src/configs/guidance.yaml

seed: 42
base_dir: /scratch/sgoel/MeMDLM_v2


lm:
  pretrained_esm: facebook/esm2_t33_650M_UR50D
  pretrained_evoflow: fredzzp/EvoFlow-650M-context-3070
  pretrained_dplm: airkingbd/dplm_650m
  ft_evoflow: ft_eflow-3070-650M_steps=50k_layers=3_lr=0.00004_wd=.01_polynom_pwr=1_betas=.9-.98_bsz=8_gclip=1.0
  ft_dplm: ft_dplm-650M_steps=5k_layers=3_lr=0.00004_wd=.01_polynom_pwr=1_betas=.9-.98_bsz=32_gclip=1.0

model:
  d_model: 1280 
  num_heads: 2
  dropout: 0.5
  num_layers: 4
  label_pad_value: -100

optim:
  type: adamw
  lr: 3e-5
  lr_end: 1e-5
  weight_decay: 0.01
  beta1: 0.9
  beta2: 0.98
  power: 1


training:
  mode: test  # train / test
  n_layers: 4
  max_steps: 3000
  warmup_steps: 150
  log_every_n_steps: 10
  num_sanity_val_steps: 2
  val_check_interval: 250
  enable_progress_bar: true
  grad_clip_val: 1.0
  devices: [0]  # list of GPU IDs from 0-7

guidance:
  n_steps: 128
  alpha: 3
  gamma: 0.3
  saliency_eps: 1e-4
  saliency_t: 2.0
  sampling_t: 0.7
  boltzmann_t: 0.3
  top_p: 0.2
  steps: 128
  prior: lm_probs  # lm_probs / boltzmann

data:
  batch_size: 32
  max_seq_len: 1024
  train: ${base_dir}/data/classifier/train.csv
  test: ${base_dir}/data/classifier/test.csv
  val: ${base_dir}/data/classifier/val.csv


wandb:
  project: memdlm_guidance
  group: programmablebio
  name: new_data_cleaned_steps3k_lr3e-5_bsz32_heads2_drpt0.5_layers4
  id: ${.name}_${seed}


checkpointing:
  save_every_n_steps: 250
  save_dir: ${base_dir}/checkpoints/${wandb.name}
  resume_ckpt_path: ${checkpointing.save_dir}/last.ckpt
  best_ckpt_path: ${checkpointing.save_dir}/best_model.ckpt