MedQA_L3_1000steps_1e5rate_03beta_CSFTDPO
This model is a fine-tuned version of tsavage68/MedQA_L3_1000steps_1e6rate_SFT on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.8199
- Rewards/chosen: -5.6953
- Rewards/rejected: -5.2697
- Rewards/accuracies: 0.4571
- Rewards/margins: -0.4255
- Logps/rejected: -51.4207
- Logps/chosen: -50.3128
- Logits/rejected: -1.1748
- Logits/chosen: -1.1747
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-05
- 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.6976 | 0.0489 | 50 | 1.6003 | -6.0871 | -6.7321 | 0.5626 | 0.6450 | -56.2952 | -51.6189 | -0.8478 | -0.8474 |
| 2.0492 | 0.0977 | 100 | 1.5171 | -2.8937 | -2.7957 | 0.4791 | -0.0979 | -43.1739 | -40.9741 | -0.7086 | -0.7085 |
| 3.2675 | 0.1466 | 150 | 2.4839 | -9.5405 | -8.8952 | 0.4264 | -0.6452 | -63.5056 | -63.1301 | -0.6090 | -0.6092 |
| 2.5387 | 0.1954 | 200 | 2.8407 | -10.8845 | -10.2333 | 0.4220 | -0.6513 | -67.9657 | -67.6103 | -2.0451 | -2.0454 |
| 3.5954 | 0.2443 | 250 | 5.2964 | -26.2267 | -26.1016 | 0.4725 | -0.1251 | -120.8603 | -118.7509 | -2.7907 | -2.7903 |
| 5.2171 | 0.2931 | 300 | 3.1156 | -11.9636 | -11.4341 | 0.4549 | -0.5294 | -71.9686 | -71.2070 | -1.4795 | -1.4797 |
| 2.6671 | 0.3420 | 350 | 2.8765 | -8.6508 | -8.1258 | 0.4220 | -0.5250 | -60.9407 | -60.1644 | -0.9503 | -0.9502 |
| 3.7894 | 0.3908 | 400 | 2.8694 | -9.8779 | -9.1060 | 0.4242 | -0.7720 | -64.2081 | -64.2550 | -1.0926 | -1.0927 |
| 4.4115 | 0.4397 | 450 | 2.6152 | -9.1581 | -8.5492 | 0.4176 | -0.6089 | -62.3523 | -61.8555 | -1.3932 | -1.3933 |
| 3.6882 | 0.4885 | 500 | 2.5995 | -10.0842 | -9.5563 | 0.4352 | -0.5279 | -65.7092 | -64.9425 | -1.3920 | -1.3918 |
| 4.7478 | 0.5374 | 550 | 3.1439 | -13.8538 | -13.2693 | 0.4264 | -0.5845 | -78.0858 | -77.5078 | -1.4673 | -1.4673 |
| 3.6453 | 0.5862 | 600 | 2.5501 | -10.1562 | -9.6020 | 0.4154 | -0.5542 | -65.8615 | -65.1824 | -1.8008 | -1.8006 |
| 1.9093 | 0.6351 | 650 | 2.0900 | -7.1034 | -6.4496 | 0.4352 | -0.6537 | -55.3536 | -55.0064 | -1.5307 | -1.5306 |
| 1.978 | 0.6839 | 700 | 1.9643 | -5.1638 | -4.6928 | 0.4593 | -0.4710 | -49.4976 | -48.5413 | -1.2420 | -1.2419 |
| 2.6252 | 0.7328 | 750 | 1.8926 | -6.6759 | -6.1506 | 0.4396 | -0.5254 | -54.3567 | -53.5815 | -1.3560 | -1.3560 |
| 2.0384 | 0.7816 | 800 | 1.8552 | -6.4512 | -5.9923 | 0.4374 | -0.4588 | -53.8292 | -52.8324 | -1.2189 | -1.2188 |
| 2.3167 | 0.8305 | 850 | 1.8255 | -5.8191 | -5.3851 | 0.4549 | -0.4341 | -51.8050 | -50.7256 | -1.1902 | -1.1901 |
| 2.1526 | 0.8793 | 900 | 1.8196 | -5.7219 | -5.2966 | 0.4549 | -0.4252 | -51.5102 | -50.4014 | -1.1751 | -1.1750 |
| 2.0182 | 0.9282 | 950 | 1.8220 | -5.6982 | -5.2706 | 0.4593 | -0.4276 | -51.4235 | -50.3224 | -1.1750 | -1.1749 |
| 1.3984 | 0.9770 | 1000 | 1.8199 | -5.6953 | -5.2697 | 0.4571 | -0.4255 | -51.4207 | -50.3128 | -1.1748 | -1.1747 |
Framework versions
- Transformers 4.41.1
- Pytorch 2.0.0+cu117
- Datasets 2.19.1
- Tokenizers 0.19.1
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