--- 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 - alignment-handbook - generated_from_trainer datasets: - HuggingFaceH4/ultrafeedback_binarized model-index: - name: zephyr-7b-align-scan-0.0-0.0-cosine-2 results: [] --- # zephyr-7b-align-scan-0.0-0.0-cosine-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: 1.7260 - Rewards/chosen: -5.0723 - Rewards/rejected: -8.1169 - Rewards/accuracies: 0.3075 - Rewards/margins: 3.0445 - Logps/rejected: -601.5961 - Logps/chosen: -399.7386 - Logits/rejected: 4.1594 - Logits/chosen: 3.9982 ## 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: 8.19247218325348e-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: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Logits/chosen | Logits/rejected | Logps/chosen | Logps/rejected | Validation Loss | Rewards/accuracies | Rewards/chosen | Rewards/margins | Rewards/rejected | |:-------------:|:------:|:----:|:-------------:|:---------------:|:------------:|:--------------:|:---------------:|:------------------:|:--------------:|:---------------:|:----------------:| | 0.66 | 1.0417 | 100 | 2.2227 | 2.3998 | -231.4086 | -327.2254 | 0.6710 | 0.3155 | -0.1518 | 0.0862 | -0.2380 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.0 - Datasets 2.21.0 - Tokenizers 0.19.1