metadata

license: mit
datasets:
  - mstafam/Kilter-Board-Dataset
pipeline_tag: text2text-generation

GenClimb: AI-Generated Climbing Routes for Interactive Training Boards

GenClimb is a generative AI model designed to create climbing routes for Standardized Interactive Climbing Training Boards (SICTBs). It a seq2seq transformer architecture, GenClimb generates climbs based on board layouts and climb difficulties.

Model Details

Architecture

Dimension: 512
Attention Heads: 4
Layers: 5
Feed-Forward Dimension: 1024
Dropout Rate: 0.15
Activation Function: GELU
Layer Normalization Epsilon: 1e-5

Training Configuration

Device: CUDA (1x NVIDIA GeForce RTX 3070)
Learning Rate: 1e-4
Epochs: 8
Weight Decay: 0.0125
Batch Size: 32
Train/Test Split: 90/10

Performance Metrics

Training Time: 12 hours and 6 minutes
Final Loss: 2.114803

Dataset

The model is trained on the Kilter-Board-Dataset, a comprehensive collection of climbing routes curated with the help of the BoardLib utility by lemeryfertitta.