
#### metric random vocab
python3 train.py --data-mode unlearn --file-path data/polarity/metric-random-vocab-m10-s0/t_0.05_npt_10_l_2/ --save-dir outputs/polarity/metric-random-vocab-m10-s0/t_0.05_npt_10_l_2/Meta-Llama-3.1-8B-bnb-4bit/ --model-name Meta-Llama-3.1-8B-bnb-4bit

python3 train.py --data-mode unlearn --file-path data/polarity/metric-random-vocab-m10-s42/t_0.05_npt_10_l_2/ --save-dir outputs/polarity/metric-random-vocab-m10-s42/t_0.05_npt_10_l_2/Meta-Llama-3.1-8B-bnb-4bit/ --model-name Meta-Llama-3.1-8B-bnb-4bit

python3 train.py --data-mode unlearn --file-path data/polarity/metric-random-vocab-m10-s1/t_0.05_npt_10_l_2/ --save-dir outputs/polarity/metric-random-vocab-m10-s1/t_0.05_npt_10_l_2/Meta-Llama-3.1-8B-bnb-4bit/ --model-name Meta-Llama-3.1-8B-bnb-4bit


#### impact of max_poisons - 5, 15, 20 (do later)


#### impact of npt - 5, 50, 100 (do later)
