feat: Variet Engine v1.0 + 5-model tuning complete

Phase 01 (LLM Tuning):
- Gemma4 26B: 74.65 t/s (fast)
- Qwen 35B: 61.62 t/s (balanced)
- Gemma4 31B: 16.0 t/s (deep-coder)
- Qwen 27B: 16.7 t/s (deep-logic)
- Qwen 122B: 8.95 t/s (ultra, GPU 1 only)

Phase 02 (API Engine):
- FastAPI reverse proxy on port 8000
- /engine/switch hot-swap with 503 protection
- config/engine_models.json as single source of truth
- Replaced 4 individual .bat files with unified engine

File cleanup:
- scripts/ 85 files -> 9 + _archive/
- Root .bat files -> _archive/

scripts/_archive/benchmarks/bench_122b.py

@@ -0,0 +1,38 @@
+import urllib.request, json, time, sys
+try: sys.stdout.reconfigure(encoding='utf-8')
+except: pass
+
+BASE = "http://127.0.0.1:8000"
+prompt = "Write a Python function to calculate fibonacci numbers efficiently using memoization. Include type hints and docstring."
+
+payload = json.dumps({
+    "model": "m",
+    "messages": [
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": prompt}
+    ],
+    "max_tokens": 500,
+    "temperature": 0.0
+}).encode('utf-8')
+
+req = urllib.request.Request(
+    f"{BASE}/v1/chat/completions",
+    data=payload,
+    headers={"Content-Type": "application/json"}
+)
+
+print("Sending request...")
+t0 = time.time()
+resp = json.loads(urllib.request.urlopen(req, timeout=300).read())
+dt = time.time() - t0
+
+u = resp.get("usage", {})
+tokens = u.get("completion_tokens", 0)
+speed = tokens / dt if dt > 0 else 0
+
+print(f"\n=== 122B Benchmark ===")
+print(f"Time: {dt:.1f}s")
+print(f"Completion Tokens: {tokens}")
+print(f"Speed: {speed:.2f} t/s")
+print(f"\n--- Response Preview ---")
+print(resp["choices"][0]["message"]["content"][:300])