Appliance Mode for LLM Training

Appliance Mode simplifies and streamlines the process of training large language models (LLMs) on Cerebras Wafer-Scale Clusters. It abstracts away the complexities of distributed job launching, resource management, and parameter tuning, allowing users to focus on model research and results. This mode supports both pipelined and weight streaming execution, enabling training of models from small to extremely large sizes with minimal code changes. It integrates seamlessly with standard Python environments and allows for push-button linear scaling up to 192 CS-2 systems.