You can deploy this model and start earning money today!

fliarbi/
phi-2-hummanize1

Name: phi-2-hummanize1
Availability: InStock

peft

safetensors

generated_from_trainer

microsoft/phi-2

adapter

mit

Views

No views yet

phi-2-hummanize1

This model is a fine-tuned version of microsoft/phi-2 on the None dataset. It achieves the following results on the evaluation set:

Loss: 6.7074

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: 0.0025
train_batch_size: 20
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 20
total_train_batch_size: 400
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 2
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.02	1	2.0864
No log	0.05	2	4.0671
No log	0.07	3	3.6332
No log	0.09	4	2.5537
3.0197	0.11	5	2.3394
3.0197	0.14	6	2.8862
3.0197	0.16	7	2.5140
3.0197	0.18	8	2.4603
3.0197	0.21	9	2.2094
2.5958	0.23	10	2.1767
2.5958	0.25	11	2.3343
2.5958	0.28	12	2.2511
2.5958	0.3	13	2.1854
2.5958	0.32	14	2.1385
2.2944	0.34	15	2.3556
2.2944	0.37	16	2.2056
2.2944	0.39	17	2.2127
2.2944	0.41	18	2.1507
2.2944	0.44	19	2.1388
2.2841	0.46	20	2.6540
2.2841	0.48	21	2.8934
2.2841	0.51	22	3.0981
2.2841	0.53	23	2.4155
2.2841	0.55	24	2.1754
2.7585	0.57	25	2.0927
2.7585	0.6	26	2.0865
2.7585	0.62	27	2.2345
2.7585	0.64	28	2.4123
2.7585	0.67	29	2.7718
2.3906	0.69	30	4.2964
2.3906	0.71	31	6.5295
2.3906	0.73	32	5.8489
2.3906	0.76	33	7.2467
2.3906	0.78	34	7.6353
6.5839	0.8	35	7.7842
6.5839	0.83	36	8.8627
6.5839	0.85	37	7.9511
6.5839	0.87	38	9.7736
6.5839	0.9	39	8.3666
8.6795	0.92	40	8.9768
8.6795	0.94	41	9.0808
8.6795	0.96	42	8.5933
8.6795	0.99	43	8.9317
8.6795	1.01	44	8.5291
8.8177	1.03	45	8.5935
8.8177	1.06	46	8.6773
8.8177	1.08	47	8.5914
8.8177	1.1	48	8.5006
8.8177	1.13	49	8.3959
8.6883	1.15	50	8.2375
8.6883	1.17	51	8.2022
8.6883	1.19	52	8.2063
8.6883	1.22	53	8.2254
8.6883	1.24	54	8.3408
8.4216	1.26	55	8.0367
8.4216	1.29	56	7.8776
8.4216	1.31	57	7.6720
8.4216	1.33	58	7.5050
8.4216	1.35	59	7.3863
7.8151	1.38	60	7.3775
7.8151	1.4	61	7.3820
7.8151	1.42	62	7.2597
7.8151	1.45	63	7.1959
7.8151	1.47	64	7.1233
7.3639	1.49	65	7.0625
7.3639	1.52	66	7.0302
7.3639	1.54	67	6.9862
7.3639	1.56	68	6.9601
7.3639	1.58	69	6.9606
7.1152	1.61	70	6.8977
7.1152	1.63	71	6.8981
7.1152	1.65	72	6.8453
7.1152	1.68	73	6.8523
7.1152	1.7	74	6.8641
6.9712	1.72	75	6.8261
6.9712	1.75	76	6.8273
6.9712	1.77	77	6.8053
6.9712	1.79	78	6.7712
6.9712	1.81	79	6.7542
6.8925	1.84	80	6.7466
6.8925	1.86	81	6.7341
6.8925	1.88	82	6.7255
6.8925	1.91	83	6.7211
6.8925	1.93	84	6.7154
6.8192	1.95	85	6.7103
6.8192	1.97	86	6.7074

Framework versions

PEFT 0.8.2
Transformers 4.38.0.dev0
Pytorch 2.1.0+cu118
Datasets 2.17.0
Tokenizers 0.15.2

fliarbi/phi-2-hummanize1

phi-2-hummanize1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

fliarbi/
phi-2-hummanize1