gpt_1_causual_finetune
This model is a fine-tuned version of openai-gpt on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6698
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: 0.0002511643502833988
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 25
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.7166 | 1.0 | 12 | 1.0911 |
| 0.9172 | 2.0 | 24 | 0.8221 |
| 0.6982 | 3.0 | 36 | 0.7172 |
| 0.5758 | 4.0 | 48 | 0.6670 |
| 0.504 | 5.0 | 60 | 0.6512 |
| 0.4566 | 6.0 | 72 | 0.6512 |
| 0.4187 | 7.0 | 84 | 0.6503 |
| 0.3829 | 8.0 | 96 | 0.6641 |
| 0.3589 | 9.0 | 108 | 0.6698 |
Framework versions
- Transformers 4.49.0
- Pytorch 2.6.0
- Datasets 3.4.0
- Tokenizers 0.21.1
- Downloads last month
- 7
Inference Providers
NEW
This model isn't deployed by any Inference Provider.
🙋
Ask for provider support
Model tree for jacob-danner/gpt_1_causual_finetune
Base model
openai-community/openai-gpt