You can deploy this model and start earning money today!

Litzy619/
V0414H4

Name: V0414H4
Availability: InStock

safetensors

generated_from_trainer

microsoft/phi-2

finetune

mit

Views

No views yet

V0414H4

This model is a fine-tuned version of microsoft/phi-2 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.0509

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.003
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine_with_restarts
lr_scheduler_warmup_steps: 100
num_epochs: 4
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
2.2233	0.05	10	1.3485
0.7462	0.09	20	0.1747
0.1543	0.14	30	0.1288
0.1245	0.18	40	0.1069
0.1035	0.23	50	0.0897
0.0977	0.27	60	0.0858
0.0917	0.32	70	0.0843
0.1014	0.36	80	0.0814
0.0817	0.41	90	0.0802
0.0879	0.45	100	0.0743
0.0858	0.5	110	0.0767
0.088	0.54	120	0.0740
0.0846	0.59	130	0.0735
0.0891	0.63	140	0.0766
0.0862	0.68	150	0.0794
0.0813	0.73	160	0.0842
0.0859	0.77	170	0.0706
0.0806	0.82	180	0.0753
0.092	0.86	190	0.0716
0.0727	0.91	200	0.0709
0.1142	0.95	210	0.0756
0.0861	1.0	220	0.0733
0.0673	1.04	230	0.0680
0.0599	1.09	240	0.0643
0.1244	1.13	250	0.0694
0.0724	1.18	260	0.0726
0.0712	1.22	270	0.0596
0.0544	1.27	280	0.0706
0.081	1.31	290	0.0648
0.0931	1.36	300	0.0632
0.0736	1.41	310	0.0566
0.0631	1.45	320	0.0566
0.7605	1.5	330	0.7501
0.1829	1.54	340	0.0805
0.0928	1.59	350	0.0756
0.4824	1.63	360	0.1228
0.0929	1.68	370	0.0644
0.0735	1.72	380	0.0858
0.0856	1.77	390	0.0622
0.0689	1.81	400	0.0668
0.0674	1.86	410	0.0658
0.0694	1.9	420	0.0648
0.0729	1.95	430	0.0670
0.0756	1.99	440	0.0759
0.0695	2.04	450	0.0648
0.0617	2.08	460	0.0557
0.0617	2.13	470	0.0591
0.0588	2.18	480	0.0604
0.0549	2.22	490	0.0582
0.0494	2.27	500	0.0672
0.0675	2.31	510	0.0673
0.1043	2.36	520	0.0938
0.0762	2.4	530	0.0614
0.0661	2.45	540	0.0593
0.0619	2.49	550	0.0561
0.0607	2.54	560	0.0531
0.0522	2.58	570	0.0538
0.0728	2.63	580	0.0539
0.0472	2.67	590	0.0540
0.0522	2.72	600	0.0519
0.0507	2.76	610	0.0479
0.0518	2.81	620	0.0488
0.0487	2.86	630	0.0498
0.0505	2.9	640	0.0532
0.0445	2.95	650	0.0508
0.0455	2.99	660	0.0525
0.0459	3.04	670	0.0529
0.04	3.08	680	0.0527
0.035	3.13	690	0.0524
0.0556	3.17	700	0.0516
0.0354	3.22	710	0.0513
0.038	3.26	720	0.0508
0.0348	3.31	730	0.0530
0.0358	3.35	740	0.0538
0.0434	3.4	750	0.0542
0.0443	3.44	760	0.0520
0.0417	3.49	770	0.0509
0.0437	3.54	780	0.0502
0.0384	3.58	790	0.0510
0.0388	3.63	800	0.0510
0.0341	3.67	810	0.0506
0.0397	3.72	820	0.0509
0.0353	3.76	830	0.0507
0.0364	3.81	840	0.0508
0.0381	3.85	850	0.0508
0.0268	3.9	860	0.0509
0.0364	3.94	870	0.0509
0.044	3.99	880	0.0509

Framework versions

Transformers 4.36.0.dev0
Pytorch 2.1.2+cu121
Datasets 2.14.6
Tokenizers 0.14.1

Litzy619/V0414H4

V0414H4

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Litzy619/
V0414H4