apertus-cpt-sft-feedback API & Inference Endpoint

More information needed

More information needed

More information needed

The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 2
eval_batch_size: 2
seed: 42
distributed_type: multi-GPU
num_devices: 7
total_train_batch_size: 14
total_eval_batch_size: 14
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.05
num_epochs: 1.0

Table with columns: Training Loss, Epoch, Step, Validation Loss
Training Loss	Epoch	Step	Validation Loss
0.0609	0.0777	1000	0.0622
0.0428	0.1553	2000	0.0455
0.04	0.2330	3000	0.0402
0.0349	0.3107	4000	0.0371
0.0384

More information needed

More information needed

More information needed

The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 2
eval_batch_size: 2
seed: 42
distributed_type: multi-GPU
num_devices: 7
total_train_batch_size: 14
total_eval_batch_size: 14
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.05
num_epochs: 1.0

Table with columns: Training Loss, Epoch, Step, Validation Loss
Training Loss	Epoch	Step	Validation Loss
0.0609	0.0777	1000	0.0622
0.0428	0.1553	2000	0.0455
0.04	0.2330	3000	0.0402
0.0349	0.3107	4000	0.0371
0.0384

apertus-cpt-sft-feedback