learning_rate: 5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 64
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 5
num_epochs: 3

Training Loss	Epoch	Step	Validation Loss
1.0787	0.1975	40	0.9601
0.9313	0.3951	80	0.9440
0.8649	0.5926	120	0.8996
0.8648	0.7901	160	0.8341
0.8421	0.9877	200	0.8553
0.7576	1.1827	240	0.8185
0.7483	1.3802	280	0.8028
0.7361	1.5778	320	0.7917
0.7264	1.7753	360	0.7943
0.7602	1.9728	400	0.7573
0.6597	2.1679	440	0.7896
0.6092	2.3654	480	0.7708
0.6076	2.5630	520	0.7760
0.5964	2.7605	560	0.7813
0.6047	2.9580	600	0.7781

aysangh/medsiglip-448-vindr-lp

medsiglip-448-vindr-lp