model2_win.yaml

# Model configuration
model:
  model_name: "model2"  # Name of the model architecture to use

  vocab_size: 979  # Vocabulary size for token embeddings
  embed_dim: 640     # Embedding dimension
  num_heads: 10     # Number of attention heads
  dropout: 0.1       # Dropout rate
  
  tick_num_layers: 8
  temporal_num_layers: 8

  pad_token_id: 978

  num_cond: 0
  cond_vocab_size: 0
  n_logits: 1 # Number of logits to predict (e.g., 1 for win rate prediction)

  pretrained_path: 'checkpoints_pretraining_v2/final.pth'

pretrain:
  model_name: "TickTransformerModelROPE"

  vocab_size: 979  # Vocabulary size for token embeddings
  embed_dim: 640     # Embedding dimension
  seq_len: 512       # Sequence length per tick
  dropout: 0.1       # Dropout rate
  
  # Embedder (non-causal transformer encoder)
  embedder_heads: 10
  embedder_layers: 6
  
  # Processor (GPT-style causal transformer for next token prediction)
  processor_heads: 10
  processor_layers: 8
  
  # Decoder (non-causal transformer to decode embeddings to sequences)
  decoder_heads: 10
  decoder_layers: 6

data:
  tick_seq_len: 512
  temporal_seq_len: 32
  num_workers: 4

training:
  batch_size: 32
  grad_accum_steps: 1
  lr: 0.00012
  weight_decay: 0.05
  num_epochs: 22
  warmup_steps: 4500
  max_grad_norm: 1.0
  
  checkpoint_dir: 'model2_win_ckpts'

logging:
  project_name: 'model2_win'
  test: 1024