Hyponatremia_M2_350steps_1e8rate_05beta_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: 0.6748
- Rewards/chosen: -0.0068
- Rewards/rejected: -0.0657
- Rewards/accuracies: 0.5680
- Rewards/margins: 0.0590
- Logps/rejected: -152.8613
- Logps/chosen: -93.7533
- Logits/rejected: -2.3514
- Logits/chosen: -2.3039
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-08
- 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: 350
Training results
Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
---|---|---|---|---|---|---|---|---|---|---|---|
0.7203 | 0.0112 | 50 | 0.6987 | -0.0033 | -0.0121 | 0.5140 | 0.0089 | -152.7540 | -93.7463 | -2.3525 | -2.3051 |
0.7141 | 0.0224 | 100 | 0.7007 | -0.0065 | -0.0124 | 0.5120 | 0.0059 | -152.7545 | -93.7527 | -2.3518 | -2.3044 |
0.6852 | 0.0336 | 150 | 0.6771 | -0.0123 | -0.0648 | 0.5840 | 0.0525 | -152.8595 | -93.7644 | -2.3522 | -2.3047 |
0.6459 | 0.0448 | 200 | 0.6783 | -0.0073 | -0.0594 | 0.5780 | 0.0520 | -152.8485 | -93.7544 | -2.3503 | -2.3029 |
0.7282 | 0.0559 | 250 | 0.6723 | -0.0149 | -0.0813 | 0.5920 | 0.0664 | -152.8924 | -93.7697 | -2.3516 | -2.3041 |
0.6859 | 0.0671 | 300 | 0.6770 | -0.0108 | -0.0658 | 0.5540 | 0.0550 | -152.8613 | -93.7613 | -2.3514 | -2.3039 |
0.7327 | 0.0783 | 350 | 0.6748 | -0.0068 | -0.0657 | 0.5680 | 0.0590 | -152.8613 | -93.7533 | -2.3514 | -2.3039 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.0.0+cu117
- Datasets 2.20.0
- Tokenizers 0.19.1
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