README.md

metadata

title: First-Principle AI · Gotcha Arena
emoji: 🎯
colorFrom: indigo
colorTo: green
sdk: gradio
sdk_version: 6.14.0
python_version: '3.12'
app_file: app.py
fullWidth: true
header: mini
short_description: Trick-question arena for a small first-principle reasoner
suggested_hardware: zero-a10g
models:
  - build-small-hackathon/phase-3-gguf
tags:
  - gradio
  - build-small-hackathon
  - track:wood
  - sponsor:nvidia
  - achievement:offbrand
  - achievement:offgrid
  - achievement:sharing
  - reasoning
  - nemotron
  - gguf
  - llama-cpp
  - zerogpu
license: mit

🎯 First-Principle AI · Gotcha Arena

A small reasoning model that reads the question, not the pattern.

Big models get fooled by familiar-looking trick questions — a riddle with one word swapped, a decoy distance, a false premise. First-Principle AI is a compact ~31.6B Q8 MoE (Nemotron-H) reasoning model built for this hackathon, and this Space is a playground for testing exactly that skill.

What you can do here

• 🎯 The Gauntlet — pick a trick question from the deck (or hit Surprise me), run it, and see whether the model reasons past the trap. A live scoreboard tracks how many traps it caught vs. fell for, and reveals why each one is a trap.
• 🧠 Trap It Yourself — two modes:
  • Trap the AI: write your own trick question and try to fool it.
  • Blind-Spot Check: paste any real plan or "should I X or Y?" decision and the model surfaces the hidden assumption you might be missing. (This is the practical use case — the same first-principle reasoning, pointed at your own decisions.)
• ⚙️ Engine Room — sampling controls and live llama.cpp runtime diagnostics.

The trap deck mixes the model's signature goal-binding puzzles (the object that must physically move) with classic gotchas: the Moses illusion, the bat-and-ball cognitive-reflection test, unit misdirection, false-premise questions, and language-parsing traps.

The auto-verdict in the Gauntlet is a lightweight keyword heuristic — the real proof is reading the model's answer. It's there to keep score, not to grade.

The model

Repo	build-small-hackathon/phase-3-gguf
Base	owenisas/nemotron-3-nano-reasoning (merged)
Architecture	Nemotron-H hybrid MoE, ChatML chat template
Size	model-Q8_0.gguf, ~31.6B params (under the 32B cap)
Context (this Space)	2,048 tokens (n_ctx); the GGUF reports a larger architectural max, but the Space serves 2K for fast, memory-safe startup
Runtime	official llama.cpp llama-server (release b9360) on ZeroGPU

Because it's a Mixture-of-Experts model, only a small slice of the 31.6B parameters is active per token, which is what makes a model this capable run on a small footprint.

Runtime notes

• The 33.6 GB Q8 GGUF is not preloaded during build (it would make startup unreliable). It downloads and loads through llama-server on the first prompt, which takes about 1–2 minutes; every prompt after that reuses the warm server.
• ZeroGPU is primarily documented around PyTorch workloads, so this app runs the GGUF through the official llama.cpp CLI path instead of depending on a Python extension compile at build time. If the runtime can't provide enough memory or a compatible binary, the app shows a visible diagnostic instead of a silent mock answer.

Hackathon submission — Build Small

Track: 🌲 Thousand Token Wood (track:wood) — a whimsical, AI-native game. Sponsor model: NVIDIA Nemotron (sponsor:nvidia). Badges targeted: Off Brand (custom UI), Off Grid (fully local llama.cpp inference), Sharing (social post).

• Gradio Space inside the org — build-small-hackathon/First-Principle-AI
• Model ≤ 32B params — ~31.6B Q8 MoE (Nemotron-H)
• Track + badge tags in YAML — track:wood, sponsor:nvidia, achievement:offbrand/offgrid/sharing
• Write-up of idea + technical approach — this README
• Demo video — TODO: add a public video link (YouTube / Loom / Space) showing the app running
• Social post + link — TODO: post once about the build and paste the URL here
• Team HF usernames — owenisas (add teammates if any; each must register + join the org)

The three TODOs need a human (record the video, make the social post, confirm teammates), then paste the links above. Re-run the field-guide validator after editing: https://build-small-hackathon-field-guide.hf.space/submit

Local smoke test

cd hf-spaces/phase-3-gguf-lab
PHASE3_DISABLE_MODEL=1 python app.py   # UI only, no model load