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---
tags:
- trl
- dpo
- generated_from_trainer
model-index:
- name: dpo-selective-buffer-safeipo
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# dpo-selective-buffer-safeipo
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4322.0576
- Rewards/chosen: -0.9426
- Rewards/rejected: -1.0072
- Rewards/accuracies: 0.6033
- Rewards/margins: 0.0646
- Rewards/safe Rewards: -0.9377
- Rewards/unsafe Rewards: -0.9382
- Logps/rejected: -193.1814
- Logps/chosen: -224.6856
- Logits/rejected: -1.7714
- Logits/chosen: -1.9525
## 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: 5e-07
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------------:|:----------------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2
- Datasets 2.14.6
- Tokenizers 0.15.0
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