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correct_pretraining – AI Model by danielkosyra | AlphaNeural AI
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correct_pretraining
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transformers
safetensors
gpt2
text-generation
generated_from_trainer
openai-community/gpt2
finetune
mit
autotrain_compatible
text-generation-inference
endpoints_compatible
us
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correct_pretraining
This model is a fine-tuned version of
gpt2
on the None dataset. It achieves the following results on the evaluation set:
Loss: 3.4416
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: 0.0006
train_batch_size: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 10
total_train_batch_size: 320
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 250
training_steps: 1250
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
6.6027
0.4093
250
5.1478
4.6105
0.8186
500
4.1169
3.892
1.2279
750
3.7216
3.6007
1.6372
1000
3.5301
3.4432
2.0465
1250
3.4416
Framework versions
Transformers 4.40.1
Pytorch 2.3.0+cu121
Datasets 2.19.0
Tokenizers 0.19.1
