--- license: apache-2.0 library_name: peft tags: - trl - dpo - generated_from_trainer base_model: alignment-handbook/zephyr-7b-sft-full model-index: - name: zephyr-7b-dpo-lora-pubmedqa-selfgen-ultrafeedback-old results: [] --- # zephyr-7b-dpo-lora-pubmedqa-selfgen-ultrafeedback-old This model is a fine-tuned version of [alignment-handbook/zephyr-7b-sft-full](https://huggingface.co/alignment-handbook/zephyr-7b-sft-full) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5601 - Rewards/chosen: -0.4096 - Rewards/rejected: -0.9163 - Rewards/accuracies: 0.7000 - Rewards/margins: 0.5067 - Logps/rejected: -348.5905 - Logps/chosen: -331.9001 - Logits/rejected: -1.5161 - Logits/chosen: -1.5640 ## 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-06 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - total_eval_batch_size: 4 - 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 | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | |:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:| | 0.5074 | 0.39 | 3000 | 0.5837 | -0.4893 | -0.8886 | 0.6940 | 0.3993 | -345.8194 | -339.8665 | -1.8683 | -1.8925 | | 0.5332 | 0.79 | 6000 | 0.5604 | -0.4169 | -0.9240 | 0.7040 | 0.5071 | -349.3569 | -332.6285 | -1.5156 | -1.5626 | ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.2