tfm_alive_fine-tuning.yaml

# Model configuration
model:
  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

  # Alive prediction head
  alive_hidden_dim: 512  # Hidden dimension for alive prediction head
  alive_hidden_layers: 1     # Number of hidden layers in alive prediction head

# Data configuration
data:
  # Data dimensions (must match model)
  ticks_per_sample: 64  # Number of ticks in each training sample
  seq_len: 512          # Must match model.seq_len
  pad_token: 978     # Token ID used for padding sequences

# Device configuration
device: 'cuda:1'  # 'cuda' or 'cpu'

# wandb logging configuration
logging:
  project_name: 'tick-transformer-alive-fine-tuning'
  test: 2048