MedQA_L3_1000steps_1e6rate_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: 0.4888
- Rewards/chosen: 3.1508
- Rewards/rejected: 1.3776
- Rewards/accuracies: 0.7868
- Rewards/margins: 1.7732
- Logps/rejected: -29.2628
- Logps/chosen: -20.8258
- Logits/rejected: -0.8475
- Logits/chosen: -0.8455
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: 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: 300
Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.685 | 0.0489 | 50 | 0.6334 | -0.7936 | -0.9359 | 0.7363 | 0.1423 | -36.9746 | -33.9739 | -0.7278 | -0.7271 |
| 0.4052 | 0.0977 | 100 | 0.6106 | 3.7995 | 2.4858 | 0.6945 | 1.3137 | -25.5688 | -18.6634 | -0.7922 | -0.7909 |
| 0.6421 | 0.1466 | 150 | 0.5225 | 2.5850 | 1.3506 | 0.7538 | 1.2344 | -29.3529 | -22.7119 | -0.8369 | -0.8356 |
| 0.3501 | 0.1954 | 200 | 0.5243 | 2.6639 | 0.8481 | 0.7626 | 1.8159 | -31.0279 | -22.4487 | -0.8442 | -0.8422 |
| 0.3618 | 0.2443 | 250 | 0.4899 | 3.1411 | 1.3754 | 0.7802 | 1.7657 | -29.2702 | -20.8582 | -0.8474 | -0.8454 |
| 0.3181 | 0.2931 | 300 | 0.4888 | 3.1508 | 1.3776 | 0.7868 | 1.7732 | -29.2628 | -20.8258 | -0.8475 | -0.8455 |
Framework versions
- Transformers 4.41.1
- Pytorch 2.0.0+cu117
- Datasets 2.19.1
- Tokenizers 0.19.1
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