#!/bin/bash # Define the parameters model_name="Salesforce/blip-image-captioning-base" tok_name="Salesforce/blip-image-captioning-base" batch_size=16 max_tokens=5000000000000 sae_name="batchtopk" lr=1e-4 expansion_factor=8 k=8 auxk=128 auxk_coef=0.03125 device_id=1 max_epochs=3 dead_tokens_threshold=1000000 log_every_n_steps=100 save_every_n_training_steps=100 save_step=false use_loss_var=true num_workers=4 hf_dataset="pixparse/cc3m-wds" dtype="float16" # change local_val_path local_train_path="CC3M-Dataset/preproc/CC3M-Dataset/cc3m_images/train" local_val_path="CC3M-Dataset/preproc/CC3M-Dataset/cc3m_images/val" wandb_key=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 cd .. # Loop over 12 layers for layer in {10..10} do # hook names: vision_model.encoder.layers.${layer}.hook_resid_pre, # text_decoder.bert.encoder.layer.${layer}.attention.self.hook_resid_pre, # text_decoder.bert.encoder.layer.${layer}.crossattention.self.hook_resid_pre, hook_name="vision_model.encoder.layers.${layer}.hook_resid_post" wandb login --relogin $wandb_key wandb login python Train_SAE_Blip.py \ --model_name "$model_name" \ --tok_name "$tok_name" \ --layer "$layer" \ --batch_size "$batch_size" \ --hook_name "$hook_name" \ --sae_name "$sae_name" \ --lr "$lr" \ --expansion_factor "$expansion_factor" \ --k "$k" \ --auxk "$auxk" \ --max_epochs "$max_epochs" \ --dead_tokens_threshold "$dead_tokens_threshold" \ --log_every_n_steps "$log_every_n_steps" \ --save_every_n_training_steps "$save_every_n_training_steps" \ --save_step "$save_step" \ --use_loss_var "$use_loss_var" \ --max_tokens "$max_tokens" \ --auxk_coef "$auxk_coef" \ --num_workers "$num_workers" \ --hf_dataset "$hf_dataset" \ --local_train_path "$local_train_path" \ --local_val_path "$local_val_path" \ --device_id "$device_id" \ --dtype "$dtype" \ done