
         }
     }
@@ -852,7 +860,7 @@ llama_kv_cache::slot_info llama_kv_cache::find_slot(const llama_ubatch & ubatch,
                         const llama_seq_id seq_id_cell = cells.seq_get(idx);
 
                         // SWA mask
-                        if (is_masked_swa(pos_cell, cells.seq_pos_max(seq_id_cell) + 1)) {
+                        if (llama_hparams::is_masked_swa(n_swa, swa_type, pos_cell, cells.seq_pos_max(seq_id_cell) + 1)) {
                             can_use = true;
                         }
                     }
@@ -1237,6 +1245,197 @@ void llama_kv_cache::set_input_k_shift(ggml_tensor * dst) const {
     }
 }
 
+struct args_set_input_kq_mask {
+    const llama_hparams & hparams;
+    const llama_ubatch  * ubatch;
+
+    const std::vector<llama_kv_cells> & v_cells;
+    const std::vector<uint32_t>       & seq_to_stream;
+
+    uint32_t       n_swa;
+    llama_swa_type swa_type;
+
+    int64_t n_kv;
+    int64_t n_stream;
+    int64_t n_tps;
+};
+
+template<bool causal, bool swa, bool is_2d, bool alibi>
+static void set_input_kq_mask_impl(const args_set_input_kq_mask & args, float * data) {
+  //const auto & hparams = args.hparams;
+    const auto & ubatch  = args.ubatch;
+
+    const auto & v_cells       = args.v_cells;
+    const auto & seq_to_stream = args.seq_to_stream;
+
+    const uint32_t       n_swa    = args.n_swa;
+    const llama_swa_type swa_type = args.swa_type;
+
+    const int64_t n_kv     = args.n_kv;
+    const int64_t n_stream = args.n_stream;
+    const int64_t n_tps    = args.n_tps;
+
+    // the min position in the batch for each sequence
+    llama_pos seq_pos_min[LLAMA_MAX_SEQ];
+    std::fill(seq_pos_min, seq_pos_min + LLAMA_MAX_SEQ, INT32_MAX);
+
+    for (uint32_t i = 0; i < ubatch->n_tokens; ++i) {
+        const llama_seq_id seq_id = ubatch->seq_id[i][0];
+
+        seq_pos_min[seq_id] = std::min(seq_pos_min[seq_id], ubatch->pos[i]);
+    }
+
+    for (uint32_t s = 0; s < n_stream; ++s) {
+        // bookeeping of the KQ mask cells that could change for other tokens of the same sequence
+        std::unordered_map<llama_seq_id, uint32_t>              seq_srct;
+        std::unordered_map<llama_seq_id, std::vector<uint32_t>> seq_idxs;
+
+        for (uint32_t ii = 0; ii < n_tps; ++ii) {
+            const uint32_t i = s*n_tps + ii;
+
+            const llama_seq_id seq_id = ubatch->seq_id[i][0];
+
+            const auto & cells = v_cells.at(seq_to_stream[seq_id]);
+
+                  llama_pos p0 = -1;
+            const llama_pos p1 = ubatch->pos[i];
+
+            // for M-RoPE
+            const llama_pos p1_x = is_2d ? ubatch->pos[i + ubatch->n_tokens*2] : 0;
+            const llama_pos p1_y = is_2d ? ubatch->pos[i + ubatch->n_tokens]   : 0;
+
+            const uint64_t idst = n_kv*i;
+
+            // for tokens of the same sequence, the mask is mostly the same, so we can reuse it
+            // the only cells that could change are the ones that are with similar positions as the
+            //   ones in the batch (i.e. due to causal masking, SWA, etc.)
+            // keep track of those cells and shortcut the loop to save time
+            // note: this optimization is not compatible with Alibi position encoding
+            // ref:  https://github.com/ggml-org/llama.cpp/pull/18842
+            bool prev = false;
+
+            auto & idxs = seq_idxs[seq_id];
+
+            if (!alibi) {
+                if (seq_srct.find(seq_id) != seq_srct.end()) {
+                    const uint32_t srct = seq_srct[seq_id];
+
+                    const uint64_t idst_prev = n_kv*srct;
+
+                    std::copy(data + idst_prev, data + idst_prev + n_kv, data + idst);
+
+                    prev = true;
+                } else {
+                    idxs.clear();
+                    idxs.reserve(ubatch->n_tokens + n_swa + 32);
+
+                    seq_srct[seq_id] = i;
+                }
+            }
+
+            for (uint32_t jj = 0; jj < n_kv; ++jj) {
+                uint32_t j = jj;
+
+                // we have an exiting mask for this sequence -> update just seq_idxs
+                if (!alibi) {
+                    if (prev) {
+                        if (jj >= idxs.size()) {
+                            break;
+                        }
+
+                        j = idxs[jj];
+                    }
+                }
+
+                if (cells.is_empty(j)) {
+                    goto skip;
+                }
+
+                // mask the token if not the same sequence
+                if (!cells.seq_has(j, seq_id)) {
+                    goto skip;
+                }
+
+                p0 = cells.pos_get(j);
+
+                if (!alibi) {
+                    if (!prev) {
+                        // record all cells for which: p0 >= seq_pos_min[seq_id] - n_swa - 32
+                        if (p0 + (int32_t) (n_swa + 32) >= seq_pos_min[seq_id]) {
+                            idxs.push_back(j);
+                        }
+                    }
+                }
+
+                if (causal) {
+                    // mask future tokens
+                    if (p0 > p1) {
+                        goto skip;
+                    }
+
+                    // M-RoPE causal mask
+                    if (is_2d) {
+                        if (p0 == p1) {
+                            const auto & p0_ext = cells.ext_get(j);
+
+                            if (p0_ext.is_2d_gt(p1_x, p1_y)) {
+                                goto skip;
+                            }
+                        }
+                    }
+                }
+
+                // apply SWA if any
+                if (swa) {
+                    if (llama_hparams::is_masked_swa(n_swa, swa_type, p0, p1)) {
+                        goto skip;
+                    }
+                }
+
+                if (alibi) {
+                    data[idst + j] = -std::abs(p0 - p1);
+                } else {
+                    data[idst + j] = 0.0f;
+                }
+
+                continue;
+skip:
+                data[idst + j] = -INFINITY;
+            }
+        }
+    }
+}
+
+template<bool causal, bool swa, bool is_2d>
+static void set_input_kq_mask_impl(const args_set_input_kq_mask & args, float * data) {
+    const bool alibi = args.hparams.use_alibi;
+    if (alibi) {
+        set_input_kq_mask_impl<causal, swa, is_2d, true> (args, data);
+    } else {
+        set_input_kq_mask_impl<causal, swa, is_2d, false>(args, data);
+    }
+}
+
+template<bool causal, bool swa>
+static void set_input_kq_mask_impl(const args_set_input_kq_mask & args, float * data) {
+    const bool is_2d = args.ubatch->is_pos_2d();
+    if (is_2d) {
+        set_input_kq_mask_impl<causal, swa, true> (args, data);
+    } else {
+        set_input_kq_mask_impl<causal, swa, false>(args, data);
+    }
+}
+
+template<bool causal>
+static void set_input_kq_mask_impl(const args_set_input_kq_mask & args, float * data) {
+    const bool swa = args.swa_type != LLAMA_SWA_TYPE_NONE;
+    if (swa) {
+        set_input_kq_mask_impl<causal, true> (args, data);
+    } else {
+        set_input_kq_mask_impl<causal, false>(args, data);
+    }
+}
+
 void llama_kv_cache::set_input_kq_mask(ggml_tensor * dst, const llama_ubatch * ubatch, bool causal_attn) const {
     const uint32_t n_tokens = ubatch->n_tokens;
 
@@ -1251,74 +1450,29 @@ void llama_kv_cache::set_input_kq_mask(ggml_tensor * dst, const llama_ubatch * u
     // n_tps == n_tokens_per_stream
     const int64_t n_tps = n_tokens/n_stream;
 
-    std::fill(data, data + ggml_nelements(dst), -INFINITY);
+    //const int64_t t_start = ggml_time_us();
 
-    // Use only the previous KV cells of the correct sequence for each token of the ubatch.
-    // It's assumed that if a token in the batch has multiple sequences, they are equivalent.
-    // Example with a cache of 10 tokens, 2 tokens populated in cache and 3 tokens in batch:
-    //   Causal mask:
-    //      xxx-------
-    //      xxxx------
-    //      xxxxx-----
-    //   Non-causal mask:
-    //      xxxxx-----
-    //      xxxxx-----
-    //      xxxxx-----
-    // To visualize the mask, see https://github.com/ggml-org/llama.cpp/pull/12615
-    // TODO: optimize this section
-    for (uint32_t h = 0; h < 1; ++h) {
-        for (uint32_t s = 0; s < n_stream; ++s) {
-            for (uint32_t ii = 0; ii < n_tps; ++ii) {
-                const uint32_t i = s*n_tps + ii;
+    const args_set_input_kq_mask args = {
+        /*.hparams          =*/ hparams,
+        /*.ubatch           =*/ ubatch,
+        /*.v_cells          =*/ v_cells,
+        /*.seq_to_stream    =*/ seq_to_stream,
+        /*.n_swa            =*/ n_swa,
+        /*.swa_type         =*/ swa_type,
+        /*.n_kv             =*/ n_kv,
+        /*.n_stream         =*/ n_stream,
+        /*.n_tps            =*/ n_tps,
+    };
 
-                const llama_seq_id seq_id = ubatch->seq_id[i][0];
-
-                const auto & cells = v_cells[seq_to_stream[seq_id]];
-
-                const llama_pos p1 = ubatch->pos[i];
-
-                // for M-RoPE
-                const bool is_2d = ubatch->is_pos_2d();
-                const llama_pos p1_x = is_2d ? ubatch->pos[i + ubatch->n_tokens*2] : 0;
-                const llama_pos p1_y = is_2d ? ubatch->pos[i + ubatch->n_tokens]   : 0;
-
-                const uint64_t idst = n_kv*(h*n_stream*n_tps + s*n_tps + ii);
-
-                for (uint32_t j = 0; j < n_kv; ++j) {
-                    if (cells.is_empty(j)) {
-                        continue;
-                    }
-
-                    // mask the token if not the same sequence
-                    if (!cells.seq_has(j, seq_id)) {
-                        continue;
-                    }
-
-                    const llama_pos p0 = cells.pos_get(j);
-
-                    // mask future tokens
-                    if (causal_attn && p0 > p1) {
-                        continue;
-                    }
-
-                    // M-RoPE causal mask
-                    if (causal_attn && is_2d && p0 == p1) {
-                        const auto & p0_ext = cells.ext_get(j);
-                        if (p0_ext.is_2d_gt(p1_x, p1_y)) {
-                            continue;
-                        }
-                    }
-
-                    // apply SWA if any
-                    if (is_masked_swa(p0, p1)) {
-                        continue;
-                    }
-
-                    data[idst + j] = hparams.use_alibi ? -std::abs(p0 - p1) : 0.0f;
-                }
-            }
-        }
+    if (causal_attn) {
+        set_input_kq_mask_impl<true> (args, data);
+    } else {
+        set_input_kq_mask_impl<false>(args, data);
     }
+
+    //const int64_t t_end = ggml_time_us();
+
+    //LLAMA_LOG_ERROR("%s: kq mask time: %0.3f ms\n", __func__, (t_end - t_start)/1000.0);
 }
 
 void llama_kv_cache::set_input_pos_bucket(ggml_tensor * dst, const llama_ubatch * ubatch) const {
@@ -1370,7 +1524,7 @@ size_t llama_kv_cache::size_v_bytes() const {
     size_t size_v_bytes = 0;
 
     for (const auto & layer : layers) {
-        size_v_bytes += ggml_nbytes(layer.v);
+        size_v_bytes += layer.v ? ggml_nbytes(layer.v) : 0;
     }
 
     return size_v_bytes;
@@ -1448,6 +1602,10 @@ ggml_cgraph * llama_kv_cache::build_graph_shift(llm_graph_result * res, llama_co
     const auto & n_embd_head_k = hparams.n_embd_head_k;
   //const auto & n_embd_head_v = hparams.n_embd_head_v;
 
+    const auto & n_rot = hparams.n_rot;
+
+    const auto n_embd_nope = hparams.n_lora_kv > 0 ? n_embd_head_k - n_rot : 0;
+
     auto inp = std::make_unique<llm_graph_input_k_shift>(this);
 
     inp->k_shift = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, (int64_t) get_size()*n_stream);
@@ -1468,10 +1626,10 @@ ggml_cgraph * llama_kv_cache::build_graph_shift(llm_graph_result * res, llama_co
 
         ggml_tensor * k =
             ggml_view_3d(ctx, layer.k,
-                n_embd_head_k, n_head_kv, get_size()*n_stream,
+                n_rot, n_head_kv, get_size()*n_stream,
                 ggml_row_size(layer.k->type, n_embd_head_k),
                 ggml_row_size(layer.k->type, n_embd_k_gqa),
-                0);
+                ggml_row_size(layer.k->type, n_embd_nope));
 
         ggml_tensor * cur = build_rope_shift(cparams, ctx, k, inp->k_shift, rope_factors, freq_base_l, freq_scale_l);
 
@@ -1483,10 +1641,6 @@ ggml_cgraph * llama_kv_cache::build_graph_shift(llm_graph_result * res, llama_co
     return gf;
 }
 
-bool llama_kv_cache::is_masked_swa(llama_pos p0, llama_pos p1) const {
-    return llama_hparams::is_masked_swa(n_swa, swa_type, p0, p1);
-}
-
 void llama_kv_cache::state_write(llama_io_write_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) const {
     GGML_UNUSED(flags);
 
@@ -1652,6 +1806,9 @@ void llama_kv_cache::state_write_data(llama_io_write_i & io, const cell_ranges_t
             const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il);
 
             auto * v = layer.v_stream[cr.strm];
+            if (!v) {
+                continue;
+            }
 
             // Write value type
             const int32_t v_type_i = (int32_t) v->type;
@@ -1678,6 +1835,9 @@ void llama_kv_cache::state_write_data(llama_io_write_i & io, const cell_ranges_t
             const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il);
 
             auto * v = layer.v_stream[cr.strm];
+            if (!v) {
+                continue;
+            }
 
             // Write value type
             const int32_t v_type_i = (int32_t) v->type;
@@ -1881,6 +2041,9 @@ bool llama_kv_cache::state_read_data(llama_io_read_i & io, uint32_t strm, uint32
             const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il);
 
             auto * v = layer.v_stream[strm];
+            if (!v) {
+                continue;
+            }
 
             // Read type of value
             int32_t v_type_i_ref;
@@ -1922,6 +2085,9 @@ bool llama_kv_cache::state_read_data(llama_io_read_i & io, uint32_t strm, uint32
             const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il);
 
             auto * v = layer.v_stream[strm];
+            if (!v) {
+                continue;
+            }
 
             // Read type of value
             int32_t v_type_i_ref;

llama/llama.cpp/src/llama-kv-cache.h

@@ -257,8 +257,6 @@ private:
     size_t size_k_bytes() const;
     size_t size_v_bytes() const;
 
-    bool is_masked_swa(llama_pos p0, llama_pos p1) const;
-
     ggml_tensor * build_rope_shift(
             const llama_cparams & cparams,
                    ggml_context * ctx,
@@ -305,7 +303,7 @@ public:
             bool do_shift,
             stream_copy_info sc_info);
 
-    // used to create a batch procesing context from a batch
+    // used to create a batch processing context from a batch
     llama_kv_cache_context(
             llama_kv_cache * kv,
             slot_info_vec_t sinfos,

llama/llama.cpp/src/llama-memory-hybrid-iswa.cpp

@@ -0,0 +1,275 @@
+#include "llama-memory-hybrid-iswa.h"
+
+#include "llama-impl.h"
+#include "llama-model.h"
+#include "llama-context.h"
+
+//
+// llama_memory_hybrid_iswa
+//
+
+llama_memory_hybrid_iswa::llama_memory_hybrid_iswa(
+        const llama_model & model,
+                            /* attn */
+                ggml_type   type_k,
+                ggml_type   type_v,
+                     bool   v_trans,
+                     bool   swa_full,
+                 uint32_t   kv_size,
+                 uint32_t   n_ubatch,
+                 uint32_t   n_pad,
+                            /* recurrent */
+                ggml_type   type_r,
+                ggml_type   type_s,
+                 uint32_t   rs_size,
+                            /* common */
+                 uint32_t   n_seq_max,
+                     bool   offload,
+                     bool   unified,
+                            /* layer filters */
+    const layer_filter_cb & filter_attn,
+    const layer_filter_cb & filter_recr) :
+    hparams(model.hparams),
+    mem_attn(new llama_kv_cache_iswa(
+        model,
+        type_k,
+        type_v,
+        v_trans,
+        offload,
+        swa_full,
+        unified,
+        kv_size,
+        n_seq_max,
+        n_ubatch,
+        n_pad,
+        filter_attn == nullptr ?
+            [&](int32_t il) { return !hparams.is_recurrent(il); }
+            : filter_attn,
+        nullptr
+    )),
+    mem_recr(new llama_memory_recurrent(
+        model,
+        type_r,
+        type_s,
+        offload,
+        rs_size,
+        n_seq_max,
+        filter_recr == nullptr ?
+            [&](int32_t il) { return hparams.is_recurrent(il); }
+            : filter_recr
+    )) {}
+
+llama_memory_context_ptr llama_memory_hybrid_iswa::init_batch(llama_batch_allocr & balloc, uint32_t n_ubatch, bool embd_all) {
+    do {
+        balloc.split_reset();
+
+        // follow the recurrent pattern for creating the ubatch splits
+        std::vector<llama_ubatch> ubatches;
+
+        while (true) {
+            llama_ubatch ubatch;
+
+            if (embd_all) {
+                // if all tokens are output, split by sequence
+                ubatch = balloc.split_seq(n_ubatch);
+            } else {
+                // TODO: non-sequential equal split can be done if using unified KV cache
+                //       for simplicity, we always use sequential equal split for now
+                ubatch = balloc.split_equal(n_ubatch, true);
+            }
+
+            if (ubatch.n_tokens == 0) {
+                break;
+            }
+
+            ubatches.push_back(std::move(ubatch)); // NOLINT
+        }
+
+        if (balloc.get_n_used() < balloc.get_n_tokens()) {
+            // failed to find a suitable split
+            break;
+        }
+
+        // prepare the recurrent batches first
+        if (!mem_recr->prepare(ubatches)) {
+            // TODO: will the recurrent cache be in an undefined context at this point?
+            LLAMA_LOG_ERROR("%s: failed to prepare recurrent ubatches\n", __func__);
+            return std::make_unique<llama_memory_hybrid_iswa_context>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
+        }
+
+        // prepare the attention cache (iswa version returns both base and swa slot infos)
+        auto sinfos_base = mem_attn->get_base()->prepare(ubatches);
+        if (sinfos_base.empty()) {
+            LLAMA_LOG_ERROR("%s: failed to prepare attention base ubatches\n", __func__);
+            return std::make_unique<llama_memory_hybrid_iswa_context>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
+        }
+
+        auto sinfos_swa = mem_attn->get_swa()->prepare(ubatches);
+        if (sinfos_swa.empty()) {
+            LLAMA_LOG_ERROR("%s: failed to prepare attention swa ubatches\n", __func__);
+            return std::make_unique<llama_memory_hybrid_iswa_context>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
+        }
+
+        return std::make_unique<llama_memory_hybrid_iswa_context>(
+                this, std::move(sinfos_base), std::move(sinfos_swa), std::move(ubatches));
+    } while(false);
+
+    return std::make_unique<llama_memory_hybrid_iswa_context>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
+}
+
+llama_memory_context_ptr llama_memory_hybrid_iswa::init_full() {
+    return std::make_unique<llama_memory_hybrid_iswa_context>(this);
+}
+
+llama_memory_context_ptr llama_memory_hybrid_iswa::init_update(llama_context * lctx, bool optimize) {
+    return std::make_unique<llama_memory_hybrid_iswa_context>(this, lctx, optimize);
+}
+
+bool llama_memory_hybrid_iswa::get_can_shift() const {
+    // Shifting is trivially supported for recurrent
+    return mem_attn->get_can_shift();
+}
+
+void llama_memory_hybrid_iswa::clear(bool data) {
+    mem_attn->clear(data);
+    mem_recr->clear(data);
+}
+
+bool llama_memory_hybrid_iswa::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos p1) {
+    // Try removing from the recurrent cache first since it may fail. If it does
+    // fail, the cache will not have been mutated.
+    if (!mem_recr->seq_rm(seq_id, p0, p1)) {
+        return false;
+    }
+    return mem_attn->seq_rm(seq_id, p0, p1);
+}
+
+void llama_memory_hybrid_iswa::seq_cp(llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) {
+    mem_attn->seq_cp(seq_id_src, seq_id_dst, p0, p1);
+    mem_recr->seq_cp(seq_id_src, seq_id_dst, p0, p1);
+}
+
+void llama_memory_hybrid_iswa::seq_keep(llama_seq_id seq_id) {
+    mem_attn->seq_keep(seq_id);
+    mem_recr->seq_keep(seq_id);
+}
+
+void llama_memory_hybrid_iswa::seq_add(llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos shift) {
+    mem_attn->seq_add(seq_id, p0, p1, shift);
+    mem_recr->seq_add(seq_id, p0, p1, shift);
+}
+
+void llama_memory_hybrid_iswa::seq_div(llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) {
+    mem_attn->seq_div(seq_id, p0, p1, d);
+    mem_recr->seq_div(seq_id, p0, p1, d);
+}
+
+llama_pos llama_memory_hybrid_iswa::seq_pos_min(llama_seq_id seq_id) const {
+    // the min of the total cache is the max of the two caches' min values
+    return std::max(mem_attn->seq_pos_min(seq_id), mem_recr->seq_pos_min(seq_id));
+}
+
+llama_pos llama_memory_hybrid_iswa::seq_pos_max(llama_seq_id seq_id) const {
+    // the max of the total cache is the min of the two caches' max values
+    return std::min(mem_attn->seq_pos_max(seq_id), mem_recr->seq_pos_max(seq_id));
+}
+
+std::map<ggml_backend_buffer_type_t, size_t> llama_memory_hybrid_iswa::memory_breakdown() const {
+    std::map<ggml_backend_buffer_type_t, size_t> mb = mem_attn->memory_breakdown();
+    for (const auto & buft_size : mem_recr->memory_breakdown()) {
+        mb[buft_size.first] += buft_size.second;
+    }
+    return mb;
+}
+
+void llama_memory_hybrid_iswa::state_write(llama_io_write_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) const {
+    mem_attn->state_write(io, seq_id, flags);
+    mem_recr->state_write(io, seq_id, flags);
+}
+
+void llama_memory_hybrid_iswa::state_read(llama_io_read_i & io, llama_seq_id seq_id, llama_state_seq_flags flags) {
+    mem_attn->state_read(io, seq_id, flags);
+    mem_recr->state_read(io, seq_id, flags);
+}
+
+llama_kv_cache_iswa * llama_memory_hybrid_iswa::get_mem_attn() const {
+    return mem_attn.get();
+}
+
+llama_memory_recurrent * llama_memory_hybrid_iswa::get_mem_recr() const {
+    return mem_recr.get();
+}
+
+//
+// llama_memory_hybrid_iswa_context
+//
+
+llama_memory_hybrid_iswa_context::llama_memory_hybrid_iswa_context(llama_memory_status status) : status(status) {}
+
+llama_memory_hybrid_iswa_context::llama_memory_hybrid_iswa_context(llama_memory_hybrid_iswa * mem) :
+    ctx_attn(mem->get_mem_attn()->init_full()),
+    ctx_recr(mem->get_mem_recr()->init_full()),
+    status(llama_memory_status_combine(ctx_attn->get_status(), ctx_recr->get_status())) {
+}
+
+llama_memory_hybrid_iswa_context::llama_memory_hybrid_iswa_context(
+        llama_memory_hybrid_iswa * mem,
+                   llama_context * lctx,
+                            bool   optimize) :
+    ctx_attn(mem->get_mem_attn()->init_update(lctx, optimize)),
+    ctx_recr(mem->get_mem_recr()->init_update(lctx, optimize)),
+    status(llama_memory_status_combine(ctx_attn->get_status(), ctx_recr->get_status())) {
+}
+
+llama_memory_hybrid_iswa_context::llama_memory_hybrid_iswa_context(
+           llama_memory_hybrid_iswa * mem,
+                    slot_info_vec_t   sinfos_base,
+                    slot_info_vec_t   sinfos_swa,
+          std::vector<llama_ubatch>   ubatches) :
+    ubatches(std::move(ubatches)),
+    // note: here we copy the ubatches. not sure if this is ideal
+    ctx_attn(new llama_kv_cache_iswa_context(mem->get_mem_attn(), std::move(sinfos_base), std::move(sinfos_swa), this->ubatches)),
+    ctx_recr(new llama_memory_recurrent_context(mem->get_mem_recr(), this->ubatches)),
+    status(llama_memory_status_combine(ctx_attn->get_status(), ctx_recr->get_status())) {
+}
+
+bool llama_memory_hybrid_iswa_context::next() {
+    assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
+
+    ctx_attn->next();
+    ctx_recr->next();
+
+    if (++i_next >= ubatches.size()) {
+        return false;
+    }
+
+    return true;
+}
+
+bool llama_memory_hybrid_iswa_context::apply() {
+    assert(!llama_memory_status_is_fail(status));
+
+    bool res = true;
+
+    res = res & ctx_attn->apply();
+    res = res & ctx_recr->apply();
+
+    return res;
+}
+
+llama_memory_status llama_memory_hybrid_iswa_context::get_status() const {
+    return status;
+}
+
+const llama_ubatch & llama_memory_hybrid_iswa_context::get_ubatch() const {
+    assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
+    return ubatches[i_next];
+}
+
+const llama_kv_cache_iswa_context * llama_memory_hybrid_iswa_context::get_attn() const {
+    return static_cast<const llama_kv_cache_iswa_context *>(ctx_attn.get());
+}
+
+const llama_memory_recurrent_context * llama_memory_hybrid_iswa_context::get_recr() const {
+    return static_cast<const llama_memory_recurrent_context *>(ctx_recr.get());
+}

llama/llama.cpp/src/llama-memory-hybrid-iswa.h

@@ -0,0 +1,140 @@
+#pragma once
+
+#include "llama-batch.h"
+#include "llama-graph.h"
+#include "llama-kv-cache-iswa.h"
+#include "llama-memory.h"
+#include "llama-memory-recurrent.h"
+
+#include <memory>
+#include <vector>
+
+//
+// llama_memory_hybrid_iswa
+//
+
+// utilizes instances of llama_memory_recurrent and llama_kv_cache_iswa to
+//   support models where each layer may be either attention-based (with SWA support) or recurrent
+
+class llama_memory_hybrid_iswa : public llama_memory_i {
+public:
+    llama_memory_hybrid_iswa(
+        const llama_model & model,
+                            /* attn */
+                ggml_type   type_k,
+                ggml_type   type_v,
+                     bool   v_trans,
+                     bool   swa_full,
+                 uint32_t   kv_size,
+                 uint32_t   n_ubatch,
+                 uint32_t   n_pad,
+                            /* recurrent */
+                ggml_type   type_r,
+                ggml_type   type_s,
+                 uint32_t   rs_size,
+                            /* common */
+                 uint32_t   n_seq_max,
+                     bool   offload,
+                     bool   unified,
+                            /* layer filters */
+    const layer_filter_cb & filter_attn = nullptr,
+    const layer_filter_cb & filter_recr = nullptr);
+
+    ~llama_memory_hybrid_iswa() = default;
+
+    //
+    // llama_memory_i
+    //
+
+    llama_memory_context_ptr init_batch(
+            llama_batch_allocr & balloc,
+            uint32_t n_ubatch,
+            bool embd_all) override;
+
+    llama_memory_context_ptr init_full() override;
+
+    llama_memory_context_ptr init_update(llama_context * lctx, bool optimize) override;
+
+    bool get_can_shift() const override;
+
+    void clear(bool data) override;
+
+    bool seq_rm  (llama_seq_id seq_id,                              llama_pos p0, llama_pos p1) override;
+    void seq_cp  (llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) override;
+    void seq_keep(llama_seq_id seq_id)                                                          override;
+    void seq_add (llama_seq_id seq_id,                              llama_pos p0, llama_pos p1, llama_pos shift) override;
+    void seq_div (llama_seq_id seq_id,                              llama_pos p0, llama_pos p1, int d) override;
+
+    llama_pos seq_pos_min(llama_seq_id seq_id) const override;
+    llama_pos seq_pos_max(llama_seq_id seq_id) const override;
+
+    std::map<ggml_backend_buffer_type_t, size_t> memory_breakdown() const override;
+
+    // state write/load
+
+    void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1, llama_state_seq_flags flags = 0) const override;
+    void state_read (llama_io_read_i  & io, llama_seq_id seq_id = -1, llama_state_seq_flags flags = 0)       override;
+
+    //
+    // llama_memory_hybrid_iswa specific API
+    //
+
+    llama_kv_cache_iswa * get_mem_attn() const;
+    llama_memory_recurrent * get_mem_recr() const;
+
+private:
+    const llama_hparams & hparams;
+
+    const std::unique_ptr<llama_kv_cache_iswa> mem_attn;
+    const std::unique_ptr<llama_memory_recurrent> mem_recr;
+};
+
+class llama_memory_hybrid_iswa_context : public llama_memory_context_i {
+public:
+    using slot_info_vec_t = llama_kv_cache::slot_info_vec_t;
+
+    // init failure
+    explicit llama_memory_hybrid_iswa_context(llama_memory_status status);
+
+    // init full
+    explicit llama_memory_hybrid_iswa_context(llama_memory_hybrid_iswa * mem);
+
+    // init update
+    explicit llama_memory_hybrid_iswa_context(
+        llama_memory_hybrid_iswa * mem,
+                   llama_context * lctx,
+                            bool   optimize);
+
+    // init success
+    llama_memory_hybrid_iswa_context(
+           llama_memory_hybrid_iswa * mem,
+                    slot_info_vec_t   sinfos_base,
+                    slot_info_vec_t   sinfos_swa,
+          std::vector<llama_ubatch>   ubatches);
+
+    ~llama_memory_hybrid_iswa_context() = default;
+
+    bool next()  override;
+    bool apply() override;
+
+    llama_memory_status  get_status() const override;
+    const llama_ubatch & get_ubatch() const override;
+
+    //
+    // llama_memory_hybrid_iswa_context
+    //
+
+    const llama_kv_cache_iswa_context * get_attn() const;
+    const llama_memory_recurrent_context * get_recr() const;
+
+private:
+    // the index of the next ubatch to process
+    size_t i_next = 0;
+
+    std::vector<llama_ubatch> ubatches;
+
+    const llama_memory_context_ptr ctx_attn;
+    const llama_memory_context_ptr ctx_recr;
+
+    const llama_memory_status status;
+};

llama/llama.cpp/src/llama-mmap.cpp

@@ -13,9 +13,10 @@
 #ifdef __has_include
     #if __has_include(<unistd.h>)
         #include <unistd.h>
+        #include <fcntl.h>
+        #include <sys/stat.h>
         #if defined(_POSIX_MAPPED_FILES)
             #include <sys/mman.h>
-            #include <fcntl.h>
         #endif
         #if defined(_POSIX_MEMLOCK_RANGE)
             #include <sys/resource.h>
@@ -74,7 +75,7 @@ struct llama_file::impl {
         return ret;
     }
 
-    impl(const char * fname, const char * mode) {
+    impl(const char * fname, const char * mode, [[maybe_unused]] const bool use_direct_io = false) {
         fp = ggml_fopen(fname, mode);
         if (fp == NULL) {
             throw std::runtime_error(format("failed to open %s: %s", fname, strerror(errno)));
@@ -109,7 +110,7 @@ struct llama_file::impl {
         }
     }
 
-    void read_raw(void * ptr, size_t len) const {
+    void read_raw(void * ptr, size_t len) {
         size_t bytes_read = 0;
         while (bytes_read < len) {
             size_t chunk_size = std::min<size_t>(len - bytes_read, 64*1024*1024);
@@ -126,7 +127,7 @@ struct llama_file::impl {
         }
     }
 
-    uint32_t read_u32() const {
+    uint32_t read_u32() {
         uint32_t val;
         read_raw(&val, sizeof(val));
         return val;
@@ -153,16 +154,55 @@ struct llama_file::impl {
         write_raw(&val, sizeof(val));
     }
 
+    bool has_direct_io() const {
+        return true;
+    }
+
     ~impl() {
         if (fp) {
             std::fclose(fp);
         }
     }
 #else
-    impl(const char * fname, const char * mode) {
-        fp = ggml_fopen(fname, mode);
+    impl(const char * fname, const char * mode, [[maybe_unused]] const bool use_direct_io = false) : fname(fname) {
+#ifdef __linux__
+        // Try unbuffered I/O for read only
+        if (use_direct_io && std::strcmp(mode, "rb") == 0) {
+            if (init_fd()) {
+                return;
+            }
+            LLAMA_LOG_WARN("Failed to open file '%s' with error: %s. Falling back to buffered I/O",
+                           fname, strerror(errno));
+        }
+#endif
+        init_fp(mode);
+    }
+
+#ifdef __linux__
+    bool init_fd() {
+        fd = open(fname.c_str(), O_RDONLY | O_DIRECT);
+
+        if (fd != -1) {
+            struct stat file_stats{};
+            fstat(fd, &file_stats);
+
+            size = file_stats.st_size;
+            alignment = file_stats.st_blksize;
+
+            off_t ret = lseek(fd, 0, SEEK_SET);
+            if (ret == -1) {
+                throw std::runtime_error(format("seek error: %s", strerror(errno)));
+            }
+            return true;
+        }
+        return false;
+    }
+#endif
+
+    void init_fp(const char * mode) {
+        fp = ggml_fopen(fname.c_str(), mode);
         if (fp == NULL) {
-            throw std::runtime_error(format("failed to open %s: %s", fname, strerror(errno)));
+            throw std::runtime_error(format("failed to open %s: %s", fname.c_str(), strerror(errno)));
         }
         seek(0, SEEK_END);
         size = tell();
@@ -170,46 +210,122 @@ struct llama_file::impl {
     }
 
     size_t tell() const {
-// TODO: this ifdef is never true?
-#ifdef _WIN32
-        __int64 ret = _ftelli64(fp);
-#else
-        long ret = std::ftell(fp);
-#endif
-        if (ret == -1) {
-            throw std::runtime_error(format("ftell error: %s", strerror(errno)));
+        if (fd == -1) {
+            long ret = std::ftell(fp);
+            if (ret == -1) {
+                throw std::runtime_error(format("ftell error: %s", strerror(errno)));
+            }
+
+            return (size_t) ret;
         }
 
-        return (size_t) ret;
+        off_t pos = lseek(fd, 0, SEEK_CUR);
+        if (pos == -1) {
+            throw std::runtime_error(format("lseek error: %s", strerror(errno)));
+        }
+        return (size_t) pos;
     }
 
     void seek(size_t offset, int whence) const {
-// TODO: this ifdef is never true?
-#ifdef _WIN32
-        int ret = _fseeki64(fp, (__int64) offset, whence);
-#else
-        int ret = std::fseek(fp, (long) offset, whence);
-#endif
-        if (ret != 0) {
+        off_t ret = 0;
+        if (fd == -1) {
+            ret = std::fseek(fp, (long) offset, whence);
+        } else {
+            ret = lseek(fd, offset, whence);
+        }
+        if (ret == -1) {
             throw std::runtime_error(format("seek error: %s", strerror(errno)));
         }
     }
 
-    void read_raw(void * ptr, size_t len) const {
+    void read_raw_unsafe(void * ptr, size_t len) {
         if (len == 0) {
             return;
         }
         errno = 0;
-        std::size_t ret = std::fread(ptr, len, 1, fp);
-        if (ferror(fp)) {
-            throw std::runtime_error(format("read error: %s", strerror(errno)));
-        }
-        if (ret != 1) {
-            throw std::runtime_error("unexpectedly reached end of file");
+        if (fd == -1) {
+            const size_t curr_off = tell();
+            const size_t to_read = std::min(len, size - curr_off);
+
+            std::size_t ret = std::fread(ptr, to_read, 1, fp);
+            if (ferror(fp)) {
+                throw std::runtime_error(format("read error: %s", strerror(errno)));
+            }
+            if (to_read > 0 && ret != 1) {
+                throw std::runtime_error("unexpectedly reached end of file");
+            }
+        } else {
+            size_t bytes_read = 0;
+            while (bytes_read < len) {
+                const size_t to_read = len - bytes_read;
+                ssize_t ret = ::read(fd, reinterpret_cast<char *>(ptr) + bytes_read, to_read);
+
+                if (ret == -1) {
+                    if (errno == EINTR) {
+                        continue;  // Interrupted by signal, retry
+                    }
+                    // Fallback to std::fread in case the DMA controller cannot access the buffer
+                    if (errno == EFAULT || errno == EINVAL) {
+                        LLAMA_LOG_WARN("%s: Falling back to buffered IO due to %s\n", __func__, strerror(errno));
+                        auto curr_off = tell();
+                        close(fd);
+                        fd = -1;
+                        alignment = 1;
+                        init_fp("rb");
+                        seek(curr_off, SEEK_SET);
+                        read_raw_unsafe(ptr, len);
+                        return;
+                    }
+                    throw std::runtime_error(format("read error: %s", strerror(errno)));
+                }
+                if (ret == 0) {
+                    // EOF: allow if this read was only pulling alignment padding past file end
+                    off_t pos = lseek(fd, 0, SEEK_CUR);
+                    if (pos != -1 && (size_t) pos == size) {
+                        std::memset(reinterpret_cast<char *>(ptr) + bytes_read, 0, len - bytes_read);
+                        return;
+                    }
+                    throw std::runtime_error("unexpectedly reached end of file");
+                }
+
+                bytes_read += (size_t) ret;
+            }
         }
     }
 
-    uint32_t read_u32() const {
+    void read_aligned_chunk(void * dest, size_t size) {
+        size_t offset = tell();
+        off_t aligned_offset = offset & ~(alignment - 1);
+        off_t offset_from_alignment = offset - aligned_offset;
+        size_t bytes_to_read = (offset_from_alignment + size + alignment - 1) & ~(alignment - 1);
+
+        void * raw_buffer = nullptr;
+        int ret = posix_memalign(&raw_buffer, alignment, bytes_to_read);
+        if (ret != 0) {
+            throw std::runtime_error(format("posix_memalign failed with error %d", ret));
+        }
+
+        struct aligned_buffer_deleter {
+            void operator()(void * p) const { free(p); }
+        };
+        std::unique_ptr<void, aligned_buffer_deleter> buffer(raw_buffer);
+
+        seek(aligned_offset, SEEK_SET);
+        read_raw_unsafe(buffer.get(), bytes_to_read);
+
+        uintptr_t actual_data = reinterpret_cast<uintptr_t>(buffer.get()) + offset_from_alignment;
+        memcpy(dest, reinterpret_cast<void *>(actual_data), size);
+    }
+
+    void read_raw(void * ptr, size_t len) {
+        if (has_direct_io()) {
+            read_aligned_chunk(ptr, len);
+        } else {
+            read_raw_unsafe(ptr, len);
+        }
+    }
+
+    uint32_t read_u32() {
         uint32_t ret;
         read_raw(&ret, sizeof(ret));
         return ret;
@@ -230,27 +346,48 @@ struct llama_file::impl {
         write_raw(&val, sizeof(val));
     }
 
+    bool has_direct_io() const {
+        return fd != -1 && alignment > 1;
+    }
+
     ~impl() {
-        if (fp) {
+        if (fd != -1) {
+            close(fd);
+        } else {
             std::fclose(fp);
         }
     }
+    int fd = -1;
+    std::string fname;
 #endif
 
-    FILE * fp;
-    size_t size;
+    size_t read_alignment() const {
+        return alignment;
+    }
+
+    size_t alignment = 1;
+
+    FILE * fp{};
+    size_t size{};
 };
 
-llama_file::llama_file(const char * fname, const char * mode) : pimpl(std::make_unique<impl>(fname, mode)) {}
+llama_file::llama_file(const char * fname, const char * mode, const bool use_direct_io) :
+    pimpl(std::make_unique<impl>(fname, mode, use_direct_io)) {}
 llama_file::~llama_file() = default;
 
 size_t llama_file::tell() const { return pimpl->tell(); }
 size_t llama_file::size() const { return pimpl->size; }
 
+size_t llama_file::read_alignment() const { return pimpl->read_alignment(); }
+bool llama_file::has_direct_io() const { return pimpl->has_direct_io(); }
+
 int llama_file::file_id() const {
 #ifdef _WIN32
     return _fileno(pimpl->fp);
 #else
+    if (pimpl->fd != -1) {
+        return pimpl->fd;
+    }
 #if defined(fileno)
     return fileno(pimpl->fp);
 #else
@@ -260,9 +397,14 @@ int llama_file::file_id() const {
 }
 
 void llama_file::seek(size_t offset, int whence) const { pimpl->seek(offset, whence); }
-void llama_file::read_raw(void * ptr, size_t len) const { pimpl->read_raw(ptr, len); }
+void llama_file::read_raw(void * ptr, size_t len) { pimpl->read_raw(ptr, len); }
+#ifdef _WIN32
+void llama_file::read_raw_unsafe(void * ptr, size_t len) { pimpl->read_raw(ptr, len); }
+#else
+void llama_file::read_raw_unsafe(void * ptr, size_t len) { pimpl->read_raw_unsafe(ptr, len); }
+#endif
 
-uint32_t llama_file::read_u32() const { return pimpl->read_u32(); }
+uint32_t llama_file::read_u32() { return pimpl->read_u32(); }
 
 void llama_file::write_raw(const void * ptr, size_t len) const { pimpl->write_raw(ptr, len); }
 void llama_file::write_u32(uint32_t val) const { pimpl->write_u32(val); }
@@ -476,9 +618,9 @@ struct llama_mlock::impl {
 
         char* errmsg = std::strerror(errno);
         bool suggest = (errno == ENOMEM);
-#if defined(TARGET_OS_VISION) || defined(TARGET_OS_TV) || defined(_AIX)
-        // visionOS/tvOS dont't support RLIMIT_MEMLOCK
-        // Skip resource limit checks on visionOS/tvOS
+#if defined(TARGET_OS_VISION) || defined(TARGET_OS_TV) || defined(_AIX) || defined(__HAIKU__)
+        // visionOS/tvOS/Haiku don't support RLIMIT_MEMLOCK
+        // Skip resource limit checks on these platforms
         suggest = false;
 #else
         struct rlimit lock_limit;

llama/llama.cpp/src/llama-mmap.h

@@ -3,6 +3,7 @@
 #include <cstdint>
 #include <memory>
 #include <vector>
+#include <cstdio>
 
 struct llama_file;
 struct llama_mmap;
@@ -13,7 +14,7 @@ using llama_mmaps  = std::vector<std::unique_ptr<llama_mmap>>;
 using llama_mlocks = std::vector<std::unique_ptr<llama_mlock>>;
 
 struct llama_file {
-    llama_file(const char * fname, const char * mode);
+    llama_file(const char * fname, const char * mode, bool use_direct_io = false);
     ~llama_file();
 
     size_t tell() const;
@@ -23,12 +24,16 @@ struct llama_file {
 
     void seek(size_t offset, int whence) const;
 
-    void read_raw(void * ptr, size_t len) const;
-    uint32_t read_u32() const;
+    void read_raw(void * ptr, size_t len);
+    void read_raw_unsafe(void * ptr, size_t len);
+    void read_aligned_chunk(void * dest, size_t size);
+    uint32_t read_u32();
 
     void write_raw(const void * ptr, size_t len) const;
     void write_u32(uint32_t val) const;
 
+    size_t read_alignment() const;
+    bool has_direct_io() const;
 private:
     struct impl;
     std::unique_ptr<impl> pimpl;

llama/llama.cpp/src/llama-model-loader.cpp

@@ -2,6 +2,7 @@
 
 #include "ggml.h"
 
+#include <algorithm>
 #include <array>
 #include <cinttypes>
 #include <cstring>
@@ -344,6 +345,7 @@ namespace GGUFMeta {
             GGUFMeta::GKV<GGUFMeta::ArrayInfo>::get_kv(ctx, kid);
 
         switch (arr_info.gt) {
+            case GGUF_TYPE_BOOL:
             case GGUF_TYPE_UINT32:
             case GGUF_TYPE_INT32:   GGML_ASSERT((std::is_same<T,     int32_t>::value) ||
                                                 (std::is_same<T,    uint32_t>::value)); break;
@@ -365,7 +367,13 @@ namespace GGUFMeta {
                 result[i] = value;
             }
         } else {
-            std::copy((const T*)arr_info.data, (const T *)arr_info.data + arr_info.length, result.begin());
+            if (arr_info.gt == GGUF_TYPE_BOOL) {
+                std::transform((const bool *)arr_info.data, (const bool *)arr_info.data + arr_info.length, result.begin(), [](bool x) {
+                    return static_cast<T>(x);
+                });
+            } else {
+                std::copy((const T*)arr_info.data, (const T *)arr_info.data + arr_info.length, result.begin());
+            }
         }
 
         return true;
@@ -462,6 +470,29 @@ namespace GGUFMeta {
         return get_key_or_arr(llm_kv(kid), result, n, required);
     }
 
+    bool llama_model_loader::get_key_or_arr(enum llm_kv kid, uint32_t & result, bool required) {
+        const std::string key = llm_kv(kid);
+
+        const int id = gguf_find_key(meta.get(), key.c_str());
+
+        if (id < 0) {
+            if (required) {
+                throw std::runtime_error(format("key not found in model: %s", key.c_str()));
+            }
+            return false;
+        }
+
+        // throw and error if type is an array
+        if (gguf_get_kv_type(meta.get(), id) == GGUF_TYPE_ARRAY) {
+            if (required) {
+                throw std::runtime_error(format("expected scalar, found array for key: %s", key.c_str()));
+            }
+            return false;
+        }
+
+        return get_key(key, result, required);
+    }
+
     // TODO: this is not very clever - figure out something better
     template bool llama_model_loader::get_key_or_arr<std::array<int, 4>>(enum llm_kv kid, std::array<int, 4> & result, uint32_t n, bool required);
     template bool llama_model_loader::get_key_or_arr<std::array<uint32_t, 512>>(enum llm_kv kid, std::array<uint32_t, 512> & result, uint32_t n, bool required);
@@ -472,6 +503,7 @@ llama_model_loader::llama_model_loader(
         const std::string & fname,
         std::vector<std::string> & splits,
         bool use_mmap,
+        bool use_direct_io,
         bool check_tensors,
         bool no_alloc,
         const llama_model_kv_override * param_overrides_p,
@@ -504,9 +536,23 @@ llama_model_loader::llama_model_loader(
     get_key(llm_kv(LLM_KV_GENERAL_ARCHITECTURE), arch_name, false);
     llm_kv = LLM_KV(llm_arch_from_string(arch_name));
 
-    files.emplace_back(new llama_file(fname.c_str(), "rb"));
+    files.emplace_back(new llama_file(fname.c_str(), "rb", use_direct_io));
     contexts.emplace_back(ctx);
 
+    if (use_mmap && use_direct_io) {
+        if (files.back()->has_direct_io()) {
+            // Disable mmap, as DirectIO is available
+            use_mmap = false;
+            LLAMA_LOG_WARN("%s: direct I/O is enabled, disabling mmap\n", __func__);
+        } else {
+            // Disable DirectIO and reopen file using std::fopen for mmap
+            use_direct_io = false;
+            files.pop_back();
+            files.emplace_back(new llama_file(fname.c_str(), "rb", false));
+            LLAMA_LOG_WARN("%s: direct I/O is not available, using mmap\n", __func__);
+        }
+    }
+
     // Save tensors data offset of the main file.
     // For subsidiary files, `meta` tensor data offset must not be used,
     // so we build a unified tensors index for weights.
@@ -572,7 +618,7 @@ llama_model_loader::llama_model_loader(
                 }
             }
 
-            files.emplace_back(new llama_file(fname_split, "rb"));
+            files.emplace_back(new llama_file(fname_split, "rb", use_direct_io));
             contexts.emplace_back(ctx);
 
             // Save tensors data offset info of the shard.
@@ -716,6 +762,7 @@ llama_model_loader::llama_model_loader(
     }
 
     this->use_mmap = use_mmap;
+    this->use_direct_io = use_direct_io;
     this->check_tensors = check_tensors;
     this->no_alloc = no_alloc;
 }
@@ -935,7 +982,15 @@ bool llama_model_loader::load_all_data(
     // 4 staging buffers for async uploads, each sized 1MB seems to be a good default for single NVMe drives.
     // NVMe raid configurations might require more / larger buffers.
     constexpr size_t n_buffers = 4;
-    constexpr size_t buffer_size = 1 * 1024 * 1024; // 1MB
+
+    size_t alignment = 1;
+    for (const auto & file : files) {
+        alignment = std::max(file->read_alignment(), alignment);
+    }
+
+    // Buffer size: balance between memory usage and I/O efficiency
+    // 64MB works well for NVMe drives
+    const size_t buffer_size = alignment != 1 ? 64 * 1024 * 1024 + 2 * alignment : 1 * 1024 * 1024;
 
     std::vector<ggml_backend_buffer_t> host_buffers;
     std::vector<ggml_backend_event_t> events;
@@ -985,6 +1040,7 @@ bool llama_model_loader::load_all_data(
         // If the backend is supported, create pinned memory buffers and events for synchronisation.
         for (size_t idx = 0; idx < n_buffers; ++idx) {
             auto * buf = ggml_backend_buft_alloc_buffer(host_buft, buffer_size);
+
             if (!buf) {
                 LLAMA_LOG_DEBUG("%s: failed to allocate host buffer for async uploads for device %s\n", func,
                     ggml_backend_dev_name(dev));
@@ -1066,6 +1122,7 @@ bool llama_model_loader::load_all_data(
             }
         } else {
             const auto & file = files.at(weight->idx);
+
             if (ggml_backend_buffer_is_host(cur->buffer)) {
                 file->seek(weight->offs, SEEK_SET);
                 file->read_raw(cur->data, n_size);
@@ -1077,19 +1134,54 @@ bool llama_model_loader::load_all_data(
             } else {
                 // If upload_backend is valid load the tensor in chunks to pinned memory and upload the buffers asynchronously to the GPU.
                 if (upload_backend) {
-                    file->seek(weight->offs, SEEK_SET);
+                    size_t offset = weight->offs;
+                    alignment = file->read_alignment();
+                    size_t aligned_offset = offset & ~(alignment - 1);
+                    size_t offset_from_alignment = offset - aligned_offset;
+                    file->seek(aligned_offset, SEEK_SET);
+
+                    // Calculate aligned read boundaries
+                    size_t read_start = aligned_offset;
+                    size_t read_end = (offset + n_size + alignment - 1) & ~(alignment - 1);
 
                     size_t bytes_read = 0;
+                    size_t data_read = 0;  // Actual tensor data copied (excluding padding)
 
-                    while (bytes_read < n_size) {
-                        size_t read_iteration = std::min<size_t>(buffer_size, n_size - bytes_read);
+                    while (bytes_read < read_end - read_start) {
+                        size_t read_size = std::min<size_t>(buffer_size, read_end - read_start - bytes_read);
 
+                        // Align the destination pointer within the pinned buffer
+                        uintptr_t ptr_dest_aligned = (reinterpret_cast<uintptr_t>(host_ptrs[buffer_idx]) + alignment - 1) & ~(alignment - 1);
+
+                        // Wait for previous upload to complete before reusing buffer
                         ggml_backend_event_synchronize(events[buffer_idx]);
-                        file->read_raw(host_ptrs[buffer_idx], read_iteration);
-                        ggml_backend_tensor_set_async(upload_backend, cur, host_ptrs[buffer_idx], bytes_read, read_iteration);
+
+                        // Read aligned chunk from file
+                        file->read_raw_unsafe(reinterpret_cast<void *>(ptr_dest_aligned), read_size);
+
+                        // Calculate actual data portion (excluding alignment padding)
+                        uintptr_t ptr_data = ptr_dest_aligned;
+                        size_t data_to_copy = read_size;
+
+                        // Skip alignment padding at start of first chunk
+                        if (bytes_read == 0) {
+                            ptr_data += offset_from_alignment;
+                            data_to_copy -= offset_from_alignment;
+                        }
+
+                        // Trim alignment padding at end of last chunk
+                        if (aligned_offset + bytes_read + read_size > offset + n_size) {
+                            data_to_copy -= (read_end - (offset + n_size));
+                        }
+
+                        // Async upload actual data to GPU
+                        ggml_backend_tensor_set_async(upload_backend, cur,
+                                                      reinterpret_cast<void *>(ptr_data), data_read, data_to_copy);
                         ggml_backend_event_record(events[buffer_idx], upload_backend);
 
-                        bytes_read += read_iteration;
+                        data_read += data_to_copy;
+                        bytes_read += read_size;
+
                         ++buffer_idx;
                         buffer_idx %= n_buffers;
                     }

llama/llama.cpp/src/llama-model-loader.h

@@ -70,6 +70,7 @@ struct llama_model_loader {
     size_t   n_bytes    = 0;
 
     bool use_mmap = false;
+    bool use_direct_io = false;
     bool check_tensors;
     bool no_alloc;
 
@@ -97,6 +98,7 @@ struct llama_model_loader {
         const std::string & fname,
         std::vector<std::string> & splits, // optional, only need if the split does not follow naming scheme
         bool use_mmap,
+        bool use_direct_io,
         bool check_tensors,
         bool no_alloc,
         const llama_model_kv_override * param_overrides_p,
@@ -131,6 +133,8 @@ struct llama_model_loader {
     template<typename T>
     bool get_key_or_arr(enum llm_kv kid, T & result, uint32_t n, bool required = true);
 
+    bool get_key_or_arr(enum llm_kv kid, uint32_t & result, bool required = true);
+
     std::string get_arch_name() const;
 
     enum llm_arch get_arch() const;

llama/llama.cpp/src/llama-model-saver.cpp

@@ -146,6 +146,9 @@ void llama_model_saver::add_kv_from_model() {
     add_kv(LLM_KV_VOCAB_SIZE,                        vocab.n_tokens());
     add_kv(LLM_KV_CONTEXT_LENGTH,                    hparams.n_ctx_train);
     add_kv(LLM_KV_EMBEDDING_LENGTH,                  hparams.n_embd);
+    if (hparams.n_embd_out_impl > 0) {
+        add_kv(LLM_KV_EMBEDDING_LENGTH_OUT,          hparams.n_embd_out_impl);
+    }
     add_kv(LLM_KV_BLOCK_COUNT,                       hparams.n_layer);
     add_kv(LLM_KV_LEADING_DENSE_BLOCK_COUNT,         hparams.n_layer_dense_lead);
     add_kv(LLM_KV_FEED_FORWARD_LENGTH,               hparams.n_ff_arr, true);

llama/llama.cpp/src/llama-model.h

@@ -11,6 +11,7 @@
 #include <memory>
 #include <string>
 #include <unordered_map>
+#include <unordered_set>
 #include <vector>
 
 struct llama_cparams;
@@ -24,12 +25,14 @@ enum llm_type {
     LLM_TYPE_17M,
     LLM_TYPE_22M,
     LLM_TYPE_33M,
+    LLM_TYPE_47M,
     LLM_TYPE_60M,
     LLM_TYPE_70M,
     LLM_TYPE_80M,
     LLM_TYPE_109M,
     LLM_TYPE_137M,
     LLM_TYPE_140M,
+    LLM_TYPE_149M,
     LLM_TYPE_160M,
     LLM_TYPE_190M,
     LLM_TYPE_220M,
@@ -39,6 +42,7 @@ enum llm_type {
     LLM_TYPE_335M,
     LLM_TYPE_350M,
     LLM_TYPE_360M,
+    LLM_TYPE_395M,
     LLM_TYPE_410M,
     LLM_TYPE_450M,
     LLM_TYPE_475M,
@@ -117,10 +121,12 @@ enum llm_type {
     LLM_TYPE_31B_A3_5B,
     LLM_TYPE_80B_A3B, // Qwen3 Next
     LLM_TYPE_100B_A6B,
+    LLM_TYPE_102B_A12B, // Solar-Open
     LLM_TYPE_106B_A12B, // GLM-4.5-Air
     LLM_TYPE_230B_A10B, // Minimax M2
     LLM_TYPE_235B_A22B,
     LLM_TYPE_300B_A47B, // Ernie MoE big
+    LLM_TYPE_310B_A15B, // /MiMo-V2-Flash
     LLM_TYPE_355B_A32B, // GLM-4.5
     LLM_TYPE_E2B,
     LLM_TYPE_E4B,
@@ -465,8 +471,6 @@ struct llama_model {
     struct ggml_tensor * dense_2_out_layers = nullptr;
     struct ggml_tensor * dense_3_out_layers = nullptr;
 
-    llama_model_params params;
-
     // gguf metadata
     std::unordered_map<std::string, std::string> gguf_kv;
 
@@ -476,6 +480,9 @@ struct llama_model {
     // for quantize-stats only
     std::vector<std::pair<std::string, struct ggml_tensor *>> tensors_by_name;
 
+    // for keeping track of associated LoRA adapters
+    std::unordered_set<llama_adapter_lora *> loras;
+
     int64_t t_load_us  = 0;
     int64_t t_start_us = 0;
 
@@ -497,6 +504,9 @@ struct llama_model {
     size_t n_tensors() const;
     size_t n_devices() const;
 
+    uint32_t n_gpu_layers() const;
+    llama_split_mode split_mode() const;
+
     std::map<ggml_backend_buffer_type_t, size_t> memory_breakdown() const;
 
     // total number of parameters in the model
@@ -525,6 +535,8 @@ struct llama_model {
     ggml_cgraph * build_graph(const llm_graph_params & params) const;
 
 private:
+    llama_model_params params;
+
     struct impl;
     std::unique_ptr<impl> pimpl;
 };

llama/llama.cpp/src/llama-quant.cpp

@@ -422,57 +422,6 @@ static ggml_type llama_tensor_get_type(quantize_state_impl & qs, ggml_type new_t
         ++qs.i_ffn_up;
     }
 
-    //    if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;
-    //}
-    // IK: let's remove this, else Q2_K is almost the same as Q3_K_S
-    //else if (name.find("ffn_gate") != std::string::npos || name.find("ffn_up") != std::string::npos) {
-    //    if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;
-    //}
-    // This can be used to reduce the size of the Q5_K_S model.
-    // The associated PPL increase is fully in line with the size reduction
-    //else {
-    //    if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_S) new_type = GGML_TYPE_Q4_K;
-    //}
-    bool convert_incompatible_tensor = false;
-    {
-        const int64_t nx = tensor->ne[0];
-        const int64_t ny = tensor->ne[1];
-        const int64_t qk_k = ggml_blck_size(new_type);
-
-        if (nx % qk_k != 0) {
-            LLAMA_LOG_WARN("\n\n%s : tensor cols %" PRId64 " x %" PRId64 " are not divisible by %" PRId64 ", required for %s", __func__, nx, ny, qk_k, ggml_type_name(new_type));
-            convert_incompatible_tensor = true;
-        } else {
-            ++qs.n_k_quantized;
-        }
-    }
-
-    if (convert_incompatible_tensor) {
-        switch (new_type) {
-            case GGML_TYPE_TQ1_0:
-            case GGML_TYPE_TQ2_0:  new_type = GGML_TYPE_Q4_0; break;  // TODO: use a symmetric type instead
-            case GGML_TYPE_IQ2_XXS:
-            case GGML_TYPE_IQ2_XS:
-            case GGML_TYPE_IQ2_S:
-            case GGML_TYPE_IQ3_XXS:
-            case GGML_TYPE_IQ3_S:
-            case GGML_TYPE_IQ1_S:
-            case GGML_TYPE_IQ1_M:
-            case GGML_TYPE_Q2_K:
-            case GGML_TYPE_Q3_K:
-            case GGML_TYPE_IQ4_XS: new_type = GGML_TYPE_IQ4_NL; break;
-            case GGML_TYPE_Q4_K:   new_type = GGML_TYPE_Q5_0;   break;
-            case GGML_TYPE_Q5_K:   new_type = GGML_TYPE_Q5_1;   break;
-            case GGML_TYPE_Q6_K:   new_type = GGML_TYPE_Q8_0;   break;
-            default: throw std::runtime_error("\nUnsupported tensor size encountered\n");
-        }
-        if (tensor->ne[0] % ggml_blck_size(new_type) != 0) {
-            new_type = GGML_TYPE_F16;
-        }
-        LLAMA_LOG_WARN(" - using fallback quantization %s\n", ggml_type_name(new_type));
-        ++qs.n_fallback;
-    }
-
     return new_type;
 }
 
@@ -596,7 +545,7 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
     }
 
     std::vector<std::string> splits = {};
-    llama_model_loader ml(fname_inp, splits, use_mmap, /*check_tensors*/ true, /*no_alloc*/ false, kv_overrides, nullptr);
+    llama_model_loader ml(fname_inp, splits, use_mmap, /*use_direct_io*/ true, /*check_tensors*/ true, /*no_alloc*/ false, kv_overrides, nullptr);
     ml.init_mappings(false); // no prefetching
 
     llama_model model(llama_model_default_params());
@@ -875,21 +824,69 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
 
             // get more optimal quantization type based on the tensor shape, layer, etc.
             if (!params->pure && ggml_is_quantized(default_type)) {
-                int fallback = qs.n_fallback;
-                new_type = llama_tensor_get_type(qs, new_type, tensor, ftype);
-                // unless the user specifies a type, and the tensor geometry will not require fallback quantisation
-                if (params->tensor_types && qs.n_fallback - fallback == 0) {
+                // if the user provided tensor types - use those
+                bool manual = false;
+                if (params->tensor_types) {
                     const std::vector<tensor_quantization> & tensor_types = *static_cast<const std::vector<tensor_quantization> *>(params->tensor_types);
                     const std::string tensor_name(tensor->name);
                     for (const auto & [tname, qtype] : tensor_types) {
                         if (std::regex pattern(tname); std::regex_search(tensor_name, pattern)) {
                             if  (qtype != new_type) {
-                                LLAMA_LOG_DEBUG("(overriding %s) ", ggml_type_name(new_type));
+                                LLAMA_LOG_WARN("(manual override: %s -> %s) ", ggml_type_name(new_type), ggml_type_name(qtype));
                                 new_type = qtype; // if two or more types are specified for the same tensor, the last match wins
+                                manual = true;
+                                break;
                             }
                         }
                     }
                 }
+
+                // if not manual - use the standard logic for choosing the quantization type based on the selected mixture
+                if (!manual) {
+                    new_type = llama_tensor_get_type(qs, new_type, tensor, ftype);
+                }
+
+                // incompatible tensor shapes are handled here - fallback to a compatible type
+                {
+                    bool convert_incompatible_tensor = false;
+
+                    const int64_t nx = tensor->ne[0];
+                    const int64_t ny = tensor->ne[1];
+                    const int64_t qk_k = ggml_blck_size(new_type);
+
+                    if (nx % qk_k != 0) {
+                        LLAMA_LOG_WARN("\n\n%s : tensor cols %" PRId64 " x %" PRId64 " are not divisible by %" PRId64 ", required for %s", __func__, nx, ny, qk_k, ggml_type_name(new_type));
+                        convert_incompatible_tensor = true;
+                    } else {
+                        ++qs.n_k_quantized;
+                    }
+
+                    if (convert_incompatible_tensor) {
+                        switch (new_type) {
+                            case GGML_TYPE_TQ1_0:
+                            case GGML_TYPE_TQ2_0:  new_type = GGML_TYPE_Q4_0; break;  // TODO: use a symmetric type instead
+                            case GGML_TYPE_IQ2_XXS:
+                            case GGML_TYPE_IQ2_XS:
+                            case GGML_TYPE_IQ2_S:
+                            case GGML_TYPE_IQ3_XXS:
+                            case GGML_TYPE_IQ3_S:
+                            case GGML_TYPE_IQ1_S:
+                            case GGML_TYPE_IQ1_M:
+                            case GGML_TYPE_Q2_K:
+                            case GGML_TYPE_Q3_K:
+                            case GGML_TYPE_IQ4_XS: new_type = GGML_TYPE_IQ4_NL; break;
+                            case GGML_TYPE_Q4_K:   new_type = GGML_TYPE_Q5_0;   break;
+                            case GGML_TYPE_Q5_K:   new_type = GGML_TYPE_Q5_1;   break;
+                            case GGML_TYPE_Q6_K:   new_type = GGML_TYPE_Q8_0;   break;
+                            default: throw std::runtime_error("\nUnsupported tensor size encountered\n");
+                        }
+                        if (tensor->ne[0] % ggml_blck_size(new_type) != 0) {
+                            new_type = GGML_TYPE_F16;
+                        }
+                        LLAMA_LOG_WARN(" - using fallback quantization %s\n", ggml_type_name(new_type));
+                        ++qs.n_fallback;
+                    }
+                }
             }
             if (params->token_embedding_type < GGML_TYPE_COUNT && strcmp(tensor->name, "token_embd.weight") == 0) {
                 new_type = params->token_embedding_type;

llama/llama.cpp/src/llama-sampling.h

@@ -14,7 +14,19 @@ struct llama_grammar;
 struct llama_sampler_chain {
     llama_sampler_chain_params params;
 
-    std::vector<struct llama_sampler *> samplers;
+    // has .backend_init() been called?
+    bool is_init = false;
+
+    struct info {
+        bool is_backend;
+
+        llama_sampler * ptr;
+    };
+
+    std::vector<info> samplers;
+
+    // pre-allocated buffer for llama_sampler_sample to avoid repeated allocations
+    std::vector<llama_token_data> cur;
 
     // timing
 
@@ -24,9 +36,9 @@ struct llama_sampler_chain {
 };
 
 struct llama_sampler * llama_sampler_init_dry_testing(
-                         int32_t   context_size,
-                           float   dry_multiplier,
-                           float   dry_base,
-                         int32_t   dry_allowed_length,
-                         int32_t   dry_penalty_last_n,
-  const std::vector<std::vector<llama_token>>& seq_breakers);
+        int32_t context_size,
+        float   dry_multiplier,
+        float   dry_base,
+        int32_t dry_allowed_length,
+        int32_t dry_penalty_last_n,
+        const std::vector<std::vector<llama_token>> & seq_breakers);

llama/llama.cpp/src/llama-vocab.cpp

@@ -314,6 +314,12 @@ struct llm_tokenizer_bpe : llm_tokenizer {
                     "[!\"#$%&'()*+,\\-./:;<=>?@\\[\\\\\\]^_`{|}~][A-Za-z]+|[^\r\n\\p{L}\\p{P}\\p{S}]?[\\p{L}\\p{M}]+| ?[\\p{P}\\p{S}]+[\r\n]*|\\s*[\r\n]+|\\s+(?!\\S)|\\s+",
                 };
                 break;
+            case LLAMA_VOCAB_PRE_TYPE_YOUTU:
+                regex_exprs = {
+                    "[가-힣ㄱ-ㆎ]+|[！…“”‘’—：；，、-〿︰-﹏]+|[ㄅ-ㄯ]+|[一-龥぀-ゟ゠-ヿ]+",
+                    "[^\\r\\n\\p{L}\\p{N}]?[\\p{Lu}\\p{Lt}\\p{Lm}\\p{Lo}\\p{M}]*[\\p{Ll}\\p{Lm}\\p{Lo}\\p{M}]+(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])?|[^\\r\\n\\p{L}\\p{N}]?[\\p{Lu}\\p{Lt}\\p{Lm}\\p{Lo}\\p{M}]+[\\p{Ll}\\p{Lm}\\p{Lo}\\p{M}]*(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])?|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n/]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
+                };
+                break;
             case LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_CODER:
                 regex_exprs = {
                     "[\r\n]",
@@ -355,6 +361,7 @@ struct llm_tokenizer_bpe : llm_tokenizer {
             case LLAMA_VOCAB_PRE_TYPE_STABLELM2:
             case LLAMA_VOCAB_PRE_TYPE_QWEN2:
             case LLAMA_VOCAB_PRE_TYPE_HUNYUAN:
+            case LLAMA_VOCAB_PRE_TYPE_SOLAR_OPEN:
                 regex_exprs = {
                     // original regex from tokenizer.json
                     // "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+"
@@ -454,6 +461,13 @@ struct llm_tokenizer_bpe : llm_tokenizer {
                     "[!\"#$%&'()*+,\\-./:;<=>?@\\[\\\\\\]^_`{|}~][A-Za-z]+|[^\\r\\n\\p{L}\\p{P}\\p{S}]?[\\p{L}\\p{M}]+| ?[\\p{P}\\p{S}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
                 };
                 break;
+            case LLAMA_VOCAB_PRE_TYPE_EXAONE_MOE:
+                regex_exprs = {
+                    // original regex from tokenizer.json
+                    // "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?(?:\\p{L}\\p{M}*(?: \\p{L}\\p{M}*)*)+|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n/]?|\\s*[\\r\\n]|\\s+(?!\\S)|\\s+"
+                    "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?(?:\\p{L}\\p{M}*(?: \\p{L}\\p{M}*)*)+|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n/]?|\\s*[\\r\\n]|\\s+(?!\\S)|\\s+",
+                };
+                break;
             default:
                 // default regex for BPE tokenization pre-processing
                 regex_exprs = {
@@ -1849,6 +1863,11 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
                     tokenizer_pre == "deepseek-v3") {
                 pre_type = LLAMA_VOCAB_PRE_TYPE_DEEPSEEK3_LLM;
                 clean_spaces = false;
+            } else if (
+                    tokenizer_pre == "youtu") {
+                pre_type = LLAMA_VOCAB_PRE_TYPE_YOUTU;
+                clean_spaces = false;
+                ignore_merges = true;
             } else if (
                     tokenizer_pre == "falcon") {
                 pre_type = LLAMA_VOCAB_PRE_TYPE_FALCON;
@@ -1867,7 +1886,8 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
                     tokenizer_pre == "jina-v2-es" ||
                     tokenizer_pre == "jina-v2-de" ||
                     tokenizer_pre == "a.x-4.0" ||
-                    tokenizer_pre == "mellum") {
+                    tokenizer_pre == "mellum"  ||
+                    tokenizer_pre == "modern-bert" ) {
                 pre_type = LLAMA_VOCAB_PRE_TYPE_GPT2;
             } else if (
                     tokenizer_pre == "jina-v1-en" ||
@@ -1941,6 +1961,9 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
             } else if (
                 tokenizer_pre == "exaone4") {
                 pre_type = LLAMA_VOCAB_PRE_TYPE_GPT2;
+            } else if (
+                tokenizer_pre == "exaone-moe") {
+                pre_type = LLAMA_VOCAB_PRE_TYPE_EXAONE_MOE;
             } else if (
                 tokenizer_pre == "chameleon") {
                 pre_type = LLAMA_VOCAB_PRE_TYPE_CHAMELEON;
@@ -2003,6 +2026,10 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
                 tokenizer_pre == "minimax-m2") {
                 pre_type = LLAMA_VOCAB_PRE_TYPE_MINIMAX_M2;
                 clean_spaces = false;
+            } else if (
+                tokenizer_pre == "solar-open") {
+                pre_type = LLAMA_VOCAB_PRE_TYPE_SOLAR_OPEN;
+                clean_spaces = false;
             } else {
                 LLAMA_LOG_WARN("%s: missing or unrecognized pre-tokenizer type, using: 'default'\n", __func__);
                 pre_type = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
@@ -2049,6 +2076,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
         scores = (const float * ) gguf_get_arr_data(ctx, score_idx);
     }
 
+    const uint32_t n_scores = score_idx != -1 ? gguf_get_arr_n(ctx, score_idx) : 0;
     const int * toktypes = nullptr;
     const int toktype_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_TOKEN_TYPE).c_str());
     if (toktype_idx != -1) {
@@ -2070,7 +2098,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
 
         auto & token_data = id_to_token[i];
         token_data.text  = std::move(word);
-        token_data.score = scores ? scores[i] : 0.0f;
+        token_data.score = (scores && i < n_scores) ? scores[i] : 0.0f;
         token_data.attr  = LLAMA_TOKEN_ATTR_NORMAL;
 
         if (toktypes) {  //TODO: remove, required until per token attributes are available from GGUF file
@@ -2176,6 +2204,8 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
         //       for now, we apply this workaround to find the tokens based on their text
 
         for (const auto & t : token_to_id) {
+            auto & attr = id_to_token[t.second].attr;
+
             // find EOT token: "<|eot_id|>", "<|im_end|>", "<end_of_turn>", etc.
             if (special_eot_id == LLAMA_TOKEN_NULL) {
                 if (false
@@ -2191,10 +2221,10 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
                         || t.first == "<end_of_utterance>" // smoldocling
                    ) {
                     special_eot_id = t.second;
-                    if ((id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
+                    if ((attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
                         LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
                                 __func__, t.second, t.first.c_str());
-                        id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
+                        attr = (llama_token_attr) (attr | LLAMA_TOKEN_ATTR_CONTROL);
                     }
                 }
             }
@@ -2205,10 +2235,10 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
                         || t.first == "<|eom_id|>"
                         ) {
                     special_eom_id = t.second;
-                    if ((id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
+                    if ((attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
                         LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
                                 __func__, t.second, t.first.c_str());
-                        id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
+                        attr = (llama_token_attr) (attr | LLAMA_TOKEN_ATTR_CONTROL);
                     }
                 }
             }
@@ -2225,10 +2255,10 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
                         || t.first == "<|code_prefix|>" // GLM-4.5
                         ) {
                     special_fim_pre_id = t.second;
-                    if ((id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
+                    if ((attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
                         LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
                                 __func__, t.second, t.first.c_str());
-                        id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
+                        attr = (llama_token_attr) (attr | LLAMA_TOKEN_ATTR_CONTROL);
                     }
                 }
             }
@@ -2245,10 +2275,10 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
                         || t.first == "<|code_suffix|>" // GLM-4.5
                         ) {
                     special_fim_suf_id = t.second;
-                    if ((id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
+                    if ((attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
                         LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
                                 __func__, t.second, t.first.c_str());
-                        id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
+                        attr = (llama_token_attr) (attr | LLAMA_TOKEN_ATTR_CONTROL);
                     }
                 }
             }
@@ -2265,10 +2295,10 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
                         || t.first == "<|code_middle|>" // GLM-4.5
                         ) {
                     special_fim_mid_id = t.second;
-                    if ((id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
+                    if ((attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
                         LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
                                 __func__, t.second, t.first.c_str());
-                        id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
+                        attr = (llama_token_attr) (attr | LLAMA_TOKEN_ATTR_CONTROL);
                     }
                 }
             }
@@ -2282,10 +2312,10 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
                         || t.first == "<PAD>"
                         ) {
                     special_fim_pad_id = t.second;
-                    if ((id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
+                    if ((attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
                         LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
                                 __func__, t.second, t.first.c_str());
-                        id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
+                        attr = (llama_token_attr) (attr | LLAMA_TOKEN_ATTR_CONTROL);
                     }
                 }
             }
@@ -2300,10 +2330,10 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
                         || t.first == "<reponame>"    // Granite
                         ) {
                     special_fim_rep_id = t.second;
-                    if ((id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
+                    if ((attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
                         LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
                                 __func__, t.second, t.first.c_str());
-                        id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
+                        attr = (llama_token_attr) (attr | LLAMA_TOKEN_ATTR_CONTROL);
                     }
                 }
             }
@@ -2314,15 +2344,41 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
                         || t.first == "<|file_sep|>" // Qwen
                         ) {
                     special_fim_sep_id = t.second;
-                    if ((id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
+                    if ((attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
                         LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
                                 __func__, t.second, t.first.c_str());
-                        id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
+                        attr = (llama_token_attr) (attr | LLAMA_TOKEN_ATTR_CONTROL);
                     }
                 }
             }
         }
 
+        // auto-detect unused tokens: e.g. control tokens with the word "unused"
+        // ideally, these tokens should be marked as unused during conversion
+        {
+            uint32_t n_unused = 0;
+
+            for (const auto & t : token_to_id) {
+                auto & attr = id_to_token[t.second].attr;
+
+                if ((attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
+                    continue;
+                }
+
+                if ((attr & LLAMA_TOKEN_ATTR_UNUSED) == 0) {
+                    if (strstr(t.first.c_str(), "unused") != NULL) {
+                        attr = (llama_token_attr) (attr | LLAMA_TOKEN_ATTR_UNUSED);
+                    }
+                }
+
+                if (attr & LLAMA_TOKEN_ATTR_UNUSED) {
+                    n_unused++;
+                }
+            }
+
+            LLAMA_LOG_INFO("%s: %u unused tokens\n", __func__, n_unused);
+        }
+
         // maintain a list of tokens that cause end-of-generation
         // this is currently determined based on the token text, which is obviously not ideal
         // ref: https://github.com/ggerganov/llama.cpp/issues/9606
@@ -2341,12 +2397,16 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
         }
 
         for (const auto & t : token_to_id) {
+            auto & attr = id_to_token[t.second].attr;
+
             if (false
                     || t.first == "<|eot_id|>"
                     || t.first == "<|im_end|>"
                     || t.first == "<|end|>"
                     || t.first == "<|return|>" // o200k_harmony
                     || t.first == "<|call|>"   // o200k_harmony
+                    || t.first == "<|flush|>"  // solar-open
+                    || t.first == "<|calls|>"  // solar-open
                     || t.first == "<end_of_turn>"
                     || t.first == "<|endoftext|>"
                     || t.first == "<|eom_id|>"
@@ -2356,24 +2416,31 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
                     || t.first == "<end_of_utterance>" // smoldocling
                ) {
                 special_eog_ids.insert(t.second);
-                if ((id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
+                if ((attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
                     LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
                             __func__, t.second, t.first.c_str());
-                    id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
+                    attr = (llama_token_attr) (attr | LLAMA_TOKEN_ATTR_CONTROL);
                 }
             } else {
-                // token is control, but not marked as EOG -> print a debug log
-                if (id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL && special_eog_ids.count(t.second) == 0) {
-                    LLAMA_LOG_DEBUG("%s: control token: %6d '%s' is not marked as EOG\n",
-                            __func__, t.second, t.first.c_str());
+                if (attr & LLAMA_TOKEN_ATTR_CONTROL && !(attr & LLAMA_TOKEN_ATTR_UNUSED)) {
+                    // token is control, but not marked as EOG -> print a debug log
+                    if (special_eog_ids.count(t.second) == 0) {
+                        LLAMA_LOG_DEBUG("%s: control token: %6d '%s' is not marked as EOG\n",
+                                __func__, t.second, t.first.c_str());
+                    }
                 }
             }
         }
 
         // @ngxson : quick hack for gpt-oss, always render these tokens
         for (const auto & t : token_to_id) {
+            auto & attr = id_to_token[t.second].attr;
+
             if (t.first == "<|channel|>" || t.first == "<|message|>" || t.first == "<|start|>" || t.first == "<|constrain|>") {
-                id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_USER_DEFINED;
+                LLAMA_LOG_WARN("%s: setting token '%s' (%d) attribute to USER_DEFINED (%u), old attributes: %u\n",
+                        __func__, t.first.c_str(), t.second, LLAMA_TOKEN_ATTR_USER_DEFINED, attr);
+
+                attr = LLAMA_TOKEN_ATTR_USER_DEFINED;
             }
         }
 
@@ -2393,34 +2460,42 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
             LLAMA_LOG_WARN("%s: special_eom_id is not in special_eog_ids - the tokenizer config may be incorrect\n", __func__);
         }
 
-        // TODO: workaround for o200k_harmony tokenizer: the "<|end|>" token should not be EOG
-        //       we don't have a good way to detect this, so for now, if we have "<|return|>" and "<|call|>" tokens,
+        // TODO: workaround for o200k_harmony and solar-open tokenizer: the "<|end|>" token should not be EOG
+        //       we don't have a good way to detect this, so for now, if we have "<|return|>" and "<|call|>" tokens ("<|calls|>" and "<|flush|>" for solar-open),
         //       we remove the "<|end|>" token from the EOG list
         {
             bool has_return = false;
             bool has_call   = false;
             bool has_end    = false;
+            bool has_flush  = false;
 
             llama_token end_id = LLAMA_TOKEN_NULL;
 
             LLAMA_LOG_INFO("%s: printing all EOG tokens:\n", __func__);
             for (auto tid : special_eog_ids) {
-                LLAMA_LOG_INFO("%s:   - %d ('%s')\n", __func__, tid, id_to_token[tid].text.c_str());
+                auto & text = id_to_token[tid].text;
 
-                if (id_to_token[tid].text == "<|return|>") {
+                LLAMA_LOG_INFO("%s:   - %d ('%s')\n", __func__, tid, text.c_str());
+
+                if (text == "<|return|>") {
                     has_return = true;
-                } else if (id_to_token[tid].text == "<|call|>") {
+                } else if (text == "<|call|>" || text == "<|calls|>") {
                     has_call = true;
-                } else if (id_to_token[tid].text == "<|end|>") {
+                } else if (text == "<|flush|>") {
+                    has_flush = true;
+                } else if (text == "<|end|>") {
                     has_end = true;
                     end_id = tid;
                 }
             }
 
-            if (has_return && has_call && has_end) {
+            if ((has_return && has_call && has_end) || (has_call && has_flush && has_end)) {
                 special_eog_ids.erase(end_id);
-                id_to_token[end_id].attr = LLAMA_TOKEN_ATTR_USER_DEFINED;
-                LLAMA_LOG_WARN("%s: special_eog_ids contains both '<|return|>' and '<|call|>' tokens, removing '<|end|>' token from EOG list\n", __func__);
+
+                auto & attr = id_to_token[end_id].attr;
+                attr = LLAMA_TOKEN_ATTR_USER_DEFINED;
+
+                LLAMA_LOG_WARN("%s: special_eog_ids contains both '<|return|>' and '<|call|>', or '<|calls|>' and '<|flush|>' tokens, removing '<|end|>' token from EOG list\n", __func__);
             }
         }
     }
@@ -2518,6 +2593,13 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
             for (const auto * token : {"<unk>", "<s>", "<|endoftext|>"}) {
                 _set_token_attr(token, LLAMA_TOKEN_ATTR_RSTRIP, false);
             }
+        } else if (_contains_any(model_name, {"modern-bert"})) {
+            if (token_to_id.count("[MASK]") == 0 ) {
+                LLAMA_LOG_WARN("%s: Mask token missing in vocab!\n", __func__);
+            }
+            else {
+                _set_token_attr("[MASK]", LLAMA_TOKEN_ATTR_LSTRIP, true);
+            }
         }
     }
 }
@@ -3211,34 +3293,34 @@ int32_t llama_vocab::impl::detokenize(
 }
 
 void llama_vocab::impl::print_info() const {
-    LLAMA_LOG_INFO("%s: vocab type       = %s\n",     __func__, type_name().c_str());
-    LLAMA_LOG_INFO("%s: n_vocab          = %u\n",     __func__, vocab.n_tokens());
-    LLAMA_LOG_INFO("%s: n_merges         = %u\n",     __func__, (uint32_t) bpe_ranks.size());
+    LLAMA_LOG_INFO("%s: vocab type            = %s\n",     __func__, type_name().c_str());
+    LLAMA_LOG_INFO("%s: n_vocab               = %u\n",     __func__, vocab.n_tokens());
+    LLAMA_LOG_INFO("%s: n_merges              = %u\n",     __func__, (uint32_t) bpe_ranks.size());
 
     // special tokens
-    if (special_bos_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: BOS token        = %d '%s'\n", __func__, special_bos_id,     id_to_token.at(special_bos_id).text.c_str() );  }
-    if (special_eos_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: EOS token        = %d '%s'\n", __func__, special_eos_id,     id_to_token.at(special_eos_id).text.c_str() );  }
-    if (special_eot_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: EOT token        = %d '%s'\n", __func__, special_eot_id,     id_to_token.at(special_eot_id).text.c_str() );  }
-    if (special_eom_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: EOM token        = %d '%s'\n", __func__, special_eom_id,     id_to_token.at(special_eom_id).text.c_str() );  }
-    if (special_unk_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: UNK token        = %d '%s'\n", __func__, special_unk_id,     id_to_token.at(special_unk_id).text.c_str() );  }
-    if (special_sep_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: SEP token        = %d '%s'\n", __func__, special_sep_id,     id_to_token.at(special_sep_id).text.c_str() );  }
-    if (special_pad_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: PAD token        = %d '%s'\n", __func__, special_pad_id,     id_to_token.at(special_pad_id).text.c_str() );  }
-    if (special_mask_id != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: MASK token       = %d '%s'\n", __func__, special_mask_id,    id_to_token.at(special_mask_id).text.c_str() ); }
+    if (special_bos_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: BOS token             = %d '%s'\n", __func__, special_bos_id,     id_to_token.at(special_bos_id).text.c_str() );  }
+    if (special_eos_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: EOS token             = %d '%s'\n", __func__, special_eos_id,     id_to_token.at(special_eos_id).text.c_str() );  }
+    if (special_eot_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: EOT token             = %d '%s'\n", __func__, special_eot_id,     id_to_token.at(special_eot_id).text.c_str() );  }
+    if (special_eom_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: EOM token             = %d '%s'\n", __func__, special_eom_id,     id_to_token.at(special_eom_id).text.c_str() );  }
+    if (special_unk_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: UNK token             = %d '%s'\n", __func__, special_unk_id,     id_to_token.at(special_unk_id).text.c_str() );  }
+    if (special_sep_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: SEP token             = %d '%s'\n", __func__, special_sep_id,     id_to_token.at(special_sep_id).text.c_str() );  }
+    if (special_pad_id  != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: PAD token             = %d '%s'\n", __func__, special_pad_id,     id_to_token.at(special_pad_id).text.c_str() );  }
+    if (special_mask_id != LLAMA_TOKEN_NULL)    { LLAMA_LOG_INFO( "%s: MASK token            = %d '%s'\n", __func__, special_mask_id,    id_to_token.at(special_mask_id).text.c_str() ); }
 
-    if (linefeed_id != LLAMA_TOKEN_NULL)        { LLAMA_LOG_INFO( "%s: LF token         = %d '%s'\n", __func__, linefeed_id,        id_to_token.at(linefeed_id).text.c_str() ); }
+    if (linefeed_id != LLAMA_TOKEN_NULL)        { LLAMA_LOG_INFO( "%s: LF token              = %d '%s'\n", __func__, linefeed_id,        id_to_token.at(linefeed_id).text.c_str() ); }
 
-    if (special_fim_pre_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: FIM PRE token    = %d '%s'\n", __func__, special_fim_pre_id, id_to_token.at(special_fim_pre_id).text.c_str() ); }
-    if (special_fim_suf_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: FIM SUF token    = %d '%s'\n", __func__, special_fim_suf_id, id_to_token.at(special_fim_suf_id).text.c_str() ); }
-    if (special_fim_mid_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: FIM MID token    = %d '%s'\n", __func__, special_fim_mid_id, id_to_token.at(special_fim_mid_id).text.c_str() ); }
-    if (special_fim_pad_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: FIM PAD token    = %d '%s'\n", __func__, special_fim_pad_id, id_to_token.at(special_fim_pad_id).text.c_str() ); }
-    if (special_fim_rep_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: FIM REP token    = %d '%s'\n", __func__, special_fim_rep_id, id_to_token.at(special_fim_rep_id).text.c_str() ); }
-    if (special_fim_sep_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: FIM SEP token    = %d '%s'\n", __func__, special_fim_sep_id, id_to_token.at(special_fim_sep_id).text.c_str() ); }
+    if (special_fim_pre_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: FIM PRE token         = %d '%s'\n", __func__, special_fim_pre_id, id_to_token.at(special_fim_pre_id).text.c_str() ); }
+    if (special_fim_suf_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: FIM SUF token         = %d '%s'\n", __func__, special_fim_suf_id, id_to_token.at(special_fim_suf_id).text.c_str() ); }
+    if (special_fim_mid_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: FIM MID token         = %d '%s'\n", __func__, special_fim_mid_id, id_to_token.at(special_fim_mid_id).text.c_str() ); }
+    if (special_fim_pad_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: FIM PAD token         = %d '%s'\n", __func__, special_fim_pad_id, id_to_token.at(special_fim_pad_id).text.c_str() ); }
+    if (special_fim_rep_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: FIM REP token         = %d '%s'\n", __func__, special_fim_rep_id, id_to_token.at(special_fim_rep_id).text.c_str() ); }
+    if (special_fim_sep_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: FIM SEP token         = %d '%s'\n", __func__, special_fim_sep_id, id_to_token.at(special_fim_sep_id).text.c_str() ); }
 
     for (const auto & id : special_eog_ids) {
-        LLAMA_LOG_INFO( "%s: EOG token        = %d '%s'\n", __func__, id, id_to_token.at(id).text.c_str() );
+        LLAMA_LOG_INFO( "%s: EOG token             = %d '%s'\n", __func__, id, id_to_token.at(id).text.c_str() );
     }
 
-    LLAMA_LOG_INFO("%s: max token length = %d\n", __func__, max_token_len);
+    LLAMA_LOG_INFO("%s: max token length      = %d\n", __func__, max_token_len);
 }
 
 llama_vocab::llama_vocab() : pimpl(new impl(*this)) {

llama/llama.cpp/src/llama-vocab.h

@@ -51,6 +51,9 @@ enum llama_vocab_pre_type {
     LLAMA_VOCAB_PRE_TYPE_GRANITE_DOCLING = 40,
     LLAMA_VOCAB_PRE_TYPE_MINIMAX_M2      = 41,
     LLAMA_VOCAB_PRE_TYPE_AFMOE           = 42,
+    LLAMA_VOCAB_PRE_TYPE_SOLAR_OPEN      = 43,
+    LLAMA_VOCAB_PRE_TYPE_YOUTU           = 44,
+    LLAMA_VOCAB_PRE_TYPE_EXAONE_MOE      = 45,
 };
 
 struct LLM_KV;

llama/llama.cpp/src/llama.cpp

@@ -71,8 +71,9 @@ static std::vector<llama_device_memory_data> llama_get_device_memory_data(
     }, &ud);
 
     llama_model_params mparams_copy = *mparams;
-    mparams_copy.no_alloc = true;
-    mparams_copy.use_mmap = false;
+    mparams_copy.no_alloc  = true;
+    mparams_copy.use_mmap  = false;
+    mparams_copy.use_mlock = false;
 
     llama_model * model = llama_model_load_from_file(path_model, mparams_copy);
     if (model == nullptr) {
@@ -110,8 +111,20 @@ static std::vector<llama_device_memory_data> llama_get_device_memory_data(
         }
     }
     for (size_t i = 0; i < ret.size(); i++) {
-        size_t free, total;
+        size_t free;
+        size_t total;
         ggml_backend_dev_memory(model->devices[i], &free, &total);
+
+        // devices can return 0 bytes for free and total memory if they do not
+        // have any to report. in this case, we will use the host memory as a fallback
+        // fixes: https://github.com/ggml-org/llama.cpp/issues/18577
+        if (free == 0 && total == 0) {
+            ggml_backend_dev_t cpu_dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
+            if (cpu_dev == nullptr) {
+                throw std::runtime_error(format("%s: no CPU backend found", __func__));
+            }
+            ggml_backend_dev_memory(cpu_dev, &free, &total);
+        }
         ret[i].free  = free;
         ret[i].total = total;
     }
@@ -139,12 +152,15 @@ enum layer_fraction_t {
 };
 // this enum is only used in llama_params_fit_impl but needs to be defined outside of it to fix a Windows compilation issue
 
+class llama_params_fit_exception : public std::runtime_error {
+    using std::runtime_error::runtime_error;
+};
+
 static void llama_params_fit_impl(
         const char * path_model, struct llama_model_params * mparams, struct llama_context_params * cparams,
         float * tensor_split, struct llama_model_tensor_buft_override * tensor_buft_overrides,
-        size_t margin_s, uint32_t n_ctx_min, enum ggml_log_level log_level) {
+        size_t * margins_s, uint32_t n_ctx_min, enum ggml_log_level log_level) {
     constexpr int64_t MiB = 1024*1024;
-    const int64_t margin = margin_s; // this function uses int64_t rather than size_t for memory sizes to more conveniently handle deficits
     typedef std::vector<llama_device_memory_data> dmds_t;
     const llama_model_params default_mparams = llama_model_default_params();
 
@@ -163,6 +179,12 @@ static void llama_params_fit_impl(
         return;
     }
 
+    std::vector<int64_t> margins; // this function uses int64_t rather than size_t for memory sizes to more conveniently handle deficits
+    margins.reserve(nd);
+    for (size_t id = 0; id < nd; id++) {
+        margins.push_back(margins_s[id]);
+    }
+
     std::vector<std::string> dev_names;
     {
         dev_names.reserve(nd);
@@ -180,11 +202,12 @@ static void llama_params_fit_impl(
         }
     }
 
-    int64_t sum_total          = 0;
-    int64_t sum_projected_free = 0;
-    int64_t min_projected_free = INT64_MAX;
-    int64_t sum_projected_used = 0;
-    int64_t sum_projected_ctx  = 0;
+    int64_t sum_free            = 0;
+    int64_t sum_projected_free  = 0;
+    int64_t sum_projected_used  = 0;
+    int64_t sum_projected_model = 0;
+    std::vector<int64_t> projected_free_per_device;
+    projected_free_per_device.reserve(nd);
 
     if (nd > 1) {
         LLAMA_LOG_INFO("%s: projected memory use with initial parameters [MiB]:\n", __func__);
@@ -194,63 +217,106 @@ static void llama_params_fit_impl(
 
         const int64_t projected_used = dmd.mb.total();
         const int64_t projected_free = dmd.free - projected_used;
+        projected_free_per_device.push_back(projected_free);
 
-        sum_total          += dmd.total;
-        sum_projected_used += projected_used;
-        sum_projected_free += projected_free;
-        min_projected_free  = std::min(min_projected_free, projected_free);
-        sum_projected_ctx  += dmd.mb.context;
+        sum_free            += dmd.free;
+        sum_projected_used  += projected_used;
+        sum_projected_free  += projected_free;
+        sum_projected_model += dmd.mb.model;
 
         if (nd > 1) {
-            LLAMA_LOG_INFO("%s:   - %s: %6" PRId64 " total, %6" PRId64 " used, %6" PRId64 " %s\n",
-                __func__, dev_names[id].c_str(), dmd.total/MiB, projected_used/MiB, std::abs(projected_free)/MiB,
-                projected_free >= 0 ? "surplus" : "deficit");
+            LLAMA_LOG_INFO("%s:   - %s: %6" PRId64 " total, %6" PRId64 " used, %6" PRId64 " free vs. target of %6" PRId64 "\n",
+                __func__, dev_names[id].c_str(), dmd.total/MiB, projected_used/MiB, projected_free/MiB, margins[id]/MiB);
         }
     }
-    assert(sum_total >= 0 && sum_projected_used >= 0 && sum_projected_ctx >= 0);
-    assert(sum_projected_used >= sum_projected_ctx);
+    assert(sum_free >= 0 && sum_projected_used >= 0);
     LLAMA_LOG_INFO("%s: projected to use %" PRId64 " MiB of device memory vs. %" PRId64 " MiB of free device memory\n",
-        __func__, sum_projected_used/MiB, sum_total/MiB);
-    if (min_projected_free >= margin) {
-        if (nd == 1) {
+        __func__, sum_projected_used/MiB, sum_free/MiB);
+    if (nd == 1) {
+        if (projected_free_per_device[0] >= margins[0]) {
             LLAMA_LOG_INFO("%s: will leave %" PRId64 " >= %" PRId64 " MiB of free device memory, no changes needed\n",
-                __func__, min_projected_free/MiB, margin/MiB);
+                __func__, projected_free_per_device[0]/MiB, margins[0]/MiB);
+            return;
+        }
+    } else {
+        bool changes_needed = false;
+        for (size_t id = 0; id < nd; id++) {
+            if (projected_free_per_device[id] < margins[id]) {
+                changes_needed = true;
+                break;
+            }
+        }
+        if (!changes_needed) {
+            LLAMA_LOG_INFO("%s: targets for free memory can be met on all devices, no changes needed\n", __func__);
             return;
         }
-        LLAMA_LOG_INFO("%s: will leave at least %" PRId64 " >= %" PRId64 " MiB of free memory on all devices, no changes needed\n",
-            __func__, min_projected_free/MiB, margin/MiB);
-        return;
     }
 
     // step 2: try reducing memory use by reducing the context size
 
     {
-        int64_t global_surplus = sum_projected_free - int64_t(nd)*margin;
+        int64_t global_surplus = sum_projected_free;
+        for (size_t id = 0; id < nd; id++) {
+            global_surplus -= margins[id];
+        }
         if (global_surplus < 0) {
-            LLAMA_LOG_INFO(nd == 1 ?
-                "%s: cannot fulfill margin of %" PRId64 " MiB, need to reduce device memory by %" PRId64 " MiB\n" :
-                "%s: cannot fulfill margin of %" PRId64 " MiB on all devices, need to use %" PRId64 " MiB less in total\n",
-                __func__, margin/MiB, -global_surplus/MiB);
+            if (nd == 1) {
+                LLAMA_LOG_INFO("%s: cannot meet free memory target of %" PRId64 " MiB, need to reduce device memory by %" PRId64 " MiB\n",
+                    __func__, margins[0]/MiB, -global_surplus/MiB);
+            } else {
+                LLAMA_LOG_INFO(
+                    "%s: cannot meet free memory targets on all devices, need to use %" PRId64 " MiB less in total\n",
+                    __func__, -global_surplus/MiB);
+            }
             if (cparams->n_ctx == 0) {
                 if (hp_nct > n_ctx_min) {
-                    const int64_t bytes_per_ctx = sum_projected_ctx / hp_nct;
-                    const uint32_t ctx_reduction = std::min(
-                        uint32_t((-global_surplus + bytes_per_ctx - 1) / bytes_per_ctx), hp_nct - n_ctx_min);
-                    cparams->n_ctx = hp_nct - ctx_reduction;
-                    const int64_t memory_reduction = ctx_reduction * bytes_per_ctx;
-                    global_surplus += memory_reduction;
-                    LLAMA_LOG_INFO("%s: context size reduced from %" PRIu32 " to %" PRIu32 " -> need %" PRId64 " MiB less memory in total\n",
-                        __func__, hp_nct, cparams->n_ctx, memory_reduction/MiB);
-                    if (global_surplus >= 0) {
+                    int64_t sum_used_target = sum_free;
+                    for (size_t id = 0; id < nd; id++) {
+                        sum_used_target -= margins[id];
+                    }
+                    if (nd > 1) {
+                        // for multiple devices we need to be more conservative in terms of how much context we think can fit:
+                        //   - for dense models only whole layers can be assigned to devices
+                        //   - for MoE models only whole tensors can be assigned to devices, which we estimate to be <= 1/3 of a layer
+                        //   - on average we expect a waste of 0.5 layers/tensors per device
+                        //   - use slightly more than the expected average for nd devices to be safe
+                        const int64_t model_per_layer = sum_projected_model / std::min(uint32_t(mparams->n_gpu_layers), hp_ngl);
+                        sum_used_target -= (nd + 1) * model_per_layer / (hp_nex == 0 ? 2 : 6);
+                    }
+
+                    int64_t sum_projected_used_min_ctx = 0;
+                    cparams->n_ctx = n_ctx_min;
+                    const dmds_t dmds_min_ctx = llama_get_device_memory_data(path_model, mparams, cparams, devs, hp_ngl, hp_nct, hp_nex, log_level);
+                    for (const auto & dmd : dmds_min_ctx) {
+                        sum_projected_used_min_ctx += dmd.mb.total();
+                    }
+                    if (sum_used_target > sum_projected_used_min_ctx) {
+                        // linear interpolation between minimum and maximum context size:
+                        cparams->n_ctx += (hp_nct - n_ctx_min) * (sum_used_target - sum_projected_used_min_ctx)
+                            / (sum_projected_used - sum_projected_used_min_ctx);
+                        cparams->n_ctx = std::max(cparams->n_ctx - cparams->n_ctx % 256, n_ctx_min); // round down context for CUDA backend
+
+                        const int64_t bytes_per_ctx = (sum_projected_used - sum_projected_used_min_ctx) / (hp_nct - n_ctx_min);
+                        const int64_t memory_reduction = (hp_nct - cparams->n_ctx) * bytes_per_ctx;
+                        LLAMA_LOG_INFO("%s: context size reduced from %" PRIu32 " to %" PRIu32 " -> need %" PRId64 " MiB less memory in total\n",
+                            __func__, hp_nct, cparams->n_ctx, memory_reduction/MiB);
                         if (nd == 1) {
                             LLAMA_LOG_INFO("%s: entire model can be fit by reducing context\n", __func__);
                             return;
                         }
                         LLAMA_LOG_INFO("%s: entire model should be fit across devices by reducing context\n", __func__);
+                    } else {
+                        const int64_t memory_reduction = sum_projected_used - sum_projected_used_min_ctx;
+                        LLAMA_LOG_INFO("%s: context size reduced from %" PRIu32 " to %" PRIu32 " -> need %" PRId64 " MiB less memory in total\n",
+                            __func__, hp_nct, cparams->n_ctx, memory_reduction/MiB);
                     }
                 } else {
-                    LLAMA_LOG_INFO("%s: default model context size is %" PRIu32 " which is <= the min. context size of %" PRIu32 " -> no change\n",
-                        __func__, hp_nct, n_ctx_min);
+                    if (n_ctx_min == UINT32_MAX) {
+                        LLAMA_LOG_INFO("%s: user has requested full context size of %" PRIu32 " -> no change\n", __func__, hp_nct);
+                    } else {
+                        LLAMA_LOG_INFO("%s: default model context size is %" PRIu32 " which is <= the min. context size of %" PRIu32 " -> no change\n",
+                            __func__, hp_nct, n_ctx_min);
+                    }
                 }
             } else {
                 LLAMA_LOG_INFO("%s: context size set by user to %" PRIu32 " -> no change\n", __func__, cparams->n_ctx);
@@ -259,32 +325,28 @@ static void llama_params_fit_impl(
     }
 
     if (mparams->n_gpu_layers != default_mparams.n_gpu_layers) {
-        throw std::runtime_error("n_gpu_layers already set by user to " + std::to_string(mparams->n_gpu_layers) + ", abort");
+        throw llama_params_fit_exception("n_gpu_layers already set by user to " + std::to_string(mparams->n_gpu_layers) + ", abort");
     }
     if (nd > 1) {
         if (!tensor_split) {
-            throw std::runtime_error("did not provide a buffer to write the tensor_split to, abort");
+            throw llama_params_fit_exception("did not provide a buffer to write the tensor_split to, abort");
         }
         if (mparams->tensor_split) {
             for (size_t id = 0; id < nd; id++) {
                 if (mparams->tensor_split[id] != 0.0f) {
-                    throw std::runtime_error("model_params::tensor_split already set by user, abort");
+                    throw llama_params_fit_exception("model_params::tensor_split already set by user, abort");
                 }
             }
         }
         if (mparams->split_mode == LLAMA_SPLIT_MODE_ROW) {
-            throw std::runtime_error("changing weight allocation for LLAMA_SPLIT_MODE_ROW not implemented, abort");
-        }
-        if (hp_ngl < 2*nd) {
-            throw std::runtime_error("model has only " + std::to_string(hp_ngl) + " layers but need at least "
-                + std::to_string(2*nd) + " to fit memory for " + std::to_string(nd) + " devices, abort");
+            throw llama_params_fit_exception("changing weight allocation for LLAMA_SPLIT_MODE_ROW not implemented, abort");
         }
     }
     if (!tensor_buft_overrides) {
-        throw std::runtime_error("did not provide buffer to set tensor_buft_overrides, abort");
+        throw llama_params_fit_exception("did not provide buffer to set tensor_buft_overrides, abort");
     }
     if (mparams->tensor_buft_overrides && (mparams->tensor_buft_overrides->pattern || mparams->tensor_buft_overrides->buft)) {
-        throw std::runtime_error("model_params::tensor_buft_overrides already set by user, abort");
+        throw llama_params_fit_exception("model_params::tensor_buft_overrides already set by user, abort");
     }
 
     // step 3: iteratively fill the back to front with "dense" layers
@@ -337,6 +399,11 @@ static void llama_params_fit_impl(
 
         // for the first partial layer varying parts can overflow, all further layers use LAYER_FRACTION_MOE:
         layer_fraction_t overflow_type = LAYER_FRACTION_MOE;
+
+        uint32_t n_full() const {
+            assert(n_layer >= n_part);
+            return n_layer - n_part;
+        }
     };
 
     const size_t ntbo = llama_max_tensor_buft_overrides();
@@ -345,8 +412,7 @@ static void llama_params_fit_impl(
     auto set_ngl_tensor_split_tbo = [&](
             const std::vector<ngl_t> & ngl_per_device,
             const std::vector<ggml_backend_buffer_type_t> & overflow_bufts,
-            llama_model_params & mparams,
-            const bool add_nonrepeating) {
+            llama_model_params & mparams) {
         mparams.n_gpu_layers = 0;
         for (size_t id = 0; id < nd; id++) {
             mparams.n_gpu_layers += ngl_per_device[id].n_layer;
@@ -354,29 +420,25 @@ static void llama_params_fit_impl(
                 tensor_split[id] = ngl_per_device[id].n_layer;
             }
         }
-        assert(uint32_t(mparams.n_gpu_layers) <= hp_ngl);
-        uint32_t il0 = hp_ngl - mparams.n_gpu_layers; // start index for tensor buft overrides
+        assert(uint32_t(mparams.n_gpu_layers) <= hp_ngl + 1);
+        uint32_t il0 = hp_ngl + 1 - mparams.n_gpu_layers; // start index for tensor buft overrides
 
-        if (add_nonrepeating) {
-            mparams.n_gpu_layers += 1;
-            tensor_split[nd - 1] += 1;
-        }
         mparams.tensor_split = tensor_split;
 
         size_t itbo = 0;
         for (size_t id = 0; id < nd; id++) {
-            il0 += ngl_per_device[id].n_layer - ngl_per_device[id].n_part;
+            il0 += ngl_per_device[id].n_full();
             for (uint32_t il = il0; il < il0 + ngl_per_device[id].n_part; il++) {
                 if (itbo + 1 >= ntbo) {
                     tensor_buft_overrides[itbo].pattern = nullptr;
                     tensor_buft_overrides[itbo].buft    = nullptr;
                     itbo++;
                     mparams.tensor_buft_overrides = tensor_buft_overrides;
-                    throw std::runtime_error("llama_params_fit_n_tensor_buft_overrides() == "
-                        + std::to_string(ntbo) + " is insufficient for model\n");
+                    throw llama_params_fit_exception("llama_max_tensor_buft_overrides() == "
+                        + std::to_string(ntbo) + " is insufficient for model");
                 }
                 tensor_buft_overrides[itbo].pattern = get_overflow_pattern(il, il == il0 ? ngl_per_device[id].overflow_type : LAYER_FRACTION_MOE);
-                tensor_buft_overrides[itbo].buft = overflow_bufts[id];
+                tensor_buft_overrides[itbo].buft = il == il0 ? overflow_bufts[id] : ggml_backend_cpu_buffer_type();
                 itbo++;
             }
             il0 += ngl_per_device[id].n_part;
@@ -391,10 +453,9 @@ static void llama_params_fit_impl(
     auto get_memory_for_layers = [&](
             const char * func_name,
             const std::vector<ngl_t> & ngl_per_device,
-            const std::vector<ggml_backend_buffer_type_t> & overflow_bufts,
-            const bool add_nonrepeating) -> std::vector<int64_t> {
+            const std::vector<ggml_backend_buffer_type_t> & overflow_bufts) -> std::vector<int64_t> {
         llama_model_params mparams_copy = *mparams;
-        set_ngl_tensor_split_tbo(ngl_per_device, overflow_bufts, mparams_copy, add_nonrepeating);
+        set_ngl_tensor_split_tbo(ngl_per_device, overflow_bufts, mparams_copy);
 
         const dmds_t dmd_nl = llama_get_device_memory_data(
             path_model, &mparams_copy, cparams, devs, hp_ngl, hp_nct, hp_nex, log_level);
@@ -427,9 +488,9 @@ static void llama_params_fit_impl(
         const dmds_t dmds_cpu_moe = llama_get_device_memory_data(
             path_model, mparams, cparams, devs, hp_ngl, hp_nct, hp_nex, log_level);
 
-        for (const llama_device_memory_data & dmd : dmds_cpu_moe) {
-            global_surplus_cpu_moe += dmd.free;
-            global_surplus_cpu_moe -= int64_t(dmd.mb.total()) + margin;
+        for (size_t id = 0; id < nd; id++) {
+            global_surplus_cpu_moe += dmds_cpu_moe[id].free;
+            global_surplus_cpu_moe -= int64_t(dmds_cpu_moe[id].mb.total()) + margins[id];
         }
 
         if (global_surplus_cpu_moe > 0) {
@@ -448,27 +509,18 @@ static void llama_params_fit_impl(
     std::vector<int64_t> targets; // maximum acceptable memory use per device
     targets.reserve(nd);
     for (size_t id = 0; id < nd; id++) {
-        targets.push_back(dmds_full[id].free - margin);
+        targets.push_back(dmds_full[id].free - margins[id]);
         LLAMA_LOG_DEBUG("%s: id=%zu, target=%" PRId64 " MiB\n", __func__, id, targets[id]/MiB);
     }
 
-    // whether for the optimal memory use we expect to load at least some MoE tensors:
-    const bool partial_moe = hp_nex > 0 && global_surplus_cpu_moe > 0;
-
-    std::vector<ggml_backend_buffer_type_t> overflow_bufts; // which bufts the partial layers of a device overflow to:
+    std::vector<ggml_backend_buffer_type_t> overflow_bufts; // which bufts the first partial layer of a device overflows to:
     overflow_bufts.reserve(nd);
-    for (size_t id = 0; id < nd - 1; ++id) {
-        overflow_bufts.push_back(ggml_backend_dev_buffer_type(devs[id + 1]));
+    for (size_t id = 0; id < nd; id++) {
+        overflow_bufts.push_back(ggml_backend_cpu_buffer_type());
     }
-    overflow_bufts.push_back(ggml_backend_cpu_buffer_type());
 
     std::vector<ngl_t> ngl_per_device(nd);
-    std::vector<int64_t> mem = get_memory_for_layers(__func__, ngl_per_device, overflow_bufts, partial_moe);
-    if (hp_nex > 0) {
-        for (size_t id = 0; id < nd; id++) {
-            ngl_per_device[id].overflow_type = LAYER_FRACTION_MOE;
-        }
-    }
+    std::vector<int64_t> mem = get_memory_for_layers(__func__, ngl_per_device, overflow_bufts);
 
     // optimize the number of layers per device using the method of false position:
     //   - ngl_per_device has 0 layers for each device, lower bound
@@ -476,22 +528,30 @@ static void llama_params_fit_impl(
     //   - interpolate the memory use / layer between low and high linearly to get a guess where it meets our target
     //   - check memory use of our guess, replace either the low or high bound
     //   - once we only have a difference of a single layer, stop and return the lower bound that just barely still fits
+    //   - the last device has the output layer, which cannot be a partial layer
     if (hp_nex == 0) {
         LLAMA_LOG_INFO("%s: filling dense layers back-to-front:\n", __func__);
     } else {
         LLAMA_LOG_INFO("%s: filling dense-only layers back-to-front:\n", __func__);
     }
-    uint32_t n_unassigned = hp_ngl;
     for (int id = nd - 1; id >= 0; id--) {
+        uint32_t n_unassigned = hp_ngl + 1;
+        for (size_t jd = id + 1; jd < nd; ++jd) {
+            assert(n_unassigned >= ngl_per_device[jd].n_layer);
+            n_unassigned -= ngl_per_device[jd].n_layer;
+        }
+
         std::vector<ngl_t> ngl_per_device_high = ngl_per_device;
         ngl_per_device_high[id].n_layer = n_unassigned;
         if (hp_nex > 0) {
-            ngl_per_device_high[id].n_part = ngl_per_device_high[id].n_layer;
+            ngl_per_device_high[id].n_part = size_t(id) < nd - 1 ? ngl_per_device_high[id].n_layer : ngl_per_device_high[id].n_layer - 1;
         }
         if (ngl_per_device_high[id].n_layer > 0) {
-            std::vector<int64_t> mem_high = get_memory_for_layers(__func__, ngl_per_device_high, overflow_bufts, partial_moe);
+            std::vector<int64_t> mem_high = get_memory_for_layers(__func__, ngl_per_device_high, overflow_bufts);
             if (mem_high[id] > targets[id]) {
+                assert(ngl_per_device_high[id].n_layer > ngl_per_device[id].n_layer);
                 uint32_t delta = ngl_per_device_high[id].n_layer - ngl_per_device[id].n_layer;
+                LLAMA_LOG_DEBUG("%s: start filling device %" PRIu32 ", delta=%" PRIu32 "\n", __func__, id, delta);
                 while (delta > 1) {
                     uint32_t step_size = int64_t(delta) * (targets[id] - mem[id]) / (mem_high[id] - mem[id]);
                     step_size = std::max(step_size, uint32_t(1));
@@ -500,25 +560,26 @@ static void llama_params_fit_impl(
                     std::vector<ngl_t> ngl_per_device_test = ngl_per_device;
                     ngl_per_device_test[id].n_layer += step_size;
                     if (hp_nex) {
-                        ngl_per_device_test[id].n_part += step_size;
+                        ngl_per_device_test[id].n_part += size_t(id) == nd - 1 && ngl_per_device_test[id].n_part == 0 ?
+                            step_size - 1 : step_size; // the first layer is the output layer which must always be full
                     }
-                    const std::vector<int64_t> mem_test = get_memory_for_layers(__func__, ngl_per_device_test, overflow_bufts, partial_moe);
+                    const std::vector<int64_t> mem_test = get_memory_for_layers(__func__, ngl_per_device_test, overflow_bufts);
 
                     if (mem_test[id] <= targets[id]) {
-                        ngl_per_device  = ngl_per_device_test;
-                        mem             = mem_test;
-                        n_unassigned   -= ngl_per_device[id].n_layer;
+                        ngl_per_device = ngl_per_device_test;
+                        mem            = mem_test;
                         LLAMA_LOG_DEBUG("%s: set ngl_per_device[%d].n_layer=%" PRIu32 "\n", __func__, id, ngl_per_device[id].n_layer);
                     } else {
                         ngl_per_device_high = ngl_per_device_test;
                         mem_high            = mem_test;
-                        LLAMA_LOG_DEBUG("%s: set ngl_per_device_high[%d].n_layer=%" PRIu32 "\n", __func__, id, ngl_per_device[id].n_layer);
+                        LLAMA_LOG_DEBUG("%s: set ngl_per_device_high[%d].n_layer=%" PRIu32 "\n", __func__, id, ngl_per_device_high[id].n_layer);
                     }
                     delta = ngl_per_device_high[id].n_layer - ngl_per_device[id].n_layer;
                 }
             } else {
-                ngl_per_device  = ngl_per_device_high;
-                n_unassigned   -= ngl_per_device[id].n_layer;
+                assert(ngl_per_device_high[id].n_layer == n_unassigned);
+                ngl_per_device = ngl_per_device_high;
+                mem            = mem_high;
                 LLAMA_LOG_DEBUG("%s: set ngl_per_device[%d].n_layer=%" PRIu32 "\n", __func__, id, ngl_per_device[id].n_layer);
             }
         }
@@ -529,7 +590,7 @@ static void llama_params_fit_impl(
             __func__, dev_names[id].c_str(), ngl_per_device[id].n_layer, mem[id]/MiB, projected_margin/MiB);
     }
     if (hp_nex == 0 || global_surplus_cpu_moe <= 0) {
-        set_ngl_tensor_split_tbo(ngl_per_device, overflow_bufts, *mparams, partial_moe);
+        set_ngl_tensor_split_tbo(ngl_per_device, overflow_bufts, *mparams);
         return;
     }
 
@@ -549,24 +610,20 @@ static void llama_params_fit_impl(
     assert(id_dense_start < nd);
 
     LLAMA_LOG_INFO("%s: converting dense-only layers to full layers and filling them front-to-back with overflow to next device/system memory:\n", __func__);
-    for (size_t id = 0; id <= id_dense_start; id++) {
+    for (size_t id = 0; id <= id_dense_start && id_dense_start < nd; id++) {
         std::vector<ngl_t> ngl_per_device_high = ngl_per_device;
         for (size_t jd = id_dense_start; jd < nd; jd++) {
-            const uint32_t n_layer_move = ngl_per_device_high[jd].n_layer;
+            const uint32_t n_layer_move = jd < nd - 1 ? ngl_per_device_high[jd].n_layer : ngl_per_device_high[jd].n_layer - 1;
             ngl_per_device_high[id].n_layer += n_layer_move;
             ngl_per_device_high[jd].n_layer -= n_layer_move;
             ngl_per_device_high[jd].n_part = 0;
         }
         size_t id_dense_start_high = nd - 1;
-        std::vector<int64_t> mem_high = get_memory_for_layers(__func__, ngl_per_device_high, overflow_bufts, partial_moe);
+        std::vector<int64_t> mem_high = get_memory_for_layers(__func__, ngl_per_device_high, overflow_bufts);
 
         if (mem_high[id] > targets[id]) {
-            assert(ngl_per_device_high[id].n_layer >= ngl_per_device_high[id].n_part);
-            assert(ngl_per_device[id].n_layer >= ngl_per_device[id].n_part);
-            assert((ngl_per_device_high[id].n_layer - ngl_per_device_high[id].n_part)
-                   >= ngl_per_device[id].n_layer - ngl_per_device[id].n_part);
-            uint32_t delta = (ngl_per_device_high[id].n_layer - ngl_per_device_high[id].n_part)
-                - (ngl_per_device[id].n_layer - ngl_per_device[id].n_part);
+            assert(ngl_per_device_high[id].n_full() >= ngl_per_device[id].n_full());
+            uint32_t delta = ngl_per_device_high[id].n_full() - ngl_per_device[id].n_full();
             while (delta > 1) {
                 uint32_t step_size = int64_t(delta) * (targets[id] - mem[id]) / (mem_high[id] - mem[id]);
                 step_size = std::max(step_size, uint32_t(1));
@@ -582,11 +639,11 @@ static void llama_params_fit_impl(
                     ngl_per_device_test[id].n_layer += n_convert_jd;
                     n_converted_test += n_convert_jd;
 
-                    if (ngl_per_device_test[id_dense_start_test].n_layer > 0) {
+                    if (ngl_per_device_test[id_dense_start_test].n_part > 0) {
                         break;
                     }
                 }
-                const std::vector<int64_t> mem_test = get_memory_for_layers(__func__, ngl_per_device_test, overflow_bufts, partial_moe);
+                const std::vector<int64_t> mem_test = get_memory_for_layers(__func__, ngl_per_device_test, overflow_bufts);
 
                 if (mem_test[id] <= targets[id]) {
                     ngl_per_device = ngl_per_device_test;
@@ -601,32 +658,38 @@ static void llama_params_fit_impl(
                     LLAMA_LOG_DEBUG("%s: set ngl_per_device_high[%zu].(n_layer, n_part)=(%" PRIu32 ", %" PRIu32 "), id_dense_start_high=%zu\n",
                         __func__, id, ngl_per_device_high[id].n_layer, ngl_per_device_high[id].n_part, id_dense_start_high);
                 }
-                delta = (ngl_per_device_high[id].n_layer - ngl_per_device_high[id].n_part)
-                    - (ngl_per_device[id].n_layer - ngl_per_device[id].n_part);
+                assert(ngl_per_device_high[id].n_full() >= ngl_per_device[id].n_full());
+                delta = ngl_per_device_high[id].n_full() - ngl_per_device[id].n_full();
             }
         } else {
             ngl_per_device = ngl_per_device_high;
+            mem            = mem_high;
             id_dense_start = id_dense_start_high;
             LLAMA_LOG_DEBUG("%s: set ngl_per_device[%zu].(n_layer, n_part)=(%" PRIu32 ", %" PRIu32 "), id_dense_start=%zu\n",
                 __func__, id, ngl_per_device[id].n_layer, ngl_per_device[id].n_part, id_dense_start);
         }
 
         // try to fit at least part of one more layer
-        if (ngl_per_device[id_dense_start].n_layer > 0) {
+        if (ngl_per_device[id_dense_start].n_layer > (id < nd - 1 ? 0 : 1)) {
             std::vector<ngl_t> ngl_per_device_test = ngl_per_device;
             size_t id_dense_start_test = id_dense_start;
             ngl_per_device_test[id_dense_start_test].n_layer--;
             ngl_per_device_test[id_dense_start_test].n_part--;
             ngl_per_device_test[id].n_layer++;
             ngl_per_device_test[id].n_part++;
-            if (ngl_per_device_test[id_dense_start_test].n_layer == 0) {
+            if (ngl_per_device_test[id_dense_start_test].n_part == 0) {
                 id_dense_start_test++;
             }
             ngl_per_device_test[id].overflow_type = LAYER_FRACTION_UP;
+            std::vector<ggml_backend_buffer_type_t> overflow_bufts_test = overflow_bufts;
+            if (id < nd - 1) {
+                overflow_bufts_test[id] = ggml_backend_dev_buffer_type(devs[id + 1]);
+            }
             LLAMA_LOG_DEBUG("%s: trying to fit one extra layer with overflow_type=LAYER_FRACTION_UP\n", __func__);
-            std::vector<int64_t> mem_test = get_memory_for_layers(__func__, ngl_per_device_test, overflow_bufts, partial_moe);
-            if (mem_test[id] < targets[id]) {
+            std::vector<int64_t> mem_test = get_memory_for_layers(__func__, ngl_per_device_test, overflow_bufts_test);
+            if (mem_test[id] < targets[id] && (id + 1 == nd || mem_test[id + 1] < targets[id + 1])) {
                 ngl_per_device = ngl_per_device_test;
+                overflow_bufts = overflow_bufts_test;
                 mem            = mem_test;
                 id_dense_start = id_dense_start_test;
                 LLAMA_LOG_DEBUG("%s: set ngl_per_device[%zu].(n_layer, n_part, overflow_type)=(%" PRIu32 ", %" PRIu32 ", UP), id_dense_start=%zu\n",
@@ -634,9 +697,10 @@ static void llama_params_fit_impl(
 
                 ngl_per_device_test[id].overflow_type = LAYER_FRACTION_GATE;
                 LLAMA_LOG_DEBUG("%s: trying to fit one extra layer with overflow_type=LAYER_FRACTION_GATE\n", __func__);
-                mem_test = get_memory_for_layers(__func__, ngl_per_device_test, overflow_bufts, partial_moe);
-                if (mem_test[id] < targets[id]) {
+                mem_test = get_memory_for_layers(__func__, ngl_per_device_test, overflow_bufts_test);
+                if (mem_test[id] < targets[id] && (id + 1 == nd || mem_test[id + 1] < targets[id + 1])) {
                     ngl_per_device = ngl_per_device_test;
+                    overflow_bufts = overflow_bufts_test;
                     mem            = mem_test;
                     id_dense_start = id_dense_start_test;
                     LLAMA_LOG_DEBUG("%s: set ngl_per_device[%zu].(n_layer, n_part, overflow_type)=(%" PRIu32 ", %" PRIu32 ", GATE), id_dense_start=%zu\n",
@@ -645,9 +709,10 @@ static void llama_params_fit_impl(
             } else {
                 ngl_per_device_test[id].overflow_type = LAYER_FRACTION_ATTN;
                 LLAMA_LOG_DEBUG("%s: trying to fit one extra layer with overflow_type=LAYER_FRACTION_ATTN\n", __func__);
-                mem_test = get_memory_for_layers(__func__, ngl_per_device_test, overflow_bufts, partial_moe);
-                if (mem_test[id] < targets[id]) {
+                mem_test = get_memory_for_layers(__func__, ngl_per_device_test, overflow_bufts_test);
+                if (mem_test[id] < targets[id] && (id + 1 == nd || mem_test[id + 1] < targets[id + 1])) {
                     ngl_per_device = ngl_per_device_test;
+                    overflow_bufts = overflow_bufts_test;
                     mem            = mem_test;
                     id_dense_start = id_dense_start_test;
                     LLAMA_LOG_DEBUG("%s: set ngl_per_device[%zu].(n_layer, n_part, overflow_type)=(%" PRIu32 ", %" PRIu32 ", ATTN), id_dense_start=%zu\n",
@@ -662,30 +727,41 @@ static void llama_params_fit_impl(
             __func__, dev_names[id].c_str(), ngl_per_device[id].n_layer, ngl_per_device[id].n_part, mem[id]/MiB, projected_margin/MiB);
     }
 
-    set_ngl_tensor_split_tbo(ngl_per_device, overflow_bufts, *mparams, partial_moe);
+    // print info for devices that were not changed during the conversion from dense only to full layers:
+    for (size_t id = id_dense_start + 1; id < nd; id++) {
+        const int64_t projected_margin = dmds_full[id].free - mem[id];
+        LLAMA_LOG_INFO(
+            "%s:   - %s: %2" PRIu32 " layers (%2" PRIu32 " overflowing), %6" PRId64 " MiB used, %6" PRId64 " MiB free\n",
+            __func__, dev_names[id].c_str(), ngl_per_device[id].n_layer, ngl_per_device[id].n_part, mem[id]/MiB, projected_margin/MiB);
+    }
+
+    set_ngl_tensor_split_tbo(ngl_per_device, overflow_bufts, *mparams);
 }
 
-bool llama_params_fit(
+enum llama_params_fit_status llama_params_fit(
         const char * path_model, struct llama_model_params * mparams, struct llama_context_params * cparams,
         float * tensor_split, struct llama_model_tensor_buft_override * tensor_buft_overrides,
-        size_t margin_s, uint32_t n_ctx_min, enum ggml_log_level log_level) {
+        size_t * margins, uint32_t n_ctx_min, enum ggml_log_level log_level) {
     const int64_t t0_us = llama_time_us();
-    bool ok = true;
+    llama_params_fit_status status = LLAMA_PARAMS_FIT_STATUS_SUCCESS;
     try {
-        llama_params_fit_impl(path_model, mparams, cparams, tensor_split, tensor_buft_overrides, margin_s, n_ctx_min, log_level);
+        llama_params_fit_impl(path_model, mparams, cparams, tensor_split, tensor_buft_overrides, margins, n_ctx_min, log_level);
         LLAMA_LOG_INFO("%s: successfully fit params to free device memory\n", __func__);
-    } catch (const std::runtime_error & e) {
+    } catch (const llama_params_fit_exception & e) {
         LLAMA_LOG_WARN("%s: failed to fit params to free device memory: %s\n", __func__, e.what());
-        ok = false;
+        status = LLAMA_PARAMS_FIT_STATUS_FAILURE;
+    } catch (const std::runtime_error & e) {
+        LLAMA_LOG_ERROR("%s: encountered an error while trying to fit params to free device memory: %s\n", __func__, e.what());
+        status = LLAMA_PARAMS_FIT_STATUS_ERROR;
     }
     const int64_t t1_us = llama_time_us();
     LLAMA_LOG_INFO("%s: fitting params to free memory took %.2f seconds\n", __func__, (t1_us - t0_us) * 1e-6);
-    return ok;
+    return status;
 }
 
 struct llama_sampler_chain_params llama_sampler_chain_default_params() {
     struct llama_sampler_chain_params result = {
-        /*.no_perf                     =*/ true,
+        /*.no_perf =*/ true,
     };
 
     return result;
@@ -758,7 +834,7 @@ static int llama_model_load(const std::string & fname, std::vector<std::string>
     model.t_start_us = tm.t_start_us;
 
     try {
-        llama_model_loader ml(fname, splits, params.use_mmap, params.check_tensors, params.no_alloc, params.kv_overrides, params.tensor_buft_overrides);
+        llama_model_loader ml(fname, splits, params.use_mmap, params.use_direct_io, params.check_tensors, params.no_alloc, params.kv_overrides, params.tensor_buft_overrides);
 
         ml.print_info();
 
@@ -1021,25 +1097,55 @@ int32_t llama_chat_apply_template(
 // model split
 //
 
-int llama_split_path(char * split_path, size_t maxlen, const char * path_prefix, int split_no, int split_count) {
+int32_t llama_split_path(
+    char * split_path,
+    size_t maxlen,
+    const char * path_prefix,
+    int32_t split_no,
+    int32_t split_count) {
+
     static const char * const SPLIT_PATH_FORMAT = "%s-%05d-of-%05d.gguf";
-    if (snprintf(split_path, maxlen, SPLIT_PATH_FORMAT, path_prefix, split_no + 1, split_count)) {
-        return strlen(split_path);
+
+    const int written = snprintf(
+        split_path,
+        maxlen,
+        SPLIT_PATH_FORMAT,
+        path_prefix,
+        split_no + 1,
+        split_count
+    );
+
+    if (written < 0 || (size_t) written >= maxlen) {
+        return 0;
     }
-    return 0;
+
+    return (int32_t) written;
 }
 
-int llama_split_prefix(char * split_prefix, size_t maxlen, const char * split_path, int split_no, int split_count) {
-    std::string str_split_path(split_path);
-    char postfix[32];
-    snprintf(postfix, 32, "-%05d-of-%05d.gguf", split_no + 1, split_count);
-    std::string str_postfix(postfix);
+int32_t llama_split_prefix(
+    char * split_prefix,
+    size_t maxlen,
+    const char * split_path,
+    int32_t split_no,
+    int32_t split_count) {
 
-    // check if split_prefix ends with postfix
-    int size_prefix = str_split_path.size() - str_postfix.size();
-    if (size_prefix > 0 && str_split_path.find(str_postfix, size_prefix) != std::string::npos) {
-        snprintf(split_prefix, std::min((size_t) size_prefix + 1, maxlen), "%s", split_path);
-        return size_prefix;
+    const std::string str_split_path(split_path);
+
+    char postfix[32];
+    snprintf(postfix, sizeof(postfix), "-%05d-of-%05d.gguf", split_no + 1, split_count);
+
+    const std::string str_postfix(postfix);
+    if (str_split_path.size() <= str_postfix.size()) {
+        return 0;
+    }
+
+    const size_t size_prefix = str_split_path.size() - str_postfix.size();
+
+    if (str_split_path.compare(size_prefix, std::string::npos, str_postfix) == 0) {
+        const size_t copy_len = std::min(size_prefix + 1, maxlen);
+        snprintf(split_prefix, copy_len, "%s", split_path);
+
+        return (int32_t) size_prefix;
     }
 
     return 0;

llama/llama.cpp/src/models/afmoe.cpp

@@ -22,8 +22,15 @@ llm_build_afmoe::llm_build_afmoe(const llama_model & model, const llm_graph_para
     const float kq_scale = 1.0f/sqrtf(float(n_embd_head));
 
     for (int il = 0; il < n_layer; ++il) {
+        const float freq_base_l  = model.get_rope_freq_base (cparams, il);
+        const float freq_scale_l = model.get_rope_freq_scale(cparams, il);
+
         ggml_tensor * inpSA = inpL;
 
+        // This overlaps with SWA layers in current models, so get_rope_freq_base/scale may be superfluous
+        const bool use_rope = hparams.n_no_rope_layer_step > 0 &&
+                              (il + 1) % hparams.n_no_rope_layer_step != 0;
+
         // dual attention normalization (pre)
         cur = build_norm(inpL,
                 model.layers[il].attn_norm, NULL,
@@ -56,19 +63,16 @@ llm_build_afmoe::llm_build_afmoe(const llama_model & model, const llm_graph_para
             cb(Qcur, "Qcur_normed", il);
             cb(Kcur, "Kcur_normed", il);
 
-            // RoPE only for sliding_attention layers
-            const bool use_rope = hparams.n_no_rope_layer_step > 0 &&
-                                ((il + 1) % hparams.n_no_rope_layer_step) != 0;
             if (use_rope) {
                 Qcur = ggml_rope_ext(
                         ctx0, Qcur, inp_pos, nullptr,
-                        n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                        n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
                         ext_factor, attn_factor, beta_fast, beta_slow);
                 cb(Qcur, "Qcur_rope", il);
 
                 Kcur = ggml_rope_ext(
                         ctx0, Kcur, inp_pos, nullptr,
-                        n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                        n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
                         ext_factor, attn_factor, beta_fast, beta_slow);
                 cb(Kcur, "Kcur_rope", il);
             }

llama/llama.cpp/src/models/bert.cpp

@@ -142,11 +142,13 @@ llm_build_bert::llm_build_bert(const llama_model & model, const llm_graph_params
                     LLM_FFN_GELU, LLM_FFN_SEQ, il);
             cb(cur, "ffn_out", il);
         } else if (model.arch == LLM_ARCH_JINA_BERT_V2) {
+            const bool up_contains_gate = !model.layers[il].ffn_gate && model.layers[il].ffn_up->ne[1] != hparams.n_ff();
+            auto type_op = up_contains_gate ? LLM_FFN_GEGLU : LLM_FFN_GELU;
             cur = build_ffn(cur,
-                    model.layers[il].ffn_up, NULL, NULL,
+                    model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL,
                     model.layers[il].ffn_gate, NULL, NULL,
                     model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL, NULL,
-                    model.layers[il].ffn_gate ? LLM_FFN_GELU : LLM_FFN_GEGLU, LLM_FFN_PAR, il);
+                    type_op, LLM_FFN_PAR, il);
             cb(cur, "ffn_out", il);
         } else {
             cur = build_ffn(cur,

llama/llama.cpp/src/models/cogvlm.cpp

@@ -3,12 +3,14 @@
 llm_build_cogvlm::llm_build_cogvlm(const llama_model & model, const llm_graph_params & params) :
     llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v;
-    float         kq_scale    = 1.0f / sqrtf(float(n_embd_head));
+    const float   kq_scale    = 1.0f / sqrtf(float(n_embd_head));
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
     GGML_ASSERT(n_embd_head == hparams.n_rot);
 
-    ggml_tensor *inpL, *cur;
+    ggml_tensor * inpL;
+    ggml_tensor * cur;
+
     inpL = build_inp_embd(model.tok_embd);
 
     ggml_tensor * inp_pos = build_inp_pos();
@@ -44,7 +46,7 @@ llm_build_cogvlm::llm_build_cogvlm(const llama_model & model, const llm_graph_pa
         }
 
         ggml_tensor * inpSA = inpL;
-        cur                 = build_norm(inpSA, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
+        cur = build_norm(inpSA, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
 
         // build self attention
         {

llama/llama.cpp/src/models/cohere2-iswa.cpp

@@ -21,6 +21,9 @@ llm_build_cohere2_iswa::llm_build_cohere2_iswa(const llama_model & model, const
 
     for (int il = 0; il < n_layer; ++il) {
         const bool is_swa = hparams.is_swa(il);
+        // UNUSED:
+        // const float freq_base_l  = model.get_rope_freq_base (cparams, il);
+        // const float freq_scale_l = model.get_rope_freq_scale(cparams, il);
 
         // norm
         cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM, il);

llama/llama.cpp/src/models/deepseek2.cpp

@@ -2,14 +2,11 @@
 
 llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_graph_params & params) :
     llm_graph_context(params) {
-    // lite variants include DeepSeek-V2-Lite, GigaChat3-10B-A1.8B
-    bool is_lite = (hparams.n_layer == 27 || hparams.n_layer == 26);
-
-    const bool is_mla = (hparams.n_embd_head_k_mla != 0 && hparams.n_embd_head_v_mla != 0);
+    const bool is_mla = hparams.is_mla();
 
     // note: these are the actual head sizes you get when treating as MHA or after "decompression" using wv_b for MLA
-    const int64_t n_embd_head_k = is_mla ? hparams.n_embd_head_k_mla : hparams.n_embd_head_k;
-    const int64_t n_embd_head_v = is_mla ? hparams.n_embd_head_v_mla : hparams.n_embd_head_v;
+    const int64_t n_embd_head_k = hparams.n_embd_head_k_mla();
+    const int64_t n_embd_head_v = hparams.n_embd_head_v_mla();
 
     const int64_t n_embd_head_qk_rope = hparams.n_rot;
     const int64_t n_embd_head_qk_nope = n_embd_head_k - n_embd_head_qk_rope;
@@ -43,7 +40,8 @@ llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_gr
     // inp_pos - contains the positions
     ggml_tensor * inp_pos = build_inp_pos();
 
-    auto * inp_attn = build_attn_inp_kv();
+    auto * inp_attn_kv = !is_mla ? build_attn_inp_kv() : nullptr;
+    auto * inp_attn_k  =  is_mla ? build_attn_inp_k()  : nullptr;
 
     ggml_tensor * inp_out_ids = build_inp_out_ids();
 
@@ -57,6 +55,9 @@ llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_gr
         // self_attention
         {
             ggml_tensor * q = NULL;
+
+            const bool is_lite = model.layers[il].wq;
+
             if (!is_lite) {
                 q = ggml_mul_mat(ctx0, model.layers[il].wq_a, cur);
                 cb(q, "q", il);
@@ -124,14 +125,14 @@ llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_gr
 
                 // {n_embd_head_qk_rope + kv_lora_rank, n_head, n_tokens}
                 // note: rope must go first for in-place context shifting in build_rope_shift()
-                ggml_tensor * Qcur = ggml_concat(ctx0, q_pe, q_nope_absorbed, 0);
+                ggml_tensor * Qcur = ggml_concat(ctx0, q_nope_absorbed, q_pe, 0);
                 cb(Qcur, "Qcur", il);
 
                 kv_cmpr = ggml_reshape_3d(ctx0, kv_cmpr, kv_lora_rank, 1, n_tokens);
                 cb(kv_cmpr, "kv_cmpr_reshape", il);
 
                 // {n_embd_head_qk_rope + kv_lora_rank, 1, n_tokens}
-                ggml_tensor * Kcur = ggml_concat(ctx0, k_pe, kv_cmpr, 0);
+                ggml_tensor * Kcur = ggml_concat(ctx0, kv_cmpr, k_pe, 0);
                 cb(Kcur, "Kcur", il);
 
                 // {kv_lora_rank, 1, n_tokens}
@@ -145,7 +146,7 @@ llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_gr
                 }
 
                 // note: MLA with the absorption optimzation converts into MQA (ie: GQA with 1 group)
-                cur = build_attn(inp_attn,
+                cur = build_attn(inp_attn_k,
                         model.layers[il].wo, NULL,
                         Qcur, Kcur, Vcur, nullptr, nullptr, model.layers[il].wv_b, kq_scale, il);
             } else {
@@ -169,11 +170,10 @@ llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_gr
                 Vcur = ggml_cont(ctx0, Vcur);
                 cb(Vcur, "Vcur_cont", il);
 
-                // note: rope must go first for in-place context shifting in build_rope_shift()
-                ggml_tensor * Qcur = ggml_concat(ctx0, q_pe, q_nope, 0);
+                ggml_tensor * Qcur = ggml_concat(ctx0, q_nope, q_pe, 0);
                 cb(Qcur, "Qcur", il);
 
-                ggml_tensor * Kcur = ggml_concat(ctx0, ggml_repeat(ctx0, k_pe, q_pe), k_nope, 0);
+                ggml_tensor * Kcur = ggml_concat(ctx0, k_nope, ggml_repeat(ctx0, k_pe, q_pe), 0);
                 cb(Kcur, "Kcur", il);
 
                 if (inp_attn_scale) {
@@ -183,7 +183,7 @@ llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_gr
                 }
 
                 // note: MLA without the absorption optimization converts into MHA (ie: GQA with full n_head groups)
-                cur = build_attn(inp_attn,
+                cur = build_attn(inp_attn_kv,
                             model.layers[il].wo, NULL,
                             Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
             }
@@ -215,7 +215,7 @@ llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_gr
                 model.layers[il].ffn_exp_probs_b,
                 n_expert, n_expert_used,
                 LLM_FFN_SILU, hparams.expert_weights_norm,
-                true, hparams.expert_weights_scale,
+                hparams.expert_weights_scale, hparams.expert_weights_scale,
                 (llama_expert_gating_func_type) hparams.expert_gating_func,
                 il);
             cb(moe_out, "ffn_moe_out", il);

llama/llama.cpp/src/models/exaone-moe.cpp

@@ -0,0 +1,146 @@
+#include "models.h"
+
+
+llm_build_exaone_moe::llm_build_exaone_moe(const llama_model & model, const llm_graph_params & params) :
+    llm_graph_context(params) {
+    const int64_t n_embd_head = hparams.n_embd_head_k;
+
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_v);
+    GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    // inp_pos - contains the positions
+    ggml_tensor * inp_pos = build_inp_pos();
+
+    auto * inp_attn_iswa = build_attn_inp_kv_iswa();
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    const int n_transformer_layers = n_layer - hparams.nextn_predict_layers;
+    for (int il = 0; il < n_transformer_layers; ++il) {
+        ggml_tensor * inpSA = inpL;
+
+        // use RoPE for SWA layers
+        const bool is_local_layer = hparams.is_swa(il);
+
+        // norm
+        cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
+        cb(cur, "attn_norm", il);
+
+        // self-attention
+        {
+            ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
+
+            // compute Q and K and RoPE them
+            ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+            cb(Qcur, "Qcur", il);
+
+            ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+            cb(Kcur, "Kcur", il);
+
+            ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+            cb(Vcur, "Vcur", il);
+
+            Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
+            Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+            Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+            Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
+            Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
+            cb(Qcur, "Qcur_normed", il);
+            cb(Kcur, "Kcur_normed", il);
+
+            if (is_local_layer) {
+                Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, rope_factors, n_rot, rope_type, n_ctx_orig, freq_base,
+                                     freq_scale, ext_factor, attn_factor, beta_fast, beta_slow);
+
+                Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, rope_factors, n_rot, rope_type, n_ctx_orig, freq_base,
+                                     freq_scale, ext_factor, attn_factor, beta_fast, beta_slow);
+            }
+            cb(Qcur, "Qcur", il);
+            cb(Kcur, "Kcur", il);
+            cb(Vcur, "Vcur", il);
+
+            cur = build_attn(inp_attn_iswa,
+                model.layers[il].wo, NULL,
+                Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f / sqrtf(float(n_embd_head)), il);
+            cb(cur, "attn_out", il);
+        }
+        if (il == n_transformer_layers - 1 && inp_out_ids) {
+            cur   = ggml_get_rows(ctx0, cur, inp_out_ids);
+            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+        }
+        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+        cb(ffn_inp, "ffn_inp", il);
+
+        // norm
+        cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
+        cb(cur, "ffn_norm", il);
+
+        // feed-forward network
+        if (model.layers[il].ffn_gate_inp == nullptr) {
+            // dense branch
+            cur = build_ffn(cur,
+                    model.layers[il].ffn_up, NULL, NULL,
+                    model.layers[il].ffn_gate, NULL, NULL,
+                    model.layers[il].ffn_down, NULL, NULL, NULL,
+                    LLM_FFN_SILU, LLM_FFN_PAR, il);
+            cb(cur, "ffn_out", il);
+        } else {
+            // MoE branch
+            ggml_tensor * moe_out = build_moe_ffn(cur,
+                model.layers[il].ffn_gate_inp,
+                model.layers[il].ffn_up_exps,
+                model.layers[il].ffn_gate_exps,
+                model.layers[il].ffn_down_exps,
+                model.layers[il].ffn_exp_probs_b,
+                n_expert, n_expert_used,
+                LLM_FFN_SILU, hparams.expert_weights_norm,
+                true, hparams.expert_weights_scale,
+                (llama_expert_gating_func_type) hparams.expert_gating_func,
+                il);
+            cb(moe_out, "ffn_moe_out", il);
+
+            // FFN shared expert
+            {
+                ggml_tensor * ffn_shexp =
+                    build_ffn(cur,
+                        model.layers[il].ffn_up_shexp, NULL, NULL,
+                        model.layers[il].ffn_gate_shexp, NULL, NULL,
+                        model.layers[il].ffn_down_shexp, NULL, NULL,
+                        NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
+                cb(ffn_shexp, "ffn_shexp", il);
+
+                cur = ggml_add(ctx0, moe_out, ffn_shexp);
+                cb(cur, "ffn_out", il);
+            }
+        }
+
+        cur = ggml_add(ctx0, cur, ffn_inp);
+
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        // input for next layer
+        inpL = cur;
+    }
+    cur = inpL;
+
+    // final norm
+    cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    // lm_head
+    cur = build_lora_mm(model.output, cur);
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}

llama/llama.cpp/src/models/gemma-embedding.cpp

@@ -1,7 +1,5 @@
 #include "models.h"
 
-
-
 llm_build_gemma_embedding::llm_build_gemma_embedding(const llama_model & model, const llm_graph_params & params) :
     llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_k;
@@ -12,10 +10,8 @@ llm_build_gemma_embedding::llm_build_gemma_embedding(const llama_model & model,
     inpL = build_inp_embd(model.tok_embd);
 
     // important: do not normalize weights for raw embeddings input (i.e. encoded image emdeddings)
-    if (ubatch.token) {
-        inpL = ggml_scale(ctx0, inpL, sqrtf(n_embd));
-        cb(inpL, "inp_scaled", -1);
-    }
+    inpL = ggml_scale(ctx0, inpL, ubatch.token ? sqrtf(n_embd) : 1.0f);
+    cb(inpL, "inp_scaled", -1);
 
     // inp_pos - contains the positions
     ggml_tensor * inp_pos = build_inp_pos();

llama/llama.cpp/src/models/gemma2-iswa.cpp

@@ -19,6 +19,9 @@ llm_build_gemma2_iswa::llm_build_gemma2_iswa(const llama_model & model, const ll
     ggml_tensor * inp_out_ids = build_inp_out_ids();
 
     for (int il = 0; il < n_layer; ++il) {
+        const float freq_base_l  = model.get_rope_freq_base (cparams, il);
+        const float freq_scale_l = model.get_rope_freq_scale(cparams, il);
+
         // norm
         cur = build_norm(inpL,
                 model.layers[il].attn_norm, NULL,
@@ -43,12 +46,12 @@ llm_build_gemma2_iswa::llm_build_gemma2_iswa(const llama_model & model, const ll
 
             Qcur = ggml_rope_ext(
                     ctx0, Qcur, inp_pos, nullptr,
-                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                    n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
                     ext_factor, attn_factor, beta_fast, beta_slow);
 
             Kcur = ggml_rope_ext(
                     ctx0, Kcur, inp_pos, nullptr,
-                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                    n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
                     ext_factor, attn_factor, beta_fast, beta_slow);
 
             cb(Qcur, "Qcur", il);

llama/llama.cpp/src/models/gemma3.cpp

@@ -10,10 +10,9 @@ llm_build_gemma3<iswa>::llm_build_gemma3(const llama_model & model, const llm_gr
     inpL = build_inp_embd(model.tok_embd);
 
     // important: do not normalize weights for raw embeddings input (i.e. encoded image emdeddings)
-    if (ubatch.token) {
-        inpL = ggml_scale(ctx0, inpL, sqrtf(n_embd));
-        cb(inpL, "inp_scaled", -1);
-    }
+    inpL = ggml_scale(ctx0, inpL, ubatch.token ? sqrtf(n_embd) : 1.0f);
+    cb(inpL, "inp_scaled", -1);
+
     // inp_pos - contains the positions
     ggml_tensor * inp_pos = build_inp_pos();
 

llama/llama.cpp/src/models/gemma3n-iswa.cpp

@@ -1,7 +1,5 @@
 #include "models.h"
 
-
-
 llm_build_gemma3n_iswa::llm_build_gemma3n_iswa(const llama_model & model, const llm_graph_params & params) :
     llm_graph_context(params),
     model(model),
@@ -15,10 +13,9 @@ llm_build_gemma3n_iswa::llm_build_gemma3n_iswa(const llama_model & model, const
     inpL = build_inp_embd(model.tok_embd);
 
     // important: do not normalize weights for raw embeddings input (i.e. encoded image emdeddings)
-    if (ubatch.token) {
-        inpL = ggml_scale(ctx0, inpL, sqrtf(n_embd));
-        cb(inpL, "inp_scaled", -1);
-    }
+    inpL = ggml_scale(ctx0, inpL, ubatch.token ? sqrtf(n_embd) : 1.0f);
+    cb(inpL, "inp_scaled", -1);
+
     // inp_pos - contains the positions
     ggml_tensor * inp_pos = build_inp_pos();
 
@@ -248,20 +245,30 @@ ggml_tensor * llm_build_gemma3n_iswa::view_2d_slice(ggml_tensor * x, int idx) {
 // equivalent to get_per_layer_inputs() in python code
 // output shape: [n_embd_altup, n_layer, n_tokens]
 ggml_tensor * llm_build_gemma3n_iswa::get_per_layer_inputs() {
-    auto          inp = std::make_unique<llm_graph_input_embd>();
+    auto inp = std::make_unique<llm_graph_input_embd>(n_embd);
     ggml_tensor * inp_per_layer;
     if (ubatch.token) {
         inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ubatch.n_tokens);
         ggml_set_input(inp->tokens);
-        res->t_tokens = inp->tokens;
+        res->t_inp_tokens = inp->tokens;
         inp_per_layer = ggml_get_rows(ctx0, model.tok_embd_per_layer, inp->tokens);
         inp_per_layer = ggml_reshape_3d(ctx0, inp_per_layer, n_embd_altup, n_layer, n_tokens);
         inp_per_layer = ggml_scale(ctx0, inp_per_layer, sqrtf((float) n_embd_altup));
         cb(inp_per_layer, "inp_per_layer_selected", -1);
+        res->add_input(std::move(inp));
     } else {
-        GGML_ABORT("TODO: support embd input");
+        // Vision embedding path: use padding token (ID=0) embedding
+        // TODO: verify if this is the correct behavior in transformers implementation
+        const int64_t embd_size = model.tok_embd_per_layer->ne[0];  // n_embd_altup * n_layer
+
+        // Extract and dequantize padding token embedding (row 0)
+        ggml_tensor * padding = ggml_view_1d(ctx0, model.tok_embd_per_layer, embd_size, 0);
+        inp_per_layer = ggml_cast(ctx0, padding, GGML_TYPE_F32);
+
+        // Reshape to [n_embd_altup, n_layer, 1]
+        inp_per_layer = ggml_reshape_3d(ctx0, inp_per_layer, n_embd_altup, n_layer, 1);
+        cb(inp_per_layer, "inp_per_layer_vision", -1);
     }
-    res->add_input(std::move(inp));
     return inp_per_layer;
 }
 
@@ -279,7 +286,7 @@ ggml_tensor * llm_build_gemma3n_iswa::project_per_layer_inputs(ggml_tensor * inp
                                               -1);  // [n_embd_altup, n_layer, n_tokens]
     cb(per_layer_proj, "per_layer_proj", -1);
 
-    inp_per_layer = ggml_add(ctx0, inp_per_layer, per_layer_proj);
+    inp_per_layer = ggml_add(ctx0, per_layer_proj, inp_per_layer);
     inp_per_layer = ggml_scale(ctx0, inp_per_layer, per_layer_input_scale);
     cb(inp_per_layer, "inp_per_layer", -1);
 

llama/llama.cpp/src/models/llama-iswa.cpp

@@ -25,8 +25,12 @@ llm_build_llama_iswa::llm_build_llama_iswa(const llama_model & model, const llm_
     ggml_tensor * inp_out_ids = build_inp_out_ids();
 
     for (int il = 0; il < n_layer; ++il) {
+        const float freq_base_l  = model.get_rope_freq_base (cparams, il);
+        const float freq_scale_l = model.get_rope_freq_scale(cparams, il);
+
         ggml_tensor * inpSA = inpL;
 
+        // This overlaps with SWA layers in current models, so get_rope_freq_base/scale may be superfluous
         const bool use_rope = hparams.n_no_rope_layer_step > 0 &&
                               (il + 1) % hparams.n_no_rope_layer_step != 0;
 
@@ -67,13 +71,13 @@ llm_build_llama_iswa::llm_build_llama_iswa(const llama_model & model, const llm_
             if (use_rope) {
                 Qcur = ggml_rope_ext(
                         ctx0, Qcur, inp_pos, rope_factors,
-                        n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                        n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
                         ext_factor, attn_factor, beta_fast, beta_slow
                         );
 
                 Kcur = ggml_rope_ext(
                         ctx0, Kcur, inp_pos, rope_factors,
-                        n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                        n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
                         ext_factor, attn_factor, beta_fast, beta_slow
                         );
             } else if (inp_attn_scale) {

llama/llama.cpp/src/models/llama.cpp

@@ -1,6 +1,7 @@
 #include "models.h"
 
-llm_build_llama::llm_build_llama(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+template <bool embed>
+llm_build_llama<embed>::llm_build_llama(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_v;
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
@@ -14,7 +15,14 @@ llm_build_llama::llm_build_llama(const llama_model & model, const llm_graph_para
     // inp_pos - contains the positions
     ggml_tensor * inp_pos = build_inp_pos();
 
-    auto * inp_attn = build_attn_inp_kv();
+    using inp_attn_type = std::conditional_t<embed, llm_graph_input_attn_no_cache, llm_graph_input_attn_kv>;
+
+    inp_attn_type * inp_attn = nullptr;
+    if constexpr (embed) {
+        inp_attn = build_attn_inp_no_cache();
+    } else {
+        inp_attn = build_attn_inp_kv();
+    }
 
     const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
 
@@ -145,11 +153,16 @@ llm_build_llama::llm_build_llama(const llama_model & model, const llm_graph_para
     cb(cur, "result_norm", -1);
     res->t_embd = cur;
 
-    // lm_head
-    cur = build_lora_mm(model.output, cur);
+    if constexpr (!embed) {
+        // lm_head
+        cur = build_lora_mm(model.output, cur);
 
-    cb(cur, "result_output", -1);
-    res->t_logits = cur;
+        cb(cur, "result_output", -1);
+        res->t_logits = cur;
+    }
 
     ggml_build_forward_expand(gf, cur);
 }
+
+template struct llm_build_llama<false>;
+template struct llm_build_llama<true>;

llama/llama.cpp/src/models/maincoder.cpp

@@ -0,0 +1,117 @@
+#include "models.h"
+
+llm_build_maincoder::llm_build_maincoder(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+    const int64_t n_embd_head = hparams.n_embd_head_v;
+
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+    GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    // inp_pos - contains the positions
+    ggml_tensor * inp_pos = build_inp_pos();
+
+    auto * inp_attn = build_attn_inp_kv();
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    for (int il = 0; il < n_layer; ++il) {
+        ggml_tensor * inpSA = inpL;
+
+        // norm
+        cur = build_norm(inpL,
+                model.layers[il].attn_norm, NULL,
+                LLM_NORM_RMS, il);
+        cb(cur, "attn_norm", il);
+
+        // self-attention
+        {
+            // compute Q and K and RoPE them
+            ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+            cb(Qcur, "Qcur", il);
+
+            ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+            cb(Kcur, "Kcur", il);
+
+            ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+            cb(Vcur, "Vcur", il);
+
+            Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
+            Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+            Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+            Qcur = ggml_rope_ext(
+                    ctx0, Qcur, inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                    );
+
+            Kcur = ggml_rope_ext(
+                    ctx0, Kcur, inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                    );
+
+            Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
+            cb(Qcur, "Qcur_normed", il);
+
+            Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
+            cb(Kcur, "Kcur_normed", il);
+
+            cb(Qcur, "Qcur", il);
+            cb(Kcur, "Kcur", il);
+            cb(Vcur, "Vcur", il);
+
+            cur = build_attn(inp_attn,
+                    model.layers[il].wo, model.layers[il].bo,
+                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+        }
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+        }
+        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+        cb(ffn_inp, "ffn_inp", il);
+
+        // feed-forward network
+        cur = build_norm(ffn_inp,
+                model.layers[il].ffn_norm, NULL,
+                LLM_NORM_RMS, il);
+        cb(cur, "ffn_norm", il);
+
+        cur = build_ffn(cur,
+                model.layers[il].ffn_up,   NULL, NULL,
+                model.layers[il].ffn_gate, NULL, NULL,
+                model.layers[il].ffn_down, NULL, NULL,
+                NULL,
+                LLM_FFN_SILU, LLM_FFN_PAR, il);
+        cb(cur, "ffn_out", il);
+
+        cur = ggml_add(ctx0, cur, ffn_inp);
+
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        // input for next layer
+        inpL = cur;
+    }
+    cur = inpL;
+
+    cur = build_norm(cur,
+            model.output_norm, NULL,
+            LLM_NORM_RMS, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    // lm_head
+    cur = build_lora_mm(model.output, cur);
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}

llama/llama.cpp/src/models/mimo2-iswa.cpp

@@ -0,0 +1,123 @@
+
+#include "models.h"
+
+llm_build_mimo2_iswa::llm_build_mimo2_iswa(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    ggml_tensor * inp_pos = build_inp_pos();
+    auto * inp_attn = build_attn_inp_kv_iswa();
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    for (int il = 0; il < n_layer; ++il) {
+        ggml_tensor * inpSA = inpL;
+
+        uint32_t n_head_l    = hparams.n_head(il);
+        uint32_t n_head_kv_l = hparams.n_head_kv(il);
+        const float freq_base_l  = model.get_rope_freq_base(cparams, il);
+        const float freq_scale_l = model.get_rope_freq_scale(cparams, il);
+
+        cur = inpL;
+
+        // self_attention
+        {
+            cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
+            cb(cur, "attn_norm", il);
+
+            // compute Q and K and RoPE them
+            ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+            cb(Qcur, "Qcur", il);
+
+            ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+            cb(Kcur, "Kcur", il);
+
+            ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+            cb(Vcur, "Vcur", il);
+
+            Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head_k, n_head_l,    n_tokens);
+            Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head_k, n_head_kv_l, n_tokens);
+            Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head_v, n_head_kv_l, n_tokens);
+
+            Qcur = ggml_rope_ext(
+                ctx0, Qcur, inp_pos, nullptr,
+                n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
+                ext_factor, attn_factor, beta_fast, beta_slow
+                );
+
+            Kcur = ggml_rope_ext(
+                ctx0, Kcur, inp_pos, nullptr,
+                n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
+                ext_factor, attn_factor, beta_fast, beta_slow
+                );
+
+            cb(Qcur, "Qcur", il);
+            cb(Kcur, "Kcur", il);
+            cb(Vcur, "Vcur", il);
+
+            ggml_tensor * sinks = model.layers[il].attn_sinks;
+
+            cur = build_attn(inp_attn,
+                    model.layers[il].wo, NULL,
+                    Qcur, Kcur, Vcur, nullptr, sinks, nullptr, 1.0f/sqrtf(float(n_embd_head_k)), il);
+        }
+
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+        }
+
+        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+        cb(ffn_inp, "ffn_inp", il);
+
+        cur = build_norm(ffn_inp,
+                model.layers[il].ffn_norm, NULL,
+                LLM_NORM_RMS, il);
+        cb(cur, "ffn_norm", il);
+
+        // feed-forward network
+        if (model.layers[il].ffn_gate_inp == nullptr) {
+            // dense branch
+            cur = build_ffn(cur,
+                    model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
+                    model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,
+                    model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
+                    NULL,
+                    LLM_FFN_SILU, LLM_FFN_PAR, il);
+            cb(cur, "ffn_out", il);
+        } else {
+            // MoE branch
+            cur = build_moe_ffn(cur, model.layers[il].ffn_gate_inp, model.layers[il].ffn_up_exps,
+                                model.layers[il].ffn_gate_exps, model.layers[il].ffn_down_exps,
+                                model.layers[il].ffn_exp_probs_b, n_expert, n_expert_used, LLM_FFN_SILU, true, false,
+                                0.0, LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID, il);
+            cb(cur, "ffn_moe_out", il);
+        }
+
+        cur = ggml_add(ctx0, cur, ffn_inp);
+
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        // input for next layer
+        inpL = cur;
+    }
+
+    cur = inpL;
+
+    cur = build_norm(cur,
+            model.output_norm, NULL,
+            LLM_NORM_RMS, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    // lm_head
+    cur = build_lora_mm(model.output, cur);
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}

llama/llama.cpp/src/models/minicpm3.cpp

@@ -9,6 +9,7 @@ llm_build_minicpm3::llm_build_minicpm3(const llama_model & model, const llm_grap
 
     const uint32_t n_embd_head_qk_rope = hparams.n_rot;
     const uint32_t n_embd_head_qk_nope = hparams.n_embd_head_k - hparams.n_rot;
+
     const uint32_t kv_lora_rank = hparams.n_lora_kv;
 
     ggml_tensor * cur;

llama/llama.cpp/src/models/models.h

@@ -167,6 +167,10 @@ struct llm_build_exaone : public llm_graph_context {
     llm_build_exaone(const llama_model & model, const llm_graph_params & params);
 };
 
+struct llm_build_exaone_moe : public llm_graph_context {
+    llm_build_exaone_moe(const llama_model & model, const llm_graph_params & params);
+};
+
 struct llm_build_falcon : public llm_graph_context {
     llm_build_falcon(const llama_model & model, const llm_graph_params & params);
 };
@@ -303,6 +307,7 @@ struct llm_build_llada_moe : public llm_graph_context {
     llm_build_llada_moe(const llama_model & model, const llm_graph_params & params);
 };
 
+template <bool embed>
 struct llm_build_llama : public llm_graph_context {
     llm_build_llama(const llama_model & model, const llm_graph_params & params);
 };
@@ -311,10 +316,18 @@ struct llm_build_llama_iswa : public llm_graph_context {
     llm_build_llama_iswa(const llama_model & model, const llm_graph_params & params);
 };
 
+struct llm_build_maincoder : public llm_graph_context {
+    llm_build_maincoder(const llama_model & model, const llm_graph_params & params);
+};
+
 struct llm_build_mamba : public llm_graph_context_mamba {
     llm_build_mamba(const llama_model & model, const llm_graph_params & params);
 };
 
+struct llm_build_mimo2_iswa : public llm_graph_context {
+    llm_build_mimo2_iswa(const llama_model & model, const llm_graph_params & params);
+};
+
 struct llm_build_minicpm3 : public llm_graph_context {
     llm_build_minicpm3(const llama_model & model, const llm_graph_params & params);
 };
@@ -327,6 +340,10 @@ struct llm_build_mistral3 : public llm_graph_context {
     llm_build_mistral3(const llama_model & model, const llm_graph_params & params);
 };
 
+struct llm_build_modern_bert : public llm_graph_context {
+    llm_build_modern_bert(const llama_model & model, const llm_graph_params & params);
+};
+
 struct llm_build_mpt : public llm_graph_context {
     llm_build_mpt(const llama_model & model, const llm_graph_params & params);
 };
@@ -396,6 +413,11 @@ struct llm_build_plamo : public llm_graph_context {
     llm_build_plamo(const llama_model & model, const llm_graph_params & params);
 };
 
+template <bool iswa>
+struct llm_build_plamo3 : public llm_graph_context {
+    llm_build_plamo3(const llama_model & model, const llm_graph_params & params);
+};
+
 struct llm_build_plm : public llm_graph_context {
     llm_build_plm(const llama_model & model, const llm_graph_params & params);
 };
@@ -448,7 +470,8 @@ private:
                 ggml_tensor * cur,
                         int   il);
 
-    ggml_tensor * build_delta_net_chunking(
+    // returns pair of output and new state
+    std::pair<ggml_tensor *, ggml_tensor *> build_delta_net_chunking(
                 ggml_tensor * q,
                 ggml_tensor * k,
                 ggml_tensor * v,
@@ -460,7 +483,8 @@ private:
                 ggml_tensor * diag_mask,
                         int   il);
 
-    ggml_tensor * build_delta_net_autoregressive(
+    // returns pair of output and new state
+    std::pair<ggml_tensor *, ggml_tensor *> build_delta_net_autoregressive(
                 ggml_tensor * q,
                 ggml_tensor * k,
                 ggml_tensor * v,
@@ -475,6 +499,11 @@ private:
                 ggml_tensor * gate,
                         int   layer);
 
+    // returns pair of qkv, z
+    std::pair<ggml_tensor *, ggml_tensor *> build_qkvz(
+                ggml_tensor * input,
+                        int   il);
+
     const llama_model & model;
 };
 

llama/llama.cpp/src/models/modern-bert.cpp

@@ -0,0 +1,116 @@
+#include "models.h"
+
+llm_build_modern_bert::llm_build_modern_bert(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
+
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+    ggml_tensor * inp_pos = build_inp_pos();
+
+    // construct input embeddings (token, type, position)
+    inpL = build_inp_embd(model.tok_embd);
+    cb(inpL, "inp_embd", -1);
+
+    // embed layer norm
+    inpL = build_norm(inpL, model.tok_norm, nullptr, LLM_NORM, -1);
+    cb(inpL, "inp_norm", -1);
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    auto * inp_attn = build_attn_inp_no_cache();
+
+    for (int il = 0; il < n_layer; ++il) {
+        const float freq_base_l  = model.get_rope_freq_base(cparams, il);
+        const float freq_scale_l = model.get_rope_freq_scale(cparams, il);
+
+        cur = inpL;
+
+        // attention layer norm
+        if (model.layers[il].attn_norm) {
+            cur = build_norm(inpL,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM, il);
+            cb(cur, "attn_norm", il);
+        }
+
+        // self attention
+        cur = build_lora_mm(model.layers[il].wqkv, cur);
+        cb(cur, "wqkv", il);
+
+        const size_t type_size = ggml_type_size(cur->type);
+
+        ggml_tensor * Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head,    n_tokens, n_embd_head*type_size, cur->nb[1], 0*type_size*(n_embd));
+        ggml_tensor * Kcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head*type_size, cur->nb[1], 1*type_size*(n_embd));
+        ggml_tensor * Vcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head*type_size, cur->nb[1], 1*type_size*(n_embd + n_embd_gqa));
+
+        // RoPE
+        Qcur = ggml_rope_ext(
+                ctx0, Qcur, inp_pos, nullptr,
+                n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
+                ext_factor, attn_factor, beta_fast, beta_slow
+                );
+
+        Kcur = ggml_rope_ext(
+                ctx0, Kcur, inp_pos, nullptr,
+                n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
+                ext_factor, attn_factor, beta_fast, beta_slow
+                );
+
+        cb(Qcur, "Qcur", il);
+        cb(Kcur, "Kcur", il);
+        cb(Vcur, "Vcur", il);
+
+        cur = build_attn(inp_attn,
+                    model.layers[il].wo, nullptr,
+                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+        cb(cur, "kqv_out", il);
+
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
+            inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
+        }
+
+        // re-add the layer input
+        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
+        cb(ffn_inp, "ffn_inp", il);
+
+        // attention layer norm
+        cur = build_norm(ffn_inp,
+                model.layers[il].ffn_norm, NULL,
+                LLM_NORM, il);
+        cb(cur, "ffn_norm", il);
+
+        cur = build_ffn(cur,
+                model.layers[il].ffn_up,   NULL, NULL,
+                NULL,                      NULL, NULL,
+                model.layers[il].ffn_down, NULL, NULL,
+                NULL,
+                LLM_FFN_GEGLU, LLM_FFN_SEQ, il);
+
+        // attentions bypass the intermediate layer
+        cur = ggml_add(ctx0, cur, ffn_inp);
+
+        // input for next layer
+        inpL = cur;
+    }
+
+    cur = inpL;
+
+    cur = build_norm(cur,
+            model.output_norm, NULL,
+            LLM_NORM, -1);
+    cb(cur, "final_norm_out", -1);
+
+    if (hparams.pooling_type == LLAMA_POOLING_TYPE_CLS) {
+        // extracting cls token
+        cur = ggml_view_1d(ctx0, cur, hparams.n_embd, 0);
+        cb(cur, "cls_pooled_embd", -1);
+    }
+
+    cb(cur, "res_embd", -1);
+    res->t_embd = cur;
+    ggml_build_forward_expand(gf, cur);
+}

llama/llama.cpp/src/models/nemotron-h.cpp

@@ -67,7 +67,7 @@ ggml_tensor * llm_build_nemotron_h::build_attention_layer(ggml_tensor *
                                                           const llama_model &       model,
                                                           const int64_t             n_embd_head,
                                                           const int                 il) {
-    // compute Q and K and (optionally) RoPE them
+    // compute Q and K
     ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
     cb(Qcur, "Qcur", il);
     if (model.layers[il].bq) {

llama/llama.cpp/src/models/openai-moe-iswa.cpp

@@ -14,6 +14,9 @@ llm_build_openai_moe_iswa::llm_build_openai_moe_iswa(const llama_model & model,
     ggml_tensor * inp_out_ids = build_inp_out_ids();
 
     for (int il = 0; il < n_layer; ++il) {
+        const float freq_base_l  = model.get_rope_freq_base (cparams, il);
+        const float freq_scale_l = model.get_rope_freq_scale(cparams, il);
+
         ggml_tensor * inpSA = inpL;
 
         // norm
@@ -49,13 +52,13 @@ llm_build_openai_moe_iswa::llm_build_openai_moe_iswa(const llama_model & model,
 
             Qcur = ggml_rope_ext(
                     ctx0, Qcur, inp_pos, nullptr,
-                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                    n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
                     ext_factor, attn_factor, beta_fast, beta_slow
                     );
 
             Kcur = ggml_rope_ext(
                     ctx0, Kcur, inp_pos, nullptr,
-                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                    n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
                     ext_factor, attn_factor, beta_fast, beta_slow
                     );
 

llama/llama.cpp/src/models/plamo3.cpp

@@ -0,0 +1,128 @@
+#include "models.h"
+
+template <bool iswa>
+llm_build_plamo3<iswa>::llm_build_plamo3(const llama_model & model, const llm_graph_params & params) :
+    llm_graph_context(params) {
+    const int64_t head_dim_q = hparams.n_embd_head_k;
+    const int64_t head_dim_v = hparams.n_embd_head_v;
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL = build_inp_embd(model.tok_embd);
+    ggml_tensor * inp_pos = build_inp_pos();
+
+    using inp_attn_type = std::conditional_t<iswa, llm_graph_input_attn_kv_iswa, llm_graph_input_attn_kv>;
+    inp_attn_type * inp_attn = nullptr;
+
+    if constexpr (iswa) {
+        inp_attn = build_attn_inp_kv_iswa();
+    } else {
+        inp_attn = build_attn_inp_kv();
+    }
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    for (int il = 0; il < n_layer; ++il) {
+        ggml_tensor * residual = inpL;
+
+        float freq_base_l  = 0.0f;
+        float freq_scale_l = 0.0f;
+        if constexpr (iswa) {
+            freq_base_l  = model.get_rope_freq_base (cparams, il);
+            freq_scale_l = model.get_rope_freq_scale(cparams, il);
+        } else {
+            freq_base_l  = freq_base;
+            freq_scale_l = freq_scale;
+        }
+
+        cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
+        cb(cur, "attn_norm", il);
+
+        ggml_tensor * qkv = build_lora_mm(model.layers[il].wqkv, cur);
+        cb(cur, "wqkv", il);
+
+        const int32_t n_head    = hparams.n_head(il);
+        const int32_t n_head_kv = hparams.n_head_kv(il);
+
+        const int64_t q_offset = 0;
+        const int64_t k_offset = head_dim_q * n_head;
+        const int64_t v_offset = k_offset + head_dim_q * n_head_kv;
+
+        ggml_tensor * Qcur = ggml_view_3d(ctx0, qkv, head_dim_q, n_head, n_tokens,
+                head_dim_q * sizeof(float), qkv->nb[1], q_offset * ggml_element_size(qkv));
+        ggml_tensor * Kcur = ggml_view_3d(ctx0, qkv, head_dim_q, n_head_kv, n_tokens,
+                head_dim_q * sizeof(float), qkv->nb[1], k_offset * ggml_element_size(qkv));
+        ggml_tensor * Vcur = ggml_view_3d(ctx0, qkv, head_dim_v, n_head_kv, n_tokens,
+                head_dim_v * sizeof(float), qkv->nb[1], v_offset * ggml_element_size(qkv));
+
+        cb(Qcur, "Qcur", il);
+        cb(Kcur, "Kcur", il);
+        cb(Vcur, "Vcur", il);
+
+        Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
+        cb(Qcur, "attn_q_norm", il);
+        Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
+        cb(Kcur, "attn_k_norm", il);
+
+        Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr,
+                n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
+                ext_factor, attn_factor, beta_fast, beta_slow);
+        Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr,
+                n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
+                ext_factor, attn_factor, beta_fast, beta_slow);
+
+        const float attn_scale = 1.0f / sqrtf(float(head_dim_q));
+
+        cur = build_attn(inp_attn,
+                model.layers[il].wo, NULL,
+                Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, attn_scale, il);
+        cb(cur, "attn_out", il);
+
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur      = ggml_get_rows(ctx0, cur, inp_out_ids);
+            residual = ggml_get_rows(ctx0, residual, inp_out_ids);
+        }
+
+        cur = build_norm(cur, model.layers[il].attn_post_norm, NULL, LLM_NORM_RMS, il);
+        cb(cur, "attn_post_norm", il);
+
+        cur = ggml_add(ctx0, cur, residual);
+        cb(cur, "attn_residual", il);
+
+        residual = cur;
+
+        cur = build_norm(cur, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
+        cb(cur, "ffn_norm", il);
+
+        cur = build_ffn(cur,
+                model.layers[il].ffn_up,   NULL, NULL,
+                NULL,                      NULL, NULL,
+                model.layers[il].ffn_down, NULL, NULL,
+                NULL,
+                LLM_FFN_SWIGLU, LLM_FFN_SEQ, il);
+        cb(cur, "ffn_out", il);
+
+        cur = build_norm(cur, model.layers[il].ffn_post_norm, NULL, LLM_NORM_RMS, il);
+        cb(cur, "ffn_post_norm", il);
+
+        cur = ggml_add(ctx0, cur, residual);
+        cb(cur, "ffn_residual", il);
+
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+        inpL = cur;
+    }
+
+    cur = inpL;
+
+    cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
+    res->t_embd = cur;
+
+    cur = build_lora_mm(model.output, cur);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
+
+// Explicit template instantiations
+template struct llm_build_plamo3<false>;
+template struct llm_build_plamo3<true>;