# Spot Snake decision fine-tunes by Lucasgitguy-co

> Spot Snake decision fine-tunes rank safe game moves with d1 or MiniMind-JEV. Read their licences, teacher-agreement tests and calibration limits.

Updated 2026-11-07 (Australia/Adelaide)

## Quick answers

### What is Spot Snake decision fine-tunes by Lucasgitguy-co?

[Spot Snake decision fine-tunes](<https://decisionmodels.ai/models/spot-snake>): Four local move-ranking fine-tunes for Spot’s Biscuit Snake: d1-2B, d1-3B, MiniMind-JEV and [Laya](<https://decisionmodels.ai/models/laya>) Multilingual.

[Spot Snake decision fine-tunes facts](<https://decisionmodels.ai/models/spot-snake>)

[Source](<https://huggingface.co/Lucasgitguy-co/spot-snake-d1-3B-v0.1>)

### Who makes Spot Snake decision fine-tunes?

[Spot Snake decision fine-tunes](<https://decisionmodels.ai/models/spot-snake>): Lucasgitguy-co

[Spot Snake decision fine-tunes facts](<https://decisionmodels.ai/models/spot-snake>)

[Source](<https://huggingface.co/Lucasgitguy-co/spot-snake-d1-3B-v0.1>)

By Lucasgitguy-co · d1/MiniMind-JEV licence clarification; Laya weights uploaded 6 Nov: 2026-11-05 · MIXED LICENCES

Four local move-ranking fine-tunes for Spot’s Biscuit Snake: d1-2B, d1-3B, MiniMind-JEV and [Laya](<https://decisionmodels.ai/models/laya>) Multilingual. The game supplies safe moves; the model ranks them to imitate a heuristic teacher that leaves the most open room. Keep the game’s collision checks. The d1-2B, d1-3B and MiniMind-JEV weights were uploaded on 5 November; the [Laya](<https://decisionmodels.ai/models/laya>) Multilingual weights were uploaded on 6 November.

## Facts

- Variants: d1-2B and d1-3B merged weights with GGUF formats; MiniMind-JEV ranking head and BGE encoder; [Laya](<https://decisionmodels.ai/models/laya>) Multilingual fine-tune
- Inputs: Game state and candidate safe moves
- Licence: [d1](<https://decisionmodels.ai/models/d1>) variants: LFM Open License v1.0 plus Apache-2.0 contributions (LvW-license-Snake-1.0); MiniMind-JEV: MIT; [Laya](<https://decisionmodels.ai/models/laya>) Multilingual: Apache-2.0
- Calibration: [Laya](<https://decisionmodels.ai/models/laya>) Multilingual: temperature fitted on 400 reserved training-split rows, returning approximately 1.0. d1-2B, d1-3B and MiniMind-JEV: confidence was not recalibrated after training
- Multilingual release: [Laya](<https://decisionmodels.ai/models/laya>) Multilingual fine-tune; English and Dutch; [laya](<https://decisionmodels.ai/models/laya>) 0.4.26

## Claims and sources

- **VENDOR-REPORTED**: On 1,708 held-out moves, the maker reports 99.82% teacher agreement for d1-3B and 55.80% for MiniMind-JEV. Saved-file reload checks cover 32 rows. Teacher agreement measures imitation of the heuristic; game scores, win rates and speed were not measured. [source](<https://huggingface.co/Lucasgitguy-co/spot-snake-d1-3B-v0.1>)
- **VENDOR-REPORTED**: The multilingual card reports 99.94% teacher agreement on 1,600 challenge cases, with English and Dutch using the same boards. It reports ten-bin ECE 0.0483. These are teacher-label checks; game score and win rate need separate rollouts. [source](<https://huggingface.co/Lucasgitguy-co/spot-snake-laya-multilingual-v0.1>)

## Links

- [Source: Model card and files](<https://huggingface.co/Lucasgitguy-co/spot-snake-d1-3B-v0.1>)
- [Source: d1-2B card and weights](<https://huggingface.co/Lucasgitguy-co/spot-snake-d1-2B-v0.1>)
- [Source: MiniMind-JEV card and weights](<https://huggingface.co/Lucasgitguy-co/spot-snake-minimind-jev-v0.1>)
- [Source: Multilingual card and weights](<https://huggingface.co/Lucasgitguy-co/spot-snake-laya-multilingual-v0.1>)

## Related projects

No related projects are listed in the registry.
