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SHELL := /bin/bash
train_text8:
python3 main.py \
model=small \
data=text8 \
parameterization=new_diff \
backbone=dit_bfn \
model.length=256 \
eval.compute_generative_perplexity=False \
wandb.name=tiny_text8_512 \
sampling.steps=1000 \
trainer.val_check_interval=347 \
training.beta_bfn=0.75 \
checkpointing.resume_from_ckpt=False \
T=1000 \
loader.global_batch_size=128 \
eval.new_diff_calculate=full \
trainer.devices=1 \
train_uniref50:
python3 main.py \
model=evodiff \
data=uniref50 \
parameterization=new_diff \
backbone=dit_bfn \
model.length=1024 \
sampling.length=400 \
eval.compute_generative_perplexity=False \
wandb.name=tiny_uniref_evodiff \
sampling.steps=500 \
trainer.val_check_interval=500 \
training.beta_bfn=0.75 \
checkpointing.resume_from_ckpt=False \
T=500 \
loader.global_batch_size=128 \
eval.new_diff_calculate=full \
trainer.devices=1; \
sample_uniref50:
$(eval CKPT_PATH:=/AIRvePFS/ai4science/users/yupei/test_slm_repo/ckpt/Uniref_protein.ckpt)
$(eval OUTPUT_DIR:=/AIRvePFS/ai4science/users/yupei/test_slm_repo/output/output_protein)
for sample_len in 100 200 300 400 500; do \
python3 main.py \
model=evodiff \
data=uniref50 \
mode=sample_eval \
parameterization=new_diff \
backbone=dit_bfn \
model.length=1024 \
sampling.length=$$sample_len \
sampling.outdir=${OUTPUT_DIR} \
eval.compute_generative_perplexity=False \
wandb.name=sample_uniref_evodiff \
sampling.steps=500 \
trainer.val_check_interval=500 \
training.beta_bfn=0.75 \
checkpointing.resume_from_ckpt=True \
T=500 \
loader.global_batch_size=100 \
eval.new_diff_calculate=full \
eval.checkpoint_path=${CKPT_PATH} \
trainer.devices=1; \
done
train_promoter:
python main.py \
model=small \
data=promoter \
parameterization=new_diff \
backbone=promoter \
model.length=1024 \
eval.compute_generative_perplexity=False \
sampling.steps=100 \
training.different_time=True \
training.onehot_sparse=True \
checkpointing.resume_from_ckpt=False \
T=1000 \
trainer.val_check_interval=100 \
loader.global_batch_size=128 \
trainer.devices=1; \
train_fb:
python main.py \
model=small \
data=FB \
parameterization=new_diff \
backbone=FB \
model.length=500 \
eval.compute_generative_perplexity=False \
sampling.steps=100 \
training.different_time=True \
training.onehot_sparse=True \
checkpointing.resume_from_ckpt=False \
T=1000 \
loader.global_batch_size=128 \
trainer.val_check_interval=500 \
gamma=0 \
trainer.devices=1; \
sample_fb:
$(eval CKPT_PATH:=/AIRvePFS/ai4science/users/yupei/test_slm_repo/ckpt/FB_best.ckpt)
$(eval OUTPUT_DIR:=/AIRvePFS/ai4science/users/yupei/test_slm_repo/output/output_fb)
python main.py \
model=small \
data=FB \
parameterization=new_diff \
backbone=FB \
model.length=500 \
eval.compute_generative_perplexity=False \
sampling.steps=200 \
sampling.outdir=${OUTPUT_DIR} \
loader.global_batch_size=32 \
training.different_time=True \
training.onehot_sparse=True \
mode=eval \
eval.checkpoint_path=${CKPT_PATH} \
gamma=2.7 \
trainer.devices=1; \
train_mel:
python main.py \
model=small \
data=Mel \
parameterization=new_diff \
backbone=Mel \
model.length=500 \
eval.compute_generative_perplexity=False \
sampling.steps=100 \
training.different_time=True \
training.onehot_sparse=True \
checkpointing.resume_from_ckpt=False \
T=1000 \
loader.global_batch_size=128 \
trainer.val_check_interval=100 \
gamma=0 \
trainer.devices=1; \
sample_mel:
$(eval CKPT_PATH:=/AIRvePFS/ai4science/users/yupei/test_slm_repo/ckpt/Mel_best.ckpt)
python main.py model=small \
data=Mel \
parameterization=new_diff \
backbone=Mel \
model.length=500 \
eval.compute_generative_perplexity=False \
sampling.steps=200 \
training.different_time=True \
training.onehot_sparse=True \
mode=eval \
eval.checkpoint_path=${CKPT_PATH} \
gamma=3.3; \