amdchess
This model is a fine-tuned version of reflex-ai/AMD-Llama-350M-Upgraded on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.6347
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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 0.1
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 8.019 | 0.0012 | 4 | 7.6135 |
| 7.7094 | 0.0024 | 8 | 7.0826 |
| 6.8737 | 0.0035 | 12 | 6.8392 |
| 6.6426 | 0.0047 | 16 | 6.6142 |
| 6.3563 | 0.0059 | 20 | 6.2879 |
| 6.0826 | 0.0071 | 24 | 5.9688 |
| 5.8464 | 0.0083 | 28 | 5.5885 |
| 5.3209 | 0.0094 | 32 | 5.4342 |
| 5.2345 | 0.0106 | 36 | 5.2125 |
| 4.9003 | 0.0118 | 40 | 4.9282 |
| 4.6779 | 0.0130 | 44 | 4.7029 |
| 4.3778 | 0.0142 | 48 | 4.3920 |
| 4.3256 | 0.0154 | 52 | 4.1814 |
| 3.9975 | 0.0165 | 56 | 4.0072 |
| 3.73 | 0.0177 | 60 | 3.8358 |
| 4.0483 | 0.0189 | 64 | 3.7093 |
| 3.7907 | 0.0201 | 68 | 3.5874 |
| 3.3881 | 0.0213 | 72 | 3.4606 |
| 3.5066 | 0.0224 | 76 | 3.4071 |
| 3.3845 | 0.0236 | 80 | 3.2889 |
| 3.2318 | 0.0248 | 84 | 3.1932 |
| 3.5897 | 0.0260 | 88 | 3.1209 |
| 3.0362 | 0.0272 | 92 | 3.0123 |
| 2.7973 | 0.0283 | 96 | 2.9055 |
| 2.8976 | 0.0295 | 100 | 2.8210 |
| 2.8188 | 0.0307 | 104 | 2.7422 |
| 2.5149 | 0.0319 | 108 | 2.6395 |
| 2.495 | 0.0331 | 112 | 2.5714 |
| 2.5654 | 0.0342 | 116 | 2.4863 |
| 2.4205 | 0.0354 | 120 | 2.4448 |
| 2.3487 | 0.0366 | 124 | 2.3561 |
| 2.413 | 0.0378 | 128 | 2.3265 |
| 2.2713 | 0.0390 | 132 | 2.2814 |
| 2.2293 | 0.0402 | 136 | 2.2361 |
| 2.2793 | 0.0413 | 140 | 2.1745 |
| 2.185 | 0.0425 | 144 | 2.1444 |
| 2.0137 | 0.0437 | 148 | 2.1245 |
| 2.1408 | 0.0449 | 152 | 2.0849 |
| 2.1539 | 0.0461 | 156 | 2.0650 |
| 2.0592 | 0.0472 | 160 | 2.0345 |
| 1.9849 | 0.0484 | 164 | 2.0390 |
| 1.8796 | 0.0496 | 168 | 1.9978 |
| 1.9646 | 0.0508 | 172 | 1.9860 |
| 1.9913 | 0.0520 | 176 | 1.9388 |
| 1.967 | 0.0531 | 180 | 1.9121 |
| 1.9141 | 0.0543 | 184 | 1.9085 |
| 1.9513 | 0.0555 | 188 | 1.9040 |
| 1.9123 | 0.0567 | 192 | 1.8606 |
| 1.8204 | 0.0579 | 196 | 1.8556 |
| 1.9311 | 0.0590 | 200 | 1.8390 |
| 1.8425 | 0.0602 | 204 | 1.8162 |
| 1.7932 | 0.0614 | 208 | 1.7914 |
| 1.591 | 0.0626 | 212 | 1.7749 |
| 1.7899 | 0.0638 | 216 | 1.7667 |
| 1.7094 | 0.0650 | 220 | 1.7637 |
| 1.8023 | 0.0661 | 224 | 1.7458 |
| 1.7368 | 0.0673 | 228 | 1.7339 |
| 1.5679 | 0.0685 | 232 | 1.7281 |
| 1.7265 | 0.0697 | 236 | 1.7221 |
| 1.7034 | 0.0709 | 240 | 1.7093 |
| 1.5902 | 0.0720 | 244 | 1.7086 |
| 1.6903 | 0.0732 | 248 | 1.6976 |
| 1.7581 | 0.0744 | 252 | 1.6944 |
| 1.656 | 0.0756 | 256 | 1.6899 |
| 1.4287 | 0.0768 | 260 | 1.6858 |
| 1.6527 | 0.0779 | 264 | 1.6754 |
| 1.7206 | 0.0791 | 268 | 1.6787 |
| 1.8268 | 0.0803 | 272 | 1.6673 |
| 1.538 | 0.0815 | 276 | 1.6590 |
| 1.7374 | 0.0827 | 280 | 1.6711 |
| 1.7255 | 0.0839 | 284 | 1.6513 |
| 1.6032 | 0.0850 | 288 | 1.6552 |
| 1.5297 | 0.0862 | 292 | 1.6458 |
| 1.7639 | 0.0874 | 296 | 1.6488 |
| 1.8029 | 0.0886 | 300 | 1.6441 |
| 1.665 | 0.0898 | 304 | 1.6425 |
| 1.6854 | 0.0909 | 308 | 1.6425 |
| 1.5418 | 0.0921 | 312 | 1.6396 |
| 1.6943 | 0.0933 | 316 | 1.6373 |
| 1.6758 | 0.0945 | 320 | 1.6359 |
| 1.9994 | 0.0957 | 324 | 1.6352 |
| 1.6326 | 0.0968 | 328 | 1.6349 |
| 1.6935 | 0.0980 | 332 | 1.6348 |
| 1.6358 | 0.0992 | 336 | 1.6347 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.0.2
- Tokenizers 0.19.1