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---
tags:
- generated_from_trainer
base_model: austindavis/gpt2-lichess-uci-2016-01_11
model-index:
- name: gpt2-lichess-uci-202306
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. -->
# gpt2-lichess-uci-202306
This model is a fine-tuned version of [austindavis/gpt2-lichess-uci-2016-01_11](https://huggingface.co/austindavis/gpt2-lichess-uci-2016-01_11) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8839
## 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: 0.002
- train_batch_size: 20
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:-------:|:---------------:|
| 1.022 | 0.1323 | 165000 | 1.0013 |
| 1.0204 | 0.1443 | 180000 | 1.0001 |
| 1.0186 | 0.1563 | 195000 | 0.9973 |
| 1.0155 | 0.1684 | 210000 | 0.9954 |
| 1.0133 | 0.1804 | 225000 | 0.9935 |
| 1.0118 | 0.1924 | 240000 | 0.9924 |
| 1.0092 | 0.2044 | 255000 | 0.9893 |
| 1.007 | 0.2165 | 270000 | 0.9881 |
| 1.0057 | 0.2285 | 285000 | 0.9868 |
| 1.0035 | 0.2405 | 300000 | 0.9879 |
| 1.004 | 0.2525 | 315000 | 0.9843 |
| 1.0005 | 0.2646 | 330000 | 0.9807 |
| 0.9986 | 0.2766 | 345000 | 0.9805 |
| 0.9983 | 0.2886 | 360000 | 0.9776 |
| 0.9965 | 0.3006 | 375000 | 0.9781 |
| 0.9935 | 0.3127 | 390000 | 0.9754 |
| 0.9935 | 0.3247 | 405000 | 0.9761 |
| 0.9916 | 0.3367 | 420000 | 0.9743 |
| 0.989 | 0.3487 | 435000 | 0.9712 |
| 0.988 | 0.3608 | 450000 | 0.9702 |
| 0.9862 | 0.3728 | 465000 | 0.9703 |
| 0.9837 | 0.3848 | 480000 | 0.9680 |
| 0.983 | 0.3968 | 495000 | 0.9643 |
| 0.9816 | 0.4089 | 510000 | 0.9634 |
| 0.9796 | 0.4209 | 525000 | 0.9628 |
| 0.9777 | 0.4329 | 540000 | 0.9612 |
| 0.9744 | 0.4449 | 555000 | 0.9587 |
| 0.9733 | 0.4570 | 570000 | 0.9590 |
| 0.97 | 0.4690 | 585000 | 0.9566 |
| 0.9693 | 0.4810 | 600000 | 0.9539 |
| 0.9684 | 0.4930 | 615000 | 0.9532 |
| 0.9652 | 0.5051 | 630000 | 0.9509 |
| 0.9644 | 0.5171 | 645000 | 0.9501 |
| 0.9614 | 0.5291 | 660000 | 0.9479 |
| 0.9606 | 0.5411 | 675000 | 0.9466 |
| 0.9597 | 0.5532 | 690000 | 0.9444 |
| 0.9556 | 0.5652 | 705000 | 0.9416 |
| 0.9541 | 0.5772 | 720000 | 0.9413 |
| 0.9522 | 0.5892 | 735000 | 0.9382 |
| 0.9491 | 0.6013 | 750000 | 0.9367 |
| 0.9471 | 0.6133 | 765000 | 0.9354 |
| 0.9459 | 0.6253 | 780000 | 0.9321 |
| 0.9416 | 0.6373 | 795000 | 0.9309 |
| 0.9401 | 0.6494 | 810000 | 0.9287 |
| 0.9383 | 0.6614 | 825000 | 0.9265 |
| 0.9375 | 0.6734 | 840000 | 0.9238 |
| 0.9354 | 0.6854 | 855000 | 0.9225 |
| 0.9323 | 0.6975 | 870000 | 0.9196 |
| 0.9291 | 0.7095 | 885000 | 0.9189 |
| 0.9276 | 0.7215 | 900000 | 0.9165 |
| 0.9266 | 0.7335 | 915000 | 0.9142 |
| 0.9221 | 0.7456 | 930000 | 0.9130 |
| 0.9216 | 0.7576 | 945000 | 0.9106 |
| 0.9191 | 0.7696 | 960000 | 0.9084 |
| 0.9152 | 0.7816 | 975000 | 0.9062 |
| 0.9127 | 0.7937 | 990000 | 0.9039 |
| 0.9133 | 0.8057 | 1005000 | 0.9014 |
| 0.9086 | 0.8177 | 1020000 | 0.8997 |
| 0.9078 | 0.8297 | 1035000 | 0.8978 |
| 0.9054 | 0.8418 | 1050000 | 0.8955 |
| 0.9037 | 0.8538 | 1065000 | 0.8943 |
| 0.9015 | 0.8658 | 1080000 | 0.8926 |
| 0.9006 | 0.8778 | 1095000 | 0.8912 |
| 0.8991 | 0.8899 | 1110000 | 0.8897 |
| 0.897 | 0.9019 | 1125000 | 0.8885 |
| 0.8971 | 0.9139 | 1140000 | 0.8873 |
| 0.894 | 0.9259 | 1155000 | 0.8864 |
| 0.8938 | 0.9380 | 1170000 | 0.8854 |
| 0.893 | 0.9500 | 1185000 | 0.8848 |
| 0.8922 | 0.9620 | 1200000 | 0.8844 |
| 0.8936 | 0.9740 | 1215000 | 0.8841 |
| 0.8923 | 0.9861 | 1230000 | 0.8840 |
| 0.8922 | 0.9981 | 1245000 | 0.8839 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.19.1
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