phi-2-coedit – AI Model by iliazlobin | AlphaNeural AI

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iliazlobin/
phi-2-coedit

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text-generation

generated_from_trainer

microsoft/phi-2

autotrain_compatible

text-generation-inference

endpoints_compatible

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phi-2-coedit

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: 0.7388
Rouge1: 0.5206
Rouge2: 0.4123
Rougel: 0.4979
Rougelsum: 0.5032
Sacreblue: 28.1346
Memory Used: 81917.5
Cuda Allocated: 10795.7861
Cuda Reserved: 74746.0
Ram Usage: 24042.6719
Em: 0.0
Gen Len: 120.6545

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: 2e-05
train_batch_size: 35
eval_batch_size: 35
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 140
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 1
num_epochs: 2
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Sacreblue	Memory Used	Cuda Allocated	Cuda Reserved	Ram Usage	Em	Gen Len
0.5716	0.22	100	0.7558	0.5041	0.3927	0.4809	0.4853	26.9798	81917.5	10795.811	74738.0	22888.4102	0.0	120.3347
0.5407	0.44	200	0.7404	0.5241	0.4171	0.5013	0.5068	27.6806	81917.5	10795.814	74738.0	23733.9805	0.0	120.8277
0.5324	0.66	300	0.7230	0.5176	0.4093	0.4947	0.5002	27.5145	81917.5	10795.8184	74738.0	23831.1484	0.0	120.576
0.5107	0.88	400	0.7161	0.5256	0.4167	0.5042	0.5092	28.1274	81917.5	10795.7935	74738.0	23891.7891	0.0	120.5225
0.4374	1.1	500	0.7495	0.5237	0.414	0.501	0.5059	28.0405	81917.5	10795.7861	74746.0	23922.043	0.0	120.3181
0.3515	1.32	600	0.7418	0.5216	0.4133	0.499	0.5049	28.0528	81917.5	10795.7832	74746.0	23973.8164	0.0	120.6453
0.3449	1.54	700	0.7386	0.5242	0.4163	0.5016	0.5075	28.3145	81917.5	10795.8066	74746.0	23950.1016	0.0	120.5367
0.3375	1.76	800	0.7354	0.5194	0.4124	0.4973	0.5025	28.0252	81917.5	10795.814	74746.0	23931.0	0.0	120.6476
0.3373	1.98	900	0.7388	0.5206	0.4123	0.4979	0.5032	28.1346	81917.5	10795.7861	74746.0	24042.6719	0.0	120.6545

Framework versions

Transformers 4.39.3
Pytorch 2.2.2
Datasets 2.18.0
Tokenizers 0.15.2