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license: mit
task_categories:
- other
tags:
- maze
- world-model
- sequence-modeling
- transformer
- mamba
pretty_name: World Model For Maze
---
# WorldModelForMaze
Code, datasets, and trained checkpoints for studying world-model representations in maze navigation, based on a modified NanoGPT.
## Contents
- `*.py` — training, testing, probing, and visualization scripts (see `readme.md`).
- `model/` — architectures: transformer, transformer-rope, transformer-nextlat, mamba, mamba2, gated-deltanet, gru.
- `data/maze/100/` — tokenized maze datasets for Tasks A/C/E/H/I (RWs paths, 100 nodes).
- `out/` — final (10000-iter) checkpoints, `maze_kdetour` results, and plots.
> Intermediate (non-10000-iter) checkpoints (`out2/`) are not included here.
## Usage
See `readme.md` for full instructions on data generation, training, testing, probing, and the k-step detour analysis.
Quick start:
```bash
git clone https://huggingface.co/datasets/Kalso42/WorldModelForMaze
cd WorldModelForMaze
python train_maze.py --tasks C1 --path_type RWs --num_train_dataset 10M --config 6_6_384
```
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