distilgpt2-finetuned-wellness – AI Model by Binodt | AlphaNeural AI

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Binodt/
distilgpt2-finetuned-wellness

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text-generation

generated_from_trainer

distilbert/distilgpt2

autotrain_compatible

text-generation-inference

endpoints_compatible

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distilgpt2-finetuned-wellness

This model is a fine-tuned version of distilgpt2 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 1.6526

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: 3e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100
num_epochs: 20
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
3.0101	1.0	158	2.1220
2.0886	2.0	316	1.9541
1.9941	3.0	474	1.8785
1.8701	4.0	632	1.8243
1.8238	5.0	790	1.7894
1.743	6.0	948	1.7593
1.699	7.0	1106	1.7376
1.6606	8.0	1264	1.7188
1.6345	9.0	1422	1.7027
1.6208	10.0	1580	1.6914
1.5896	11.0	1738	1.6830
1.5694	12.0	1896	1.6722
1.5468	13.0	2054	1.6671
1.5311	14.0	2212	1.6663
1.5172	15.0	2370	1.6602
1.51	16.0	2528	1.6544
1.4916	17.0	2686	1.6555
1.4782	18.0	2844	1.6529
1.4829	19.0	3002	1.6532
1.4639	20.0	3160	1.6526

Framework versions

Transformers 4.45.1
Pytorch 2.4.1+cu124
Datasets 3.0.1
Tokenizers 0.20.0