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HRM-Sudoku-S1K
ZoneTwelve/HRM-Sudoku-S1K
HRM-Sudoku-S1K is a machine learning model from ZoneTwelve. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
The model is configured using HRMACTModelConfig, designed for hierarchical recurrent memory with adaptive computation. Key architectural parameters:
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At a glance
- License
- apache-2.0
- Model type
- hrmact
- Access
- Public
- Created
- Oct 13, 2025
- Updated
- Oct 13, 2025
- SHA
- ef8f71cb
Model card
From the Hugging Face model README
🧩 Model Architecture Summary
The model is configured using HRMACTModelConfig, designed for hierarchical recurrent memory with adaptive computation. Key architectural parameters:
- Sequence length: 81
- Vocabulary size: 10
- High-level cycles: 1
- Low-level cycles: 2
Transformer Configuration:
- Layers: 16
- Hidden size: 256
- Attention heads: 4
- Feed-forward expansion ratio: 4
Adaptive Computation Time (ACT):
- Maximum halting steps: 16
- Exploration probability: 0.1
🏋️ Training details
Total training time: 1 hour 16 minutes (76 minutes total)
GPU: x1 NVIDIA GeForce RTX 5080
Sample data number: [Easy, Medium, Hard, Extreme] * 1000 (4,000 total)
Sample data file: pregenerated_puzzles.npy
⚠️ Warning: Possible Data Limitation This training run may have been conducted with insufficient data. As a result, the model could be overfitting, which means it might perform well on the training data but poorly on unseen or real-world data. Please review the dataset size and diversity before deploying or relying on these results.
- Type
- hrmact
- License
- apache-2.0
- Created
- Oct 13, 2025
- Updated
- Oct 13, 2025