Hyponatremia_M2_1000steps_1e7rate_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: 0.0020
- Rewards/chosen: -4.7124
- Rewards/rejected: -19.2249
- Rewards/accuracies: 0.9980
- Rewards/margins: 14.5125
- Logps/rejected: -344.9792
- Logps/chosen: -140.8642
- Logits/rejected: -2.0739
- Logits/chosen: -2.0387
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: 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.5233 | 0.0112 | 50 | 0.5232 | -0.0091 | -0.3925 | 0.9980 | 0.3833 | -156.6543 | -93.8310 | -2.3405 | -2.2932 |
| 0.0383 | 0.0224 | 100 | 0.0430 | -0.7708 | -4.7747 | 0.9980 | 4.0039 | -200.4769 | -101.4475 | -2.2287 | -2.1837 |
| 0.0 | 0.0336 | 150 | 0.0070 | -2.4385 | -10.5541 | 0.9980 | 8.1156 | -258.2711 | -118.1250 | -2.1599 | -2.1186 |
| 0.0016 | 0.0448 | 200 | 0.0031 | -3.4937 | -14.8511 | 0.9980 | 11.3574 | -301.2408 | -128.6765 | -2.1144 | -2.0760 |
| 0.0002 | 0.0559 | 250 | 0.0029 | -3.4966 | -15.1134 | 0.9980 | 11.6168 | -303.8634 | -128.7055 | -2.1174 | -2.0790 |
| 0.0 | 0.0671 | 300 | 0.0026 | -3.6799 | -15.9182 | 0.9980 | 12.2384 | -311.9122 | -130.5386 | -2.1042 | -2.0666 |
| 0.0012 | 0.0783 | 350 | 0.0024 | -3.9841 | -16.8321 | 0.9980 | 12.8480 | -321.0512 | -133.5813 | -2.0951 | -2.0582 |
| 0.0001 | 0.0895 | 400 | 0.0022 | -4.3249 | -17.8761 | 0.9980 | 13.5512 | -331.4908 | -136.9887 | -2.0841 | -2.0480 |
| 0.0 | 0.1007 | 450 | 0.0022 | -4.4809 | -18.3463 | 0.9980 | 13.8653 | -336.1925 | -138.5490 | -2.0802 | -2.0445 |
| 0.0 | 0.1119 | 500 | 0.0022 | -4.5041 | -18.4203 | 0.9980 | 13.9162 | -336.9331 | -138.7807 | -2.0778 | -2.0423 |
| 0.0 | 0.1231 | 550 | 0.0021 | -4.5894 | -18.8015 | 0.9980 | 14.2121 | -340.7446 | -139.6336 | -2.0767 | -2.0412 |
| 0.0 | 0.1343 | 600 | 0.0021 | -4.6515 | -19.0151 | 0.9980 | 14.3636 | -342.8809 | -140.2545 | -2.0750 | -2.0398 |
| 0.0 | 0.1454 | 650 | 0.0020 | -4.6765 | -19.1006 | 0.9980 | 14.4240 | -343.7354 | -140.5052 | -2.0753 | -2.0401 |
| 0.0 | 0.1566 | 700 | 0.0020 | -4.6869 | -19.1397 | 0.9980 | 14.4528 | -344.1270 | -140.6091 | -2.0750 | -2.0398 |
| 0.0 | 0.1678 | 750 | 0.0020 | -4.6998 | -19.1976 | 0.9980 | 14.4978 | -344.7062 | -140.7377 | -2.0747 | -2.0396 |
| 0.0 | 0.1790 | 800 | 0.0020 | -4.7132 | -19.2365 | 0.9980 | 14.5233 | -345.0950 | -140.8720 | -2.0739 | -2.0389 |
| 0.0096 | 0.1902 | 850 | 0.0020 | -4.7099 | -19.2301 | 0.9980 | 14.5202 | -345.0307 | -140.8386 | -2.0740 | -2.0389 |
| 0.0 | 0.2014 | 900 | 0.0020 | -4.7077 | -19.2206 | 0.9980 | 14.5129 | -344.9359 | -140.8168 | -2.0737 | -2.0386 |
| 0.0 | 0.2126 | 950 | 0.0020 | -4.7125 | -19.2249 | 0.9980 | 14.5125 | -344.9792 | -140.8644 | -2.0739 | -2.0387 |
| 0.0 | 0.2238 | 1000 | 0.0020 | -4.7124 | -19.2249 | 0.9980 | 14.5125 | -344.9792 | -140.8642 | -2.0739 | -2.0387 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.0.0+cu117
- Datasets 2.20.0
- Tokenizers 0.19.1
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