Hyponatremia_M2_1000steps_1e6rate_01beta_CSFTDPO
This model is a fine-tuned version of tsavage68/Summary4500_M2_200steps_1e7rate_SFT on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0014
- Rewards/chosen: -6.9453
- Rewards/rejected: -39.6400
- Rewards/accuracies: 0.9980
- Rewards/margins: 32.6947
- Logps/rejected: -549.1301
- Logps/chosen: -163.1928
- Logits/rejected: -2.1597
- Logits/chosen: -2.1358
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: 1e-06
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 1000
Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.0 | 0.0112 | 50 | 0.0019 | -4.8993 | -19.0319 | 0.9980 | 14.1326 | -343.0486 | -142.7324 | -2.0795 | -2.0441 |
| 0.0 | 0.0224 | 100 | 0.0015 | -6.3729 | -26.5440 | 0.9980 | 20.1711 | -418.1701 | -157.4689 | -2.0302 | -2.0035 |
| 0.0 | 0.0336 | 150 | 0.0015 | -6.3657 | -26.5320 | 0.9980 | 20.1663 | -418.0495 | -157.3970 | -2.0306 | -2.0039 |
| 0.0 | 0.0448 | 200 | 0.0014 | -16.4748 | -52.0376 | 0.9980 | 35.5629 | -673.1061 | -258.4873 | -1.9728 | -1.9500 |
| 0.0 | 0.0559 | 250 | 0.0014 | -6.6723 | -38.4997 | 0.9980 | 31.8273 | -537.7265 | -160.4631 | -2.0948 | -2.0746 |
| 0.0 | 0.0671 | 300 | 0.0014 | -6.6672 | -38.4953 | 0.9980 | 31.8281 | -537.6830 | -160.4116 | -2.0948 | -2.0746 |
| 0.0 | 0.0783 | 350 | 0.0014 | -6.7078 | -38.6229 | 0.9980 | 31.9151 | -538.9587 | -160.8179 | -2.0942 | -2.0740 |
| 0.0 | 0.0895 | 400 | 0.0014 | -6.7097 | -38.6087 | 0.9980 | 31.8990 | -538.8165 | -160.8368 | -2.0941 | -2.0739 |
| 0.0 | 0.1007 | 450 | 0.0014 | -6.7097 | -38.6087 | 0.9980 | 31.8990 | -538.8165 | -160.8368 | -2.0941 | -2.0739 |
| 0.0 | 0.1119 | 500 | 0.0014 | -6.7083 | -38.6077 | 0.9980 | 31.8993 | -538.8064 | -160.8230 | -2.0942 | -2.0740 |
| 0.0 | 0.1231 | 550 | 0.0014 | -7.0457 | -39.9264 | 0.9980 | 32.8807 | -551.9941 | -164.1973 | -2.1573 | -2.1335 |
| 0.0 | 0.1343 | 600 | 0.0014 | -7.0457 | -39.9264 | 0.9980 | 32.8807 | -551.9941 | -164.1973 | -2.1573 | -2.1335 |
| 0.0 | 0.1454 | 650 | 0.0014 | -7.0449 | -39.9382 | 0.9980 | 32.8933 | -552.1118 | -164.1887 | -2.1576 | -2.1338 |
| 0.0 | 0.1566 | 700 | 0.0014 | -7.0449 | -39.9382 | 0.9980 | 32.8933 | -552.1118 | -164.1887 | -2.1576 | -2.1338 |
| 0.0 | 0.1678 | 750 | 0.0014 | -7.0380 | -39.9081 | 0.9980 | 32.8700 | -551.8103 | -164.1199 | -2.1589 | -2.1351 |
| 0.0 | 0.1790 | 800 | 0.0014 | -7.0380 | -39.9081 | 0.9980 | 32.8700 | -551.8103 | -164.1199 | -2.1589 | -2.1351 |
| 0.0004 | 0.1902 | 850 | 0.0014 | -6.9510 | -39.6563 | 0.9980 | 32.7053 | -549.2929 | -163.2495 | -2.1596 | -2.1357 |
| 0.0 | 0.2014 | 900 | 0.0014 | -6.9482 | -39.6525 | 0.9980 | 32.7043 | -549.2548 | -163.2216 | -2.1596 | -2.1357 |
| 0.0 | 0.2126 | 950 | 0.0014 | -6.9451 | -39.6374 | 0.9980 | 32.6923 | -549.1039 | -163.1913 | -2.1597 | -2.1358 |
| 0.0 | 0.2238 | 1000 | 0.0014 | -6.9453 | -39.6400 | 0.9980 | 32.6947 | -549.1301 | -163.1928 | -2.1597 | -2.1358 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.0.0+cu117
- Datasets 2.20.0
- Tokenizers 0.19.1
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