madhavbiplov/laya-snake-mlx

Snake · balanced (multilingual)

A Laya typed-decision model, fine-tuned with LayaStudio on an Apple silicon Mac. It answers the questions below in a single forward pass, with calibrated probabilities and zero generated tokens.

Base model: aac6fef/laya-multilingual-mlx · method: lora, proper objective · trained in 18 minutes on an Apple silicon Mac.

Measured on the held-out test split

Metric	Base model	This model
Accuracy	15.8%	98.8% [97.6%–99.4%]
Calibration error (ECE)	0.246	0.002
Log loss	1.607	0.047
Brier score	0.865	0.021
Decisions scored	600	600

Fine-tuning fixed 500 test decisions the base model got wrong and broke 2 it got right (exact McNemar p < 0.001).

Test rows were never trained on. Accuracy intervals are Wilson intervals; the paired test is an exact McNemar test between the base and the fine-tuned model on the same rows.

Use it

pip install laya-mlx            # Apple silicon

import json, laya_mlx as laya
from huggingface_hub import hf_hub_download

agent = laya.load("madhavbiplov/laya-snake-mlx")
questions = json.load(open(hf_hub_download("madhavbiplov/laya-snake-mlx", "questions.json")))
print(agent.predict("your text here", questions)["answers"])

Ask it these questions: the instructions and option texts are part of the model's input, so changing them changes the task it was tuned for.

{
  "move": {
    "type": "choice",
    "instructions": "Snake board. Pick the next move: stay inside the board, do not hit the snake, and take the shortest safe path to the food.",
    "criteria": {
      "UP": "one cell up",
      "DOWN": "one cell down",
      "LEFT": "one cell left",
      "RIGHT": "one cell right"
    }
  }
}

The same folder also loads in the upstream PyTorch laya package on Linux and NVIDIA, and LayaStudio can export it to ONNX.

Provenance

{
  "base_model": "aac6fef/laya-multilingual-mlx",
  "hyperparameters": {
    "method": "lora",
    "objective": "proper",
    "epochs": 4,
    "batch_size": 8,
    "grad_accum": 2,
    "lr": 0.0002,
    "head_lr": 0.0001,
    "lora_rank": 16,
    "lora_alpha": 32,
    "lora_dropout": 0.05,
    "lora_layers": 0,
    "full_layers": 4,
    "head_dropout": 0.1,
    "weight_decay": 0.01,
    "warmup": 0.06,
    "max_grad_norm": 1.0,
    "shuffle_options": true,
    "class_weighting": "none",
    "patience": 2,
    "grad_checkpoint": "auto",
    "precision": "bfloat16",
    "seed": 13
  },
  "train_decisions": 2339,
  "best_epoch": 3,
  "temperature": [
    1.3116,
    1.0,
    1.0
  ],
  "temperature_by_options": {
    "choice:3-5": 1.3116
  },
  "dataset_sha256": "970a6be8b1b458204b95fa747c66b4c527512bdf373f549f88d97391243cd24a",
  "trained_on": "2026-09-23T01:11:23"
}

License and attribution

Apache-2.0. Laya and its pretrained weights are by Convai Innovations; this checkpoint is a fine-tune of aac6fef/laya-multilingual-mlx and carries the same licence. Fine-tuned and published with LayaStudio.