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sweep.sh
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sweep.sh
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#!/bin/sh
for PER in 0.00001
do
python train_simple.py \
--model_size=gpt2-large \
--ds_name=simple_hierarchical_equivalence \
--batch_size=32 \
--lr=1e-05 \
--epochs=3 \
--sweep_subfolder=random_init_average \
--loss=xent \
--model_ckpt=WTS-$PER \
--use_validation=False \
--random_init=$PER \
--num_trials=10 \
--weak_labels_path=/data/tejess/weak-to-strong/results/random_init_average/ac=0.0-bs=32-dn=simp_hier_equi-e=3-l=1e-05-mc=gpt2-$PER/weak_labels \
done
#--strong_ckpt_path=/data/tejess/weak-to-strong/results/hierarchical_equivalencek5/ac=0.0-bs=32-dn=simp_hier_equi-e=4-l=1e-05-mc=gpt2-large/model.safetensors \
#--just_evaluate=True \
#--weak_labels_path=/data/tejess/weak-to-strong/results/hierarchical_equivalence/ac=0.0-bs=32-dn=simp_hier_equi-e=2-l=1e-05-mc=gpt2/weak_labels \