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/quick_bench.py

@@ -0,0 +1,45 @@
+"""Quick benchmark for running llama-server instance"""
+import urllib.request, json, time, sys
+
+BASE = "http://127.0.0.1:8000"
+RUNS = 5
+TOKENS = 200
+
+def bench(max_tokens=TOKENS):
+    payload = json.dumps({
+        "model": "m",
+        "messages": [{"role": "user", "content": "Count from 1 to 100, each number on a new line."}],
+        "max_tokens": max_tokens,
+        "temperature": 0
+    }).encode()
+    req = urllib.request.Request(
+        f"{BASE}/v1/chat/completions",
+        data=payload,
+        headers={"Content-Type": "application/json"}
+    )
+    t0 = time.time()
+    resp = json.loads(urllib.request.urlopen(req, timeout=300).read())
+    dt = time.time() - t0
+    ct = resp.get("usage", {}).get("completion_tokens", 0)
+    return ct / dt if dt > 0 else 0, ct, dt
+
+print("Warmup...", flush=True)
+try:
+    bench(20)
+except Exception as e:
+    print(f"Warmup failed: {e}")
+    sys.exit(1)
+print("Warmup done\n", flush=True)
+
+speeds = []
+for i in range(RUNS):
+    tps, ct, dt = bench()
+    speeds.append(tps)
+    print(f"  Run {i+1}: {tps:.2f} t/s (tokens={ct}, time={dt:.2f}s)", flush=True)
+
+avg = sum(speeds) / len(speeds)
+best = max(speeds)
+mn = min(speeds)
+print(f"\n{'='*50}")
+print(f"  RESULT: AVG {avg:.2f} / BEST {best:.2f} / MIN {mn:.2f} t/s")
+print(f"{'='*50}")