--- library_name: transformers license: apache-2.0 base_model: alignment-handbook/zephyr-7b-sft-full tags: - alignment-handbook - trl - dpo - generated_from_trainer - trl - dpo - generated_from_trainer datasets: - HuggingFaceH4/ultrafeedback_binarized model-index: - name: zephyr-7b-align-scan-0.0-0.4-polynomial-2 results: [] --- # zephyr-7b-align-scan-0.0-0.4-polynomial-2 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 HuggingFaceH4/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set: - Loss: 0.8224 - Rewards/chosen: -0.8205 - Rewards/rejected: -1.9403 - Rewards/accuracies: 0.3433 - Rewards/margins: 1.1198 - Logps/rejected: -85.6178 - Logps/chosen: -76.3898 - Logits/rejected: -2.4242 - Logits/chosen: -2.4424 ## 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: 8.772160997458124e-07 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: polynomial - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | |:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:| | 0.5775 | 1.0417 | 100 | 0.7712 | 0.7556 | 0.1333 | 0.3313 | 0.6223 | -80.8200 | -72.7430 | -2.5200 | -2.5356 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.0 - Datasets 2.21.0 - Tokenizers 0.19.1