--- license: apache-2.0 library_name: peft tags: - alignment-handbook - generated_from_trainer - trl - dpo base_model: alignment-handbook/zephyr-7b-sft-full datasets: - updated - original model-index: - name: zephyr-7b-lora-64-no-quant-6k results: [] --- # zephyr-7b-lora-64-no-quant-6k 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 updated and the original datasets. It achieves the following results on the evaluation set: - Loss: 0.5788 - Rewards/chosen: -0.3940 - Rewards/rejected: -0.7469 - Rewards/accuracies: 0.7200 - Rewards/margins: 0.3529 - Logps/rejected: -332.2092 - Logps/chosen: -323.4424 - Logits/rejected: -2.2597 - Logits/chosen: -2.3729 ## 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: 4 - gradient_accumulation_steps: 32 - total_train_batch_size: 256 - total_eval_batch_size: 8 - 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 | |:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:| | 0.5916 | 0.42 | 100 | 0.6025 | -0.2538 | -0.5140 | 0.6940 | 0.2602 | -308.9146 | -309.4196 | -2.5166 | -2.6027 | | 0.5667 | 0.84 | 200 | 0.5788 | -0.3940 | -0.7469 | 0.7200 | 0.3529 | -332.2092 | -323.4424 | -2.2597 | -2.3729 | ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.2