| --- |
| library_name: transformers |
| tags: |
| - generated_from_trainer |
| model-index: |
| - name: ChessQween3-base |
| 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. --> |
|
|
| # ChessQween3-base |
|
|
| 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: 1.9453 |
|
|
| ## 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: 128 |
| - eval_batch_size: 64 |
| - seed: 42 |
| - distributed_type: multi-GPU |
| - num_devices: 2 |
| - gradient_accumulation_steps: 2 |
| - total_train_batch_size: 512 |
| - total_eval_batch_size: 128 |
| - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 1000 |
| - num_epochs: 1 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | |
| |:-------------:|:------:|:-----:|:---------------:| |
| | 3.7947 | 0.0256 | 2000 | 3.7973 | |
| | 3.2355 | 0.0512 | 4000 | 3.3104 | |
| | 3.1324 | 0.0768 | 6000 | 3.0861 | |
| | 3.0801 | 0.1025 | 8000 | 2.9574 | |
| | 2.9370 | 0.1281 | 10000 | 2.8806 | |
| | 2.8139 | 0.1537 | 12000 | 2.7924 | |
| | 2.7011 | 0.1793 | 14000 | 2.7364 | |
| | 2.5453 | 0.2049 | 16000 | 2.6827 | |
| | 2.6728 | 0.2305 | 18000 | 2.6320 | |
| | 2.5903 | 0.2561 | 20000 | 2.5825 | |
| | 2.5910 | 0.2817 | 22000 | 2.5478 | |
| | 2.4977 | 0.3074 | 24000 | 2.5110 | |
| | 2.5118 | 0.3330 | 26000 | 2.4747 | |
| | 2.4336 | 0.3586 | 28000 | 2.4338 | |
| | 2.4289 | 0.3842 | 30000 | 2.3929 | |
| | 2.3808 | 0.4098 | 32000 | 2.3621 | |
| | 2.3663 | 0.4354 | 34000 | 2.3340 | |
| | 2.3412 | 0.4610 | 36000 | 2.3026 | |
| | 2.3884 | 0.4866 | 38000 | 2.2735 | |
| | 2.3094 | 0.5123 | 40000 | 2.2408 | |
| | 2.3330 | 0.5379 | 42000 | 2.2155 | |
| | 2.1700 | 0.5635 | 44000 | 2.1895 | |
| | 2.1146 | 0.5891 | 46000 | 2.1646 | |
| | 2.1595 | 0.6147 | 48000 | 2.1398 | |
| | 2.0966 | 0.6403 | 50000 | 2.1140 | |
| | 2.2037 | 0.6659 | 52000 | 2.0877 | |
| | 1.9864 | 0.6915 | 54000 | 2.0719 | |
| | 2.0506 | 0.7172 | 56000 | 2.0551 | |
| | 1.9694 | 0.7428 | 58000 | 2.0328 | |
| | 2.1410 | 0.7684 | 60000 | 2.0160 | |
| | 2.0000 | 0.7940 | 62000 | 1.9990 | |
| | 1.9383 | 0.8196 | 64000 | 1.9859 | |
| | 2.0483 | 0.8452 | 66000 | 1.9706 | |
| | 1.9896 | 0.8708 | 68000 | 1.9616 | |
| | 1.9138 | 0.8965 | 70000 | 1.9552 | |
| | 2.0671 | 0.9221 | 72000 | 1.9515 | |
| | 1.9800 | 0.9477 | 74000 | 1.9467 | |
| | 1.9927 | 0.9733 | 76000 | 1.9455 | |
| | 1.9911 | 0.9989 | 78000 | 1.9453 | |
|
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|
|
| ### Framework versions |
|
|
| - Transformers 5.0.0 |
| - Pytorch 2.10.0+cu128 |
| - Datasets 5.0.0 |
| - Tokenizers 0.22.2 |
|
|
