---
license: apache-2.0
language:
- en
tags:
- game
- fine-tuned
- gguf
base_model:
- meta-llama/Llama-3.2-3B
- Qwen/Qwen3.5-3B
- Qwen/Qwen3-1.7B
---

# Rock Paper Anything

A series of fine-tuned small models for an open ended version of the classic game. Premise suggested by an 8 year old.

The goal has been to have a model small enough to play the game offline or in a browser and on low end machines. Each version was fine-tuned using QLoRA via Unsloth, meaning only ~1% of the model's parameters were trained, with the rest frozen. The adapter was then merged back into the base weights and quantized to Q4_K_M GGUF format.

## Usage

**Ollama**
```bash
ollama create rockpaperanything -f Modelfile
ollama run rockpaperanything '["caterpillar", "halitosis"]'
```

```json
{"winner": "caterpillar", "loser": "halitosis", "reason": "The caterpillar's transformation from gnat food to butterfly beauty defies even the most persistent bad breath."}
```

## Input / output

Input is best done as a JSON array of two items:
```json
["arcade fire", "pie"]
```

Output is JSON:
```json
{
    "winner": "arcade fire",
    "loser": "pie",
    "reason": "The Arcade Fire's infectious energy fills the entire venue, making even a pie feel like it needs to dance."
}
```

## Models
- v1: from Llama-3.2 3B — 1,500 examples (2.02gb as gguf)
- v2: from Llama-3.2 3B — 2,200 examples (2.02gb as gguf)
- v3: from Qwen3.5 2B — 2,200 examples (1.27gb as gguf; 1.08gb as mlc)
- v4: from Qwen3 1.7B — 2,200 examples (1.11gb as gguf; 0.98gb as mlc))