MedQA_L3_600steps_1e7rate_01beta_CSFTDPO
This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6692
- Rewards/chosen: 0.0482
- Rewards/rejected: -0.0053
- Rewards/accuracies: 0.6681
- Rewards/margins: 0.0535
- Logps/rejected: -21.3695
- Logps/chosen: -17.7404
- Logits/rejected: -0.9398
- Logits/chosen: -0.9393
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: 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: 600
Training results
Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
---|---|---|---|---|---|---|---|---|---|---|---|
0.6951 | 0.0489 | 50 | 0.6935 | 0.0003 | 0.0009 | 0.4901 | -0.0006 | -21.3079 | -18.2196 | -0.9258 | -0.9253 |
0.6892 | 0.0977 | 100 | 0.6881 | 0.0374 | 0.0268 | 0.6044 | 0.0106 | -21.0482 | -17.8488 | -0.9281 | -0.9276 |
0.6801 | 0.1466 | 150 | 0.6794 | 0.0588 | 0.0292 | 0.6418 | 0.0296 | -21.0241 | -17.6343 | -0.9314 | -0.9309 |
0.6807 | 0.1954 | 200 | 0.6767 | 0.0584 | 0.0227 | 0.6549 | 0.0358 | -21.0897 | -17.6383 | -0.9345 | -0.9339 |
0.6829 | 0.2443 | 250 | 0.6726 | 0.0560 | 0.0106 | 0.6571 | 0.0454 | -21.2109 | -17.6631 | -0.9367 | -0.9362 |
0.6656 | 0.2931 | 300 | 0.6715 | 0.0540 | 0.0059 | 0.6505 | 0.0481 | -21.2575 | -17.6830 | -0.9382 | -0.9376 |
0.6955 | 0.3420 | 350 | 0.6697 | 0.0524 | 0.0002 | 0.6571 | 0.0522 | -21.3145 | -17.6986 | -0.9384 | -0.9378 |
0.6605 | 0.3908 | 400 | 0.6697 | 0.0493 | -0.0031 | 0.6505 | 0.0524 | -21.3476 | -17.7294 | -0.9393 | -0.9388 |
0.6718 | 0.4397 | 450 | 0.6689 | 0.0495 | -0.0047 | 0.6527 | 0.0541 | -21.3631 | -17.7279 | -0.9396 | -0.9390 |
0.6734 | 0.4885 | 500 | 0.6687 | 0.0486 | -0.0059 | 0.6505 | 0.0545 | -21.3751 | -17.7362 | -0.9397 | -0.9392 |
0.6525 | 0.5374 | 550 | 0.6691 | 0.0482 | -0.0056 | 0.6615 | 0.0537 | -21.3720 | -17.7410 | -0.9398 | -0.9393 |
0.6637 | 0.5862 | 600 | 0.6692 | 0.0482 | -0.0053 | 0.6681 | 0.0535 | -21.3695 | -17.7404 | -0.9398 | -0.9393 |
Framework versions
- Transformers 4.41.0
- Pytorch 2.0.0+cu117
- Datasets 2.19.1
- Tokenizers 0.19.1
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