Fine tuning is a method of machine learning that uses a pre-trained, established model and adjusts or fine tunes the model to improve its accuracy for an intended use. This technique can be used to efficiently tailor an already existing model for specific datasets or tasks. The main goal of this process is to gain better performance from the model by updating weights and hyperparameters based on new data. It also allows developers to save time by avoiding extensive retraining activities each time they need a new model. Once the training process has been completed, any new data can quickly be integrated into the existing network in order to update it and ensure it remains up-to-date with changing requirements. This makes continuous fine tuning especially useful for applications such as object recognition, natural language processing and speech recognition. With this technique, developers can quickly adapt their model to new data without having to go through the entire training process from scratch. Consequently, it is a powerful tool for leveraging machine learning technology in dynamic environments where data is constantly changing. 

Datasets used for fine-tuning are significantly smaller than those used for initial pre-training, and thus fine-tuning step is not as computationally expensive. However, a model with 6B parameters doesn’t fit into GPU memory and is challenging even to fine-tune. And by challenging, we mean really expensive in time, hardware and expertise.  

 With Cerebras CS-2, we made fine-tuning easy to do. It runs on a single system – no need to think about how to fit the model and which libraries to use for distributed training across dozens or hundreds of ordinary computers. You will have total control over the fine-tuning process of this very large model without the usual pain of dealing with very large models.