Grade muse-glimmer-28b full 7-prompt battery — new benchmark leader

7 entries (30→37 total). Muse Glimmer 28B (GGUF) avg 80.7 — the strongest
model in the benchmark, 6/7 prompts Minor Logic Flaws:
  lfu 76 | webhook 81 | automation 89 | rust 85 | data 88 | tts 58 | mcp 88

Standout results:
- rust 85 (KAT 36, Qwen3-Coder 54) — real tokio channels (mpsc::channel, not
  hallucinated mpsc::bounded), two-tier CancellationToken, zero clippy lints;
  one-line E0507 compile fix.
- automation 89 — first model to print a correct summary (98/2/0/100);
  atomic temp+fsync+rename checkpointing.
- data 88 edges out Gemma-26B's 86; mcp 88 sets the bar on a new prompt.
Only weak spot: tts 58 (backpressure raises instead of awaits, like Qwen3-Coder).

Captured via the native /api/v1/chat fix (real tok/sec + TTFT). Slow
deep-thinker: ~17-19 t/s, 5-9 min/prompt, ~5-9k tokens incl. reasoning.

Also gitignore checkpoint.json (automation test runtime artifact).

Co-Authored-By: Claude <noreply@anthropic.com>

outputs/.last_response_schema.json

@@ -1,13 +1,13 @@
 {
-  "model_instance_id": "google/gemma-4-26b-a4b",
+  "model_instance_id": "meta/muse-glimmer",
   "stats": {
-    "input_tokens": 718,
-    "total_output_tokens": 5153,
-    "reasoning_output_tokens": 1919,
-    "tokens_per_second": 53.47535329547263,
-    "time_to_first_token_seconds": 1.127
+    "input_tokens": 495,
+    "total_output_tokens": 4749,
+    "reasoning_output_tokens": 3228,
+    "tokens_per_second": 20.201965201572563,
+    "time_to_first_token_seconds": 2.571259
   },
-  "response_id": "resp_d8fafc117fa4e51b927d8cd531ded49984ae59e49c39d3eb",
+  "response_id": "resp_a37c5d6bb520db613250881b4919be086d4aed4a8f3a078a",
   "_output_types": [
     "reasoning",
     "message"