feat(ai B.1 #15 + A.1 #28): cleanup presets and sequential-GPU guard

Two new Settings → AI knobs that compose cleanly with what already
shipped (aiTier, LLM model, translator prompt framing).

**B.1 #15 — Named cleanup presets.** LlmPromptPreset enum
(Default / Email / Notes / Code) appends a short context hint onto
the CLEANUP_PROMPT just before generation. Presets shape tone and
structure ("email paragraph", "bulleted meeting notes", "preserve
technical terms") without licensing the content-editing the
translator-not-editor framing forbids. cleanup_transcript_text_cmd
now takes `preset: Option<String>` which runs through the new
LlmPromptPreset::parse (normalises aliases like "meeting-notes",
collapses unknown values to Default).

**A.1 #28 — Sequential-GPU guard.** New LocalEngine::unload drops
the backend + model_id so a subsequent load actually reclaims VRAM.
load_llm_model, load_model, and load_parakeet_model Tauri commands
grow an optional `concurrent: bool` argument. When concurrent is
Some(false), loading LLM first unloads whisper+parakeet, and vice
versa — prevents VRAM OOM on tight-VRAM setups. Default is the
previous parallel behaviour so nothing changes for multi-GB cards.
Transcribe-in-progress paths (transcribe_pcm, transcribe_file, live)
pass None, so mid-dictation model loads don't accidentally tear
down the LLM.

Settings UI (AI section):
- Cleanup preset segmented button + descriptive copy for each option.
- GPU concurrency segmented button with explicit trade-off text
  ("faster transitions vs fits in tight VRAM").

Frontend wiring:
- settings.llmPromptPreset flows from DictationPage's
  cleanupTranscriptIfEnabled into the Tauri command.
- settings.aiGpuConcurrency flows from both DictationPage (auto-load
  on record) and SettingsPage (manual load/unload buttons) as
  `concurrent: "parallel" === true` to the load commands.

Tests: three new preset cases in crates/ai-formatting/src/llm_client.rs
(parse aliases, suffix non-empty for non-default, default suffix
empty). All 139 existing lib tests still pass.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

src-tauri/src/commands/llm.rs

@@ -2,7 +2,7 @@ use tauri::{Emitter, State};
 
 use crate::commands::power::PowerAssertion;
 use crate::AppState;
-use kon_ai_formatting::llm_cleanup_text;
+use kon_ai_formatting::{llm_cleanup_text, LlmPromptPreset};
 use kon_core::hardware;
 use kon_llm::model_manager::{self, model_info};
 use kon_llm::LlmModelId;
@@ -86,6 +86,7 @@ pub async fn load_llm_model(
     state: State<'_, AppState>,
     model_id: String,
     use_gpu: Option<bool>,
+    concurrent: Option<bool>,
 ) -> Result<(), String> {
     let id = parse_model_id(model_id)?;
     let path = model_manager::model_path(id);
@@ -93,6 +94,15 @@ pub async fn load_llm_model(
         return Err("Model not downloaded — call download_llm_model first".to_string());
     }
 
+    // Sequential-GPU guard (brief item A.1 #28): when the user has opted
+    // out of concurrent GPU residency, free the transcription engines
+    // before loading the LLM. Prevents VRAM OOM on tight-GPU setups.
+    // concurrent=None or Some(true) preserves legacy parallel behaviour.
+    if concurrent == Some(false) {
+        state.whisper_engine.unload();
+        state.parakeet_engine.unload();
+    }
+
     let engine = state.llm_engine.clone();
     let use_gpu = use_gpu.unwrap_or(true);
     tokio::task::spawn_blocking(move || engine.load_model(id, &path, use_gpu))
@@ -335,6 +345,7 @@ pub async fn cleanup_transcript_text_cmd(
     state: State<'_, AppState>,
     transcript: String,
     profile_id: Option<String>,
+    preset: Option<String>,
 ) -> Result<String, String> {
     let resolved_profile_id =
         profile_id.unwrap_or_else(|| kon_storage::DEFAULT_PROFILE_ID.to_string());
@@ -346,13 +357,21 @@ pub async fn cleanup_transcript_text_cmd(
             .map(|term| term.term)
             .collect();
 
+    // Named preset (brief item B.1 #15): Email / Notes / Code shape the
+    // output tone + structure without changing the translator-not-editor
+    // contract. None or unknown → Default (no additional guidance).
+    let resolved_preset = preset
+        .as_deref()
+        .map(LlmPromptPreset::parse)
+        .unwrap_or(LlmPromptPreset::Default);
+
     let engine = state.llm_engine.clone();
     tokio::task::spawn_blocking(move || {
         // macOS: pin a power assertion for the duration of the LLM
         // generation so App Nap can't decide to throttle us mid-token.
         // No-op on every other OS. Item #9.
         let _power_guard = PowerAssertion::begin("kon LLM cleanup");
-        llm_cleanup_text(&engine, &transcript, &profile_terms)
+        llm_cleanup_text(&engine, &transcript, &profile_terms, resolved_preset)
     })
     .await
     .map_err(|e| e.to_string())?