refactor(phase-01): v3 retune fast & balanced roles

fast (Gemma 4 26B-A4B):
- Enable mmproj GPU loading (vision ~1s, 12x faster than CPU)
- KV f16 → q8_0 (save ~2.5 GB VRAM for mmproj)
- Tensor split 0.5,0.5 → 0.43,0.57 (13/17 layers)
- Remove --mlock/--poll/--prio/-t/-tb (no measurable impact)
- measured_tps 74.65 → 71.89 (trade 3.7% speed for vision)

balanced (Qwen 3.5 35B-A3B):
- Tensor split 0.5,0.5 → 0.48,0.52 (enables pipeline parallelism)
- Ubatch 128 → 256 (prefill +78%: 649 → 1,157 t/s)
- mmproj + --no-mmproj-offload (CPU vision, VRAM headroom)
- Remove useless flags same as fast
- measured_tps 61.62 → 64.16 (+4.1%)

Other:
- Document full retuning in docs/v3_{fast,balanced}_retuning_log.md
- Session report at .planning/reports/20260411-session-report.md
- Add bench utilities: bench_short/bench_long/test_ts_ratios
- Speculative decoding (E2B draft) experimented but rejected
  (+14% gen vs -31% cold start + tokenizer mismatch + mmproj conflict)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

scripts/bench_short.py

@@ -0,0 +1,87 @@
+"""Phase 01 style short-prompt benchmark using llama.cpp internal timings."""
+import json
+import urllib.request
+import sys
+
+try:
+    sys.stdout.reconfigure(encoding="utf-8")
+except Exception:
+    pass
+
+
+def bench_text(model_name, n=200):
+    payload = json.dumps({
+        "model": model_name,
+        "messages": [{"role": "user", "content": "Count from 1 to 50, each number on a new line."}],
+        "max_tokens": n,
+        "temperature": 0,
+    }).encode()
+    req = urllib.request.Request(
+        "http://127.0.0.1:8000/v1/chat/completions",
+        data=payload,
+        headers={"Content-Type": "application/json"},
+    )
+    with urllib.request.urlopen(req, timeout=120) as r:
+        return json.loads(r.read()).get("timings", {})
+
+
+def bench_image(model_name, image_path, prompt):
+    import base64
+    with open(image_path, "rb") as f:
+        b64 = base64.b64encode(f.read()).decode()
+    payload = json.dumps({
+        "model": model_name,
+        "messages": [{
+            "role": "user",
+            "content": [
+                {"type": "text", "text": prompt},
+                {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{b64}"}},
+            ],
+        }],
+        "max_tokens": 100,
+        "temperature": 0.3,
+    }).encode()
+    req = urllib.request.Request(
+        "http://127.0.0.1:8000/v1/chat/completions",
+        data=payload,
+        headers={"Content-Type": "application/json"},
+    )
+    with urllib.request.urlopen(req, timeout=600) as r:
+        return json.loads(r.read()).get("timings", {})
+
+
+def main():
+    label = sys.argv[1] if len(sys.argv) > 1 else "run"
+    model = sys.argv[2] if len(sys.argv) > 2 else "fast"
+    do_image = "--image" in sys.argv
+
+    print(f"=== [{label}] model={model} do_image={do_image} ===")
+    print("warmup...")
+    try:
+        bench_text(model, 10)
+    except Exception as e:
+        print(f"warmup err: {e}")
+
+    print("text 5-run:")
+    runs = []
+    for i in range(5):
+        t = bench_text(model, 200)
+        runs.append(t["predicted_per_second"])
+        print(f"  Run {i+1}: gen {t['predicted_per_second']:.2f} t/s ({t['predicted_n']} tok, {t['predicted_ms']:.0f}ms) | prompt {t['prompt_per_second']:.1f} t/s ({t['prompt_n']} tok)")
+    avg = sum(runs) / len(runs)
+    print(f"  TEXT AVG: {avg:.2f} t/s  BEST: {max(runs):.2f}  MIN: {min(runs):.2f}")
+
+    if do_image:
+        prompts = [
+            "What do you see in this image? One sentence.",
+            "Describe the subject and background in one sentence.",
+            "What is the most prominent feature? One sentence.",
+        ]
+        print("vision 3-run (640x640 cat):")
+        for i, p in enumerate(prompts):
+            t = bench_image(model, "logs/vision_test/sample.jpg", p)
+            print(f"  Run {i+1}: prompt {t['prompt_n']} tok ({t['prompt_ms']:.0f}ms, {t['prompt_per_second']:.1f} t/s) | gen {t['predicted_n']} tok ({t['predicted_per_second']:.1f} t/s)")
+
+
+if __name__ == "__main__":
+    main()