Lucasgitguy-co/spot-snake-laya-multilingual-v0.1

Spot Snake Laya Multilingual

A Laya typed-decision model post-trained on the room-then-heading teacher for Spot's Biscuit Snake. The starting checkpoint was convaiinnovations/laya-multilingual.

Held-out slice	Rows	Base	Post-trained	Heading baseline
Legacy test (English)	1,708	53.57%	100.00%	99.94%
Challenge (combined)	1,600	46.81%	99.94%	62.75%
Challenge (English)	800	46.88%	100.00%	62.75%
Challenge (Dutch)	800	46.75%	99.88%	62.75%
Challenge (heading loses, both languages)	400	18.00%	99.75%	0.00%

Results measure teacher agreement. The English and Dutch challenge cases use the same boards. Choice temperature fitting returned approximately 1.0. The held-out challenge's ten-bin expected calibration error was 0.0483. See spot-snake-test.json for the full breakdown. Game score and win rate require separate rollouts. Keep the game's legality checks in the move selection path. The task data cover English and Dutch.

Load with laya.load() and pass the current state and a choice question containing the legal options. See the dataset for example inputs.

Training

The encoder and decision head were fine-tuned for three epochs using Laya's RLCD trainer on an Apple M4. The run used 5,600 training rows and reserved 400 training-split rows for temperature fitting. The scores above come from the separate test splits.

Quickstart

Install the runtime with pip install laya==0.4.26.

import json

import laya
from huggingface_hub import hf_hub_download

data = hf_hub_download(
    "Lucasgitguy-co/spot-snake-decisions-v0.2",
    "challenge_test.jsonl",
    repo_type="dataset",
)
with open(data, encoding="utf-8") as stream:
    example = json.loads(next(stream))

model = laya.load("Lucasgitguy-co/spot-snake-laya-multilingual-v0.1")
answer = model.predict(example["state"], example["questions"], min_confidence=0)
print(answer["answers"]["move"]["choice"])