triprorep-3B / config.yaml
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model:
name: "ELECTRAProteinModel"
max_residues: 512
embed_dim: 2560
encoder_depth: 33
encoder_heads: 40
decoder_dim: 2560
decoder_depth: 3
decoder_heads: 40
generator_depth: 8
generator_decoder_depth: 2
disc_loss_weight: 50.0 # corrective CE same scale as generator CE
generator_lr_multiplier: 1.0
generator_temperature: 2.0
relax_temperature_scaling: null # null = 1/sqrt(head_dim), relaxes attention temperature
use_qk_norm: false
# Training configuration
training:
learning_rate: 1.0e-3
weight_decay: 0.01
adam_beta1: 0.9
adam_beta2: 0.95
warmup_steps: 5000
batch_size: 32
num_workers: 8
max_steps: 500000
total_training_steps: 500000
accumulate_grad_batches: 1
val_check_interval: 100
test_interval: 100
gradient_clip_val: 1.0
# Data configuration
data:
name: "ELECTRADataModule"
lmdb_dir: "/path/to/triprorep-pretrain"
struct_format: "fullatom"
max_length: 512
bb_vocab_size: 512
fa_vocab_size: 512
mask_prob: 0.6
random_cropping: true
pack_sequences: false
# Lightning configuration
lightning:
gpus: 8
nodes: 4
precision: "bf16-mixed"
compile: true
checkpoint_dir: "./checkpoints"
log_dir: "./logs"
wandb_dir: "./wandb"
wandb_project: "k-fold-structure"
wandb_name: "triprorep_3B"
save_every_n_steps: 10000