Qwen3-1.7B-cpt-tesi_all_all_tasks API & Inference Endpoint

More information needed

More information needed

More information needed

The following hyperparameters were used during training:

learning_rate: 0.0003
train_batch_size: 64
eval_batch_size: 32
seed: 42
distributed_type: multi-GPU
gradient_accumulation_steps: 2
total_train_batch_size: 128
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-07 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 10

Table with columns: Training Loss, Epoch, Step, Validation Loss
Training Loss	Epoch	Step	Validation Loss
0.2565	1.0	581	0.2849
0.1856	2.0	1162	0.1738
0.1397	3.0	1743	0.1858
0.1204	4.0	2324	0.1544
0.1243	5.0	2905	0.1489
0.0949	6.0	3486	0.1465
0.0556	7.0	4067	0.1811
0.0535	8.0	4648	0.1942
0.009	9.0	5229	0.2420
0.0064	10.0	5810	0.2711

More information needed

More information needed

More information needed

The following hyperparameters were used during training:

learning_rate: 0.0003
train_batch_size: 64
eval_batch_size: 32
seed: 42
distributed_type: multi-GPU
gradient_accumulation_steps: 2
total_train_batch_size: 128
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-07 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 10

Table with columns: Training Loss, Epoch, Step, Validation Loss
Training Loss	Epoch	Step	Validation Loss
0.2565	1.0	581	0.2849
0.1856	2.0	1162	0.1738
0.1397	3.0	1743	0.1858
0.1204	4.0	2324	0.1544
0.1243	5.0	2905	0.1489
0.0949	6.0	3486	0.1465
0.0556	7.0	4067	0.1811
0.0535	8.0	4648	0.1942
0.009	9.0	5229	0.2420
0.0064	10.0	5810	0.2711

Qwen3-1.7B-cpt-tesi_all_all_tasks