metadata
library_name: transformers
license: mit
base_model: gpt2
tags:
- generated_from_trainer
model-index:
- name: codeparrot-ds-small
results: []
codeparrot-ds-small
This model is a fine-tuned version of gpt2 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 3.2919
Model description
Due to hardware limitations and an insufficient amount of training data, the model has only been trained for a single epoch and is currently not functional.
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: 0.0005
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- 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: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 1
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 5.9286 | 0.02 | 500 | 5.7197 |
| 5.2717 | 0.04 | 1000 | 5.1488 |
| 4.9972 | 0.06 | 1500 | 4.8345 |
| 4.8214 | 0.08 | 2000 | 4.6251 |
| 4.6272 | 0.1 | 2500 | 4.4807 |
| 4.5241 | 0.12 | 3000 | 4.3484 |
| 4.405 | 0.14 | 3500 | 4.2483 |
| 4.3189 | 0.16 | 4000 | 4.1680 |
| 4.2596 | 0.18 | 4500 | 4.0914 |
| 4.2569 | 0.2 | 5000 | 4.0341 |
| 4.1614 | 0.22 | 5500 | 3.9615 |
| 4.1073 | 0.24 | 6000 | 3.9112 |
| 4.0892 | 0.26 | 6500 | 3.8685 |
| 4.0151 | 0.28 | 7000 | 3.8277 |
| 3.903 | 0.3 | 7500 | 3.7787 |
| 3.9248 | 0.32 | 8000 | 3.7447 |
| 3.8978 | 0.34 | 8500 | 3.7189 |
| 3.9231 | 0.36 | 9000 | 3.6877 |
| 3.8936 | 0.38 | 9500 | 3.6479 |
| 3.7649 | 0.4 | 10000 | 3.6154 |
| 3.8156 | 0.42 | 10500 | 3.6069 |
| 3.7588 | 0.44 | 11000 | 3.5772 |
| 3.7559 | 0.46 | 11500 | 3.5517 |
| 3.814 | 0.48 | 12000 | 3.5230 |
| 3.7384 | 0.5 | 12500 | 3.5065 |
| 3.6827 | 0.52 | 13000 | 3.4807 |
| 3.6679 | 0.54 | 13500 | 3.4585 |
| 3.6838 | 0.56 | 14000 | 3.4419 |
| 3.7154 | 0.58 | 14500 | 3.4313 |
| 3.7117 | 0.6 | 15000 | 3.4156 |
| 3.6065 | 0.62 | 15500 | 3.3990 |
| 3.613 | 0.64 | 16000 | 3.3820 |
| 3.5824 | 0.66 | 16500 | 3.3702 |
| 3.6263 | 0.68 | 17000 | 3.3645 |
| 3.6073 | 0.7 | 17500 | 3.3529 |
| 3.587 | 0.72 | 18000 | 3.3419 |
| 3.624 | 0.74 | 18500 | 3.3340 |
| 3.583 | 0.76 | 19000 | 3.3273 |
| 3.573 | 0.78 | 19500 | 3.3185 |
| 3.5576 | 0.8 | 20000 | 3.3123 |
| 3.5623 | 0.82 | 20500 | 3.3103 |
| 3.5955 | 0.84 | 21000 | 3.3053 |
| 3.5947 | 0.86 | 21500 | 3.3015 |
| 3.5258 | 0.88 | 22000 | 3.2989 |
| 3.5985 | 0.9 | 22500 | 3.2962 |
| 3.5723 | 0.92 | 23000 | 3.2938 |
| 3.5863 | 0.94 | 23500 | 3.2927 |
| 3.5378 | 0.96 | 24000 | 3.2921 |
| 3.4952 | 0.98 | 24500 | 3.2919 |
| 3.5357 | 1.0 | 25000 | 3.2919 |
Framework versions
- Transformers 4.51.3
- Pytorch 2.5.1
- Datasets 2.19.1
- Tokenizers 0.21.1