---
license: mit
tags:
  - fighting-game
  - tiny-model
  - reinforcement-learning
  - game-ai
library_name: torch
---

# Duel Tiny Fighter (78,863 parameters)

A real-time CPU policy network for NPC move selection in a 3D fighting game.
Runs in <1ms per inference on CPU, conditioned on Nemotron strategic weights.

## Architecture

| Layer | Shape | Notes |
|-------|-------|-------|
| Linear | 168 → 256 | One-hot move history + scalars |
| LayerNorm | 256 | Stable at batch=1 inference |
| ReLU + Dropout(0.1) | | |
| Linear | 256 → 128 | |
| LayerNorm | 128 | |
| ReLU + Dropout(0.1) | | |
| Linear | 128 → 15 | Logits over 15 moves |

**Total parameters:** 78,863

## Move Vocabulary

`jab`, `cross`, `hook`, `kick`, `uppercut`, `block`, `parry`, `dodge`,
`advance`, `retreat`, `grapple`, `throw`, `sweep`, `feint`, `wait`

## Input Features (168-dim)

- Last 5 NPC moves (5 × 15 one-hot = 75)
- Last 5 player moves (5 × 15 one-hot = 75)
- HP difference, stamina difference (2)
- Distance one-hot (3)
- Strategy weights: aggression, defense, parry_affinity, kick_affinity, grapple_affinity (5)
- Round normalised (1)
- Absolute HP, stamina for both (4)
- Padding to 168

## Inference

```python
import torch
from tiny_fighter import TinyFighter, state_to_features, make_move_mask

model = TinyFighter()
model.load_state_dict(torch.load("tiny_fighter.pt", map_location="cpu"), strict=False)
model.eval()

feats = state_to_features(
    last_npc_moves=["jab", "block"],
    last_player_moves=["cross", "retreat"],
    player_hp=80.0, npc_hp=50.0,
    player_stamina=60.0, npc_stamina=40.0,
    distance="mid",
    aggression=0.7, defense=0.3,
    parry_affinity=0.4, kick_affinity=0.6,
    grapple_affinity=0.2,
)
mask = make_move_mask("mid")

with torch.inference_mode():
    logits = model.predict(feats, mask)
    move = logits.softmax(-1).argmax().item()

print(f"Selected: {model.MOVES[move]}")
```

## Training

Trained on 20k procedurally generated (state, strategy_weights) → move examples
using supervised learning on CPU. The model learns to map Nemotron's strategic
direction (aggressive/defensive/grappling) into concrete move probabilities.

## Part of Duel of Nemotron

- **Strategist:** Nemotron 3 Nano 4B (fine-tuned, Modal A10)
- **Executor:** This tiny model (CPU, <1ms)
- **Game:** React + Three.js 3D fighting game

Built for the [Build Small Hackathon](https://huggingface.co/build-small-hackathon)
by [@sankalphs](https://huggingface.co/sankalphs).