amdchess-v8
This model is a fine-tuned version of amd/AMD-Llama-135m on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.7861
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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use grokadamw with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 0.25
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 3.3296 | 0.0030 | 5 | 2.6555 |
| 1.7617 | 0.0059 | 10 | 1.6829 |
| 1.344 | 0.0089 | 15 | 1.3500 |
| 1.1587 | 0.0118 | 20 | 1.1881 |
| 1.1949 | 0.0148 | 25 | 1.1602 |
| 1.0248 | 0.0177 | 30 | 1.1076 |
| 1.1176 | 0.0207 | 35 | 1.1118 |
| 0.9583 | 0.0236 | 40 | 1.0467 |
| 1.1116 | 0.0266 | 45 | 1.0376 |
| 0.9633 | 0.0295 | 50 | 1.0231 |
| 0.9704 | 0.0325 | 55 | 1.0089 |
| 1.0535 | 0.0354 | 60 | 1.0089 |
| 0.9668 | 0.0384 | 65 | 0.9763 |
| 0.9767 | 0.0413 | 70 | 0.9681 |
| 0.9745 | 0.0443 | 75 | 0.9648 |
| 0.9497 | 0.0472 | 80 | 0.9631 |
| 0.9192 | 0.0502 | 85 | 0.9406 |
| 0.9581 | 0.0531 | 90 | 0.9435 |
| 0.8981 | 0.0561 | 95 | 0.9271 |
| 0.9811 | 0.0590 | 100 | 0.9287 |
| 0.8313 | 0.0620 | 105 | 0.9138 |
| 0.898 | 0.0649 | 110 | 0.9120 |
| 0.954 | 0.0679 | 115 | 0.9109 |
| 0.9523 | 0.0708 | 120 | 0.9067 |
| 0.948 | 0.0738 | 125 | 0.9001 |
| 0.8825 | 0.0767 | 130 | 0.8932 |
| 0.9259 | 0.0797 | 135 | 0.8908 |
| 0.7937 | 0.0826 | 140 | 0.8831 |
| 0.8315 | 0.0856 | 145 | 0.8794 |
| 0.8488 | 0.0885 | 150 | 0.8800 |
| 0.8648 | 0.0915 | 155 | 0.8726 |
| 0.8976 | 0.0945 | 160 | 0.8701 |
| 0.9298 | 0.0974 | 165 | 0.8650 |
| 0.8856 | 0.1004 | 170 | 0.8635 |
| 0.7848 | 0.1033 | 175 | 0.8584 |
| 0.8366 | 0.1063 | 180 | 0.8526 |
| 0.8413 | 0.1092 | 185 | 0.8531 |
| 0.8577 | 0.1122 | 190 | 0.8498 |
| 0.8641 | 0.1151 | 195 | 0.8457 |
| 0.7957 | 0.1181 | 200 | 0.8429 |
| 0.8379 | 0.1210 | 205 | 0.8454 |
| 0.7596 | 0.1240 | 210 | 0.8404 |
| 0.8703 | 0.1269 | 215 | 0.8390 |
| 0.7297 | 0.1299 | 220 | 0.8327 |
| 0.885 | 0.1328 | 225 | 0.8299 |
| 0.7785 | 0.1358 | 230 | 0.8300 |
| 0.851 | 0.1387 | 235 | 0.8264 |
| 0.7234 | 0.1417 | 240 | 0.8222 |
| 0.7917 | 0.1446 | 245 | 0.8226 |
| 0.8123 | 0.1476 | 250 | 0.8195 |
| 0.7801 | 0.1505 | 255 | 0.8170 |
| 0.7086 | 0.1535 | 260 | 0.8156 |
| 0.8673 | 0.1564 | 265 | 0.8137 |
| 0.8298 | 0.1594 | 270 | 0.8144 |
| 0.8097 | 0.1623 | 275 | 0.8113 |
| 0.8079 | 0.1653 | 280 | 0.8095 |
| 0.7917 | 0.1682 | 285 | 0.8079 |
| 0.8206 | 0.1712 | 290 | 0.8058 |
| 0.8438 | 0.1741 | 295 | 0.8037 |
| 0.8519 | 0.1771 | 300 | 0.8015 |
| 0.8844 | 0.1800 | 305 | 0.8016 |
| 0.8217 | 0.1830 | 310 | 0.7998 |
| 0.6939 | 0.1860 | 315 | 0.7982 |
| 0.8021 | 0.1889 | 320 | 0.7975 |
| 0.8357 | 0.1919 | 325 | 0.7961 |
| 0.8487 | 0.1948 | 330 | 0.7945 |
| 0.648 | 0.1978 | 335 | 0.7936 |
| 0.7599 | 0.2007 | 340 | 0.7924 |
| 0.8203 | 0.2037 | 345 | 0.7923 |
| 0.8072 | 0.2066 | 350 | 0.7915 |
| 0.8278 | 0.2096 | 355 | 0.7904 |
| 0.7202 | 0.2125 | 360 | 0.7898 |
| 0.7229 | 0.2155 | 365 | 0.7891 |
| 0.8432 | 0.2184 | 370 | 0.7887 |
| 0.8615 | 0.2214 | 375 | 0.7879 |
| 0.8234 | 0.2243 | 380 | 0.7875 |
| 0.8101 | 0.2273 | 385 | 0.7871 |
| 0.8464 | 0.2302 | 390 | 0.7868 |
| 0.7966 | 0.2332 | 395 | 0.7866 |
| 0.718 | 0.2361 | 400 | 0.7864 |
| 0.741 | 0.2391 | 405 | 0.7863 |
| 0.7903 | 0.2420 | 410 | 0.7862 |
| 0.7671 | 0.2450 | 415 | 0.7861 |
| 0.7657 | 0.2479 | 420 | 0.7861 |
Framework versions
- Transformers 4.46.0
- Pytorch 2.5.0+cu121
- Datasets 3.0.2
- Tokenizers 0.20.1