zephyr-7B-alpha-GPTQ

Model description

Large language models have achieved groundbreaking success in the field of natural language processing (NLP). However, since these models are generally trained for general-purpose tasks, they may not perform optimally for specific tasks. Therefore, fine-tuning these large models for specific tasks is a common practice. In this article, we will delve into the process of fine-tuning and adapting the Zephyr-7B-alpha-GPTQ, a large language model, for a particular task.

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0002
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• training_steps: 250
• mixed_precision_training: Native AMP

Framework versions

• PEFT 0.10.0
• Transformers 4.38.2
• Pytorch 2.2.1+cu121
• Datasets 2.18.0
• Tokenizers 0.15.2

Author

• Anezatra Katedram