README: clean tag block (fix best-agent typo, drop the 3 non-Field-Guide tags into a descriptive group) + add Output examples (real Modal-run outputs)

README.md

@@ -6,19 +6,21 @@ colorTo: yellow
 sdk: docker
 app_port: 7860
 pinned: false
-# Build Small Hackathon TRACK + MERIT-BADGE tags. Kebab-case best-guesses - confirm the exact strings against
-# the Field Guide (https://build-small-hackathon-field-guide.hf.space/) before final submit; the intent is right.
+# Build Small Hackathon tags: official tracks/sponsors/badges + descriptive build facts (for reviewers).
 tags:
-  - best-minicpm-build   # core entry
-  - backyard-ai          # TRACK: local, self-hosted AI (swap if you pick the other track)
+  - best-minicpm-build   # sponsor: full fine-tune of MiniCPM5-1B (core entry)
+  - backyard-ai          # track: local, self-hosted AI on CPU
+  - best-use-of-codex    # sponsor: code + Codex-attributed commits on GitHub
+  - best-use-of-modal    # sponsor: GGUF evaluated on Modal (see "How it was built")
+  - off-brand            # badge: custom UI well past the default Gradio look
+  - best-agent           # badge: the write -> run -> read -> debug -> verify loop
+  - best-demo            # badge: GIF + video demo
+  - tiny-titan           # badge: a 1B doing the real agentic loop
+  - bonus-quest-champion # badge: most bonus criteria across the board
+  - judges-wildcard      # badge: auto-considered for every entry
   - well-tuned           # full fine-tune of MiniCPM5-1B, published on the Hub
   - llama-champion       # served on the llama.cpp runtime
   - off-the-grid         # runs fully local on a CPU, no cloud model APIs
-  - best-use-of-codex
-  - plus best-agent
-  - best-demo
-  - tiny-titan
-  - best-use-of-modal
 ---
 
 # 🛠️ MiniCPM5-1B-Agent
@@ -62,6 +64,51 @@ artifact, the same loop a big agent runs, shrunk to something you could host in
 It is a tiny 1B on a free CPU: expect **~4 min per simple turn**, longer for multi-step tasks (the demo video
 shows it working end-to-end, so it can be judged even if a live run is slow).
 
+
+<details><summary><b>Output examples</b></summary>
+
+Real single-turn outputs from the Q8_0 GGUF, generated on Modal (CPU):
+
+**Prompt:** Write a Python function `fib(n)` that returns the nth Fibonacci number iteratively. Print fib(10).
+
+```python
+def fib(n):
+    if n == 0:
+        return 0
+    elif n == 1:
+        return 1
+    a, b = 0, 1
+    for _ in range(2, n + 1):
+        a, b = b, a + b
+    return b
+
+print(fib(10))
+```
+
+**Prompt:** Convert an integer (1-3999) to a Roman numeral; demonstrate on 1994.
+
+```python
+def int_to_roman(num):
+    if not 1 <= num <= 3999:
+        raise ValueError("Number must be between 1 and 3999")
+    roman_map = [(1000,'M'),(900,'CM'),(500,'D'),(400,'CD'),(100,'C'),(90,'XC'),
+                 (50,'L'),(40,'XL'),(10,'X'),(9,'IX'),(5,'V'),(4,'IV'),(1,'I')]
+    result = []
+    for value, symbol in roman_map:
+        while num >= value:
+            result.append(symbol); num -= value
+    return ''.join(result)
+
+print(int_to_roman(1994))  # -> MCMXCIV
+```
+
+**Prompt:** How many $40 games can I buy with $200, and how much is left over?
+
+> You can buy **5 games** with $200, and have **$0 left over**.
+> Number of games = 200 / 40 = 5; remaining = 200 - (5 x 40) = $0.
+
+</details>
+
 ## Model, dataset & full reproduction
 
 → **[Luminia/MiniCPM5-1B-Agent-GGUF](https://huggingface.co/Luminia/MiniCPM5-1B-Agent-GGUF)** (model card =