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--- |
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tags: |
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- trl |
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- dpo |
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- generated_from_trainer |
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model-index: |
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- name: dpo-selective-buffer-safeipo |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# dpo-selective-buffer-safeipo |
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This model was trained from scratch on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 4322.0576 |
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- Rewards/chosen: -0.9426 |
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- Rewards/rejected: -1.0072 |
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- Rewards/accuracies: 0.6033 |
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- Rewards/margins: 0.0646 |
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- Rewards/safe Rewards: -0.9377 |
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- Rewards/unsafe Rewards: -0.9382 |
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- Logps/rejected: -193.1814 |
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- Logps/chosen: -224.6856 |
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- Logits/rejected: -1.7714 |
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- Logits/chosen: -1.9525 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-07 |
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- train_batch_size: 2 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 4 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 32 |
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- total_eval_batch_size: 32 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Rewards/safe Rewards | Rewards/unsafe Rewards | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | |
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|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------------:|:----------------------:|:--------------:|:------------:|:---------------:|:-------------:| |
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| 13096.7359 | 0.16 | 300 | 4529.6733 | -0.3957 | -0.4772 | 0.6584 | 0.0815 | -0.3930 | -0.3956 | -140.1830 | -170.0027 | -2.1815 | -2.3195 | |
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| 11584.7875 | 0.32 | 600 | 4406.7134 | -0.8083 | -0.8819 | 0.6338 | 0.0736 | -0.8028 | -0.8050 | -180.6571 | -211.2575 | -1.7938 | -1.9934 | |
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| 10862.3484 | 0.48 | 900 | 4377.5635 | -0.8828 | -0.9530 | 0.6196 | 0.0701 | -0.8775 | -0.8778 | -187.7609 | -218.7140 | -1.7468 | -1.9377 | |
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| 11671.4219 | 0.65 | 1200 | 4346.4053 | -0.9811 | -1.0509 | 0.6158 | 0.0699 | -0.9764 | -0.9768 | -197.5588 | -228.5369 | -1.6740 | -1.8665 | |
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| 10202.4125 | 0.81 | 1500 | 4320.9878 | -0.9655 | -1.0271 | 0.6023 | 0.0617 | -0.9611 | -0.9618 | -195.1794 | -226.9775 | -1.7645 | -1.9420 | |
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| 11785.8336 | 0.97 | 1800 | 4320.8208 | -0.9417 | -1.0065 | 0.6027 | 0.0648 | -0.9369 | -0.9373 | -193.1151 | -224.6014 | -1.7745 | -1.9550 | |
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### Framework versions |
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- Transformers 4.36.2 |
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- Pytorch 2.1.2 |
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- Datasets 2.14.6 |
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- Tokenizers 0.15.0 |
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