| --- |
| library_name: transformers |
| tags: |
| - generated_from_trainer |
| model-index: |
| - name: chess_gpt |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # chess_gpt |
| |
| This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 2.4539 |
| |
| ## 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: 0.0003 |
| - train_batch_size: 96 |
| - eval_batch_size: 96 |
| - seed: 42 |
| - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.9) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: cosine |
| - lr_scheduler_warmup_steps: 200 |
| - num_epochs: 3 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | |
| |:-------------:|:------:|:-----:|:---------------:| |
| | 4.8573 | 0.0942 | 500 | 4.6881 | |
| | 4.0115 | 0.1884 | 1000 | 3.8051 | |
| | 3.6439 | 0.2826 | 1500 | 3.4294 | |
| | 3.4261 | 0.3769 | 2000 | 3.2262 | |
| | 3.2929 | 0.4711 | 2500 | 3.0936 | |
| | 3.1784 | 0.5653 | 3000 | 2.9891 | |
| | 3.1042 | 0.6595 | 3500 | 2.9109 | |
| | 3.0348 | 0.7537 | 4000 | 2.8448 | |
| | 2.9826 | 0.8479 | 4500 | 2.7931 | |
| | 2.9282 | 0.9422 | 5000 | 2.7484 | |
| | 2.8859 | 1.0364 | 5500 | 2.7090 | |
| | 2.8502 | 1.1306 | 6000 | 2.6747 | |
| | 2.8225 | 1.2248 | 6500 | 2.6463 | |
| | 2.7970 | 1.3190 | 7000 | 2.6216 | |
| | 2.7716 | 1.4132 | 7500 | 2.5987 | |
| | 2.7500 | 1.5074 | 8000 | 2.5777 | |
| | 2.7315 | 1.6017 | 8500 | 2.5614 | |
| | 2.7069 | 1.6959 | 9000 | 2.5424 | |
| | 2.6970 | 1.7901 | 9500 | 2.5273 | |
| | 2.6773 | 1.8843 | 10000 | 2.5158 | |
| | 2.6660 | 1.9785 | 10500 | 2.5039 | |
| | 2.6524 | 2.0727 | 11000 | 2.4941 | |
| | 2.6415 | 2.1669 | 11500 | 2.4847 | |
| | 2.6324 | 2.2612 | 12000 | 2.4775 | |
| | 2.6318 | 2.3554 | 12500 | 2.4720 | |
| | 2.6227 | 2.4496 | 13000 | 2.4655 | |
| | 2.6203 | 2.5438 | 13500 | 2.4612 | |
| | 2.6169 | 2.6380 | 14000 | 2.4581 | |
| | 2.6174 | 2.7322 | 14500 | 2.4562 | |
| | 2.6103 | 2.8265 | 15000 | 2.4544 | |
| | 2.6106 | 2.9207 | 15500 | 2.4540 | |
| | 2.6079 | 3.0 | 15921 | 2.4539 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 5.9.0 |
| - Pytorch 2.12.0+cu130 |
| - Datasets 4.8.5 |
| - Tokenizers 0.22.2 |
| |
