Hyponatremia_L3_1000steps_1e7rate_01beta_DPO
This model is a fine-tuned version of tsavage68/Hyponatremia_L3_450steps_1e7rate_SFT on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0002
- Rewards/chosen: 0.7739
- Rewards/rejected: -7.9129
- Rewards/accuracies: 1.0
- Rewards/margins: 8.6868
- Logps/rejected: -118.5559
- Logps/chosen: -14.9775
- Logits/rejected: -1.0497
- Logits/chosen: -0.9632
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-07
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.6427 | 0.2667 | 50 | 0.6206 | 0.0372 | -0.1137 | 1.0 | 0.1509 | -40.5638 | -22.3445 | -1.0187 | -0.9442 |
| 0.2712 | 0.5333 | 100 | 0.2271 | 0.3707 | -1.0112 | 1.0 | 1.3819 | -49.5389 | -19.0103 | -1.0117 | -0.9296 |
| 0.0371 | 0.8 | 150 | 0.0274 | 0.5978 | -3.0240 | 1.0 | 3.6218 | -69.6671 | -16.7390 | -1.0135 | -0.9230 |
| 0.0029 | 1.0667 | 200 | 0.0021 | 0.7710 | -5.4116 | 1.0 | 6.1826 | -93.5423 | -15.0066 | -1.0253 | -0.9359 |
| 0.0009 | 1.3333 | 250 | 0.0008 | 0.7933 | -6.3549 | 1.0 | 7.1482 | -102.9761 | -14.7838 | -1.0328 | -0.9448 |
| 0.0006 | 1.6 | 300 | 0.0005 | 0.7940 | -6.7705 | 1.0 | 7.5645 | -107.1315 | -14.7770 | -1.0361 | -0.9485 |
| 0.0004 | 1.8667 | 350 | 0.0004 | 0.7881 | -7.0759 | 1.0 | 7.8640 | -110.1858 | -14.8355 | -1.0394 | -0.9521 |
| 0.0004 | 2.1333 | 400 | 0.0003 | 0.7821 | -7.3359 | 1.0 | 8.1180 | -112.7859 | -14.8960 | -1.0429 | -0.9563 |
| 0.0003 | 2.4 | 450 | 0.0003 | 0.7798 | -7.5128 | 1.0 | 8.2926 | -114.5547 | -14.9184 | -1.0449 | -0.9579 |
| 0.0002 | 2.6667 | 500 | 0.0002 | 0.7775 | -7.6568 | 1.0 | 8.4343 | -115.9949 | -14.9422 | -1.0464 | -0.9593 |
| 0.0002 | 2.9333 | 550 | 0.0002 | 0.7737 | -7.7702 | 1.0 | 8.5438 | -117.1287 | -14.9803 | -1.0478 | -0.9611 |
| 0.0002 | 3.2 | 600 | 0.0002 | 0.7750 | -7.8413 | 1.0 | 8.6163 | -117.8397 | -14.9665 | -1.0482 | -0.9615 |
| 0.0002 | 3.4667 | 650 | 0.0002 | 0.7735 | -7.8850 | 1.0 | 8.6585 | -118.2773 | -14.9821 | -1.0487 | -0.9621 |
| 0.0002 | 3.7333 | 700 | 0.0002 | 0.7729 | -7.8996 | 1.0 | 8.6725 | -118.4227 | -14.9879 | -1.0481 | -0.9615 |
| 0.0002 | 4.0 | 750 | 0.0002 | 0.7711 | -7.9099 | 1.0 | 8.6809 | -118.5257 | -15.0061 | -1.0491 | -0.9626 |
| 0.0002 | 4.2667 | 800 | 0.0002 | 0.7740 | -7.9067 | 1.0 | 8.6807 | -118.4939 | -14.9766 | -1.0490 | -0.9623 |
| 0.0002 | 4.5333 | 850 | 0.0002 | 0.7742 | -7.9121 | 1.0 | 8.6863 | -118.5480 | -14.9751 | -1.0491 | -0.9626 |
| 0.0002 | 4.8 | 900 | 0.0002 | 0.7735 | -7.9119 | 1.0 | 8.6854 | -118.5454 | -14.9815 | -1.0497 | -0.9632 |
| 0.0002 | 5.0667 | 950 | 0.0002 | 0.7739 | -7.9129 | 1.0 | 8.6868 | -118.5559 | -14.9775 | -1.0497 | -0.9632 |
| 0.0002 | 5.3333 | 1000 | 0.0002 | 0.7739 | -7.9129 | 1.0 | 8.6868 | -118.5559 | -14.9775 | -1.0497 | -0.9632 |
Framework versions
- Transformers 4.42.3
- Pytorch 2.0.0+cu117
- Datasets 2.20.0
- Tokenizers 0.19.1
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