pii-ner-phi4-14b-lora-canonical API & Inference Endpoint

More information needed

More information needed

More information needed

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 4
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 16
optimizer: Use paged_adamw_8bit 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: 0.03
num_epochs: 1.0

Table with columns: Training Loss, Epoch, Step, Validation Loss
Training Loss	Epoch	Step	Validation Loss
0.1314	0.2667	500	0.1361
0.1022	0.5335	1000	0.1014
0.0903	0.8002	1500	0.0832

More information needed

More information needed

More information needed

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 4
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 16
optimizer: Use paged_adamw_8bit 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: 0.03
num_epochs: 1.0

Table with columns: Training Loss, Epoch, Step, Validation Loss
Training Loss	Epoch	Step	Validation Loss
0.1314	0.2667	500	0.1361
0.1022	0.5335	1000	0.1014
0.0903	0.8002	1500	0.0832

pii-ner-phi4-14b-lora-canonical