MedQA_L3_1000steps_1e5rate_05beta_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: 2.7867
- Rewards/chosen: -10.2874
- Rewards/rejected: -9.4675
- Rewards/accuracies: 0.4330
- Rewards/margins: -0.8198
- Logps/rejected: -52.7899
- Logps/chosen: -51.9033
- Logits/rejected: -0.3129
- Logits/chosen: -0.3128
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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.9373 | 0.0489 | 50 | 1.5325 | 0.6891 | -0.1945 | 0.5912 | 0.8836 | -34.2439 | -29.9504 | -1.1200 | -1.1197 |
| 3.7169 | 0.0977 | 100 | 3.7845 | -9.7504 | -8.8431 | 0.4527 | -0.9074 | -51.5409 | -50.8294 | -0.6137 | -0.6138 |
| 5.2014 | 0.1466 | 150 | 5.2600 | -22.3993 | -21.8605 | 0.4681 | -0.5389 | -77.5758 | -76.1272 | -1.3215 | -1.3217 |
| 5.4743 | 0.1954 | 200 | 3.9034 | -7.1491 | -6.2277 | 0.4176 | -0.9214 | -46.3103 | -45.6268 | -0.6483 | -0.6486 |
| 3.0731 | 0.2443 | 250 | 4.1865 | -11.6364 | -10.1791 | 0.4198 | -1.4572 | -54.2131 | -54.6012 | -0.7051 | -0.7056 |
| 5.7952 | 0.2931 | 300 | 3.6683 | -9.2381 | -7.9895 | 0.4264 | -1.2486 | -49.8338 | -49.8046 | -0.4055 | -0.4058 |
| 3.8474 | 0.3420 | 350 | 3.4898 | -12.7687 | -11.9414 | 0.4132 | -0.8274 | -57.7376 | -56.8660 | -0.8625 | -0.8625 |
| 5.5721 | 0.3908 | 400 | 3.4194 | -13.5468 | -12.3658 | 0.4044 | -1.1810 | -58.5864 | -58.4221 | -0.8921 | -0.8922 |
| 6.0929 | 0.4397 | 450 | 3.4518 | -12.5599 | -11.2787 | 0.4132 | -1.2812 | -56.4122 | -56.4483 | -0.6596 | -0.6596 |
| 5.4036 | 0.4885 | 500 | 3.4349 | -13.3250 | -12.3700 | 0.4264 | -0.9550 | -58.5948 | -57.9785 | -0.4398 | -0.4397 |
| 4.2614 | 0.5374 | 550 | 3.4447 | -13.2741 | -12.0523 | 0.4132 | -1.2218 | -57.9595 | -57.8767 | -0.2318 | -0.2318 |
| 5.0683 | 0.5862 | 600 | 3.6325 | -10.9169 | -9.7136 | 0.4242 | -1.2033 | -53.2821 | -53.1624 | 0.0024 | 0.0023 |
| 2.8041 | 0.6351 | 650 | 3.3753 | -13.7510 | -12.4756 | 0.4110 | -1.2754 | -58.8060 | -58.8306 | -0.4253 | -0.4254 |
| 2.852 | 0.6839 | 700 | 3.2123 | -11.3782 | -10.1837 | 0.4132 | -1.1945 | -54.2221 | -54.0849 | -0.3353 | -0.3353 |
| 3.1506 | 0.7328 | 750 | 2.9861 | -10.9246 | -9.9019 | 0.4198 | -1.0227 | -53.6587 | -53.1778 | -0.3577 | -0.3577 |
| 2.9206 | 0.7816 | 800 | 2.8476 | -10.3118 | -9.4465 | 0.4264 | -0.8653 | -52.7479 | -51.9522 | -0.2881 | -0.2880 |
| 3.6047 | 0.8305 | 850 | 2.8115 | -10.1979 | -9.3565 | 0.4308 | -0.8414 | -52.5679 | -51.7243 | -0.3016 | -0.3015 |
| 2.4799 | 0.8793 | 900 | 2.7874 | -10.3005 | -9.4828 | 0.4308 | -0.8177 | -52.8204 | -51.9295 | -0.3147 | -0.3146 |
| 2.8467 | 0.9282 | 950 | 2.7864 | -10.2878 | -9.4711 | 0.4330 | -0.8167 | -52.7969 | -51.9040 | -0.3132 | -0.3130 |
| 2.2638 | 0.9770 | 1000 | 2.7867 | -10.2874 | -9.4675 | 0.4330 | -0.8198 | -52.7899 | -51.9033 | -0.3129 | -0.3128 |
Framework versions
- Transformers 4.41.1
- Pytorch 2.0.0+cu117
- Datasets 2.19.1
- Tokenizers 0.19.1
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