This code snippet showcases the application of Gemma, a powerful language model, for natural language processing tasks. The following key steps are demonstrated:
Environment Setup: The code sets up the environment by installing necessary libraries and configuring the backend environment for efficient memory usage.
Data Loading and Preprocessing: Data is loaded from a JSONL file containing examples of instructions and responses. Preprocessing involves filtering out examples with context and formatting the data into a suitable structure.
Model Initialization and Summary: The Gemma model is initialized using a predefined preset. The architecture of the model, including tokenizer and layers, is summarized to provide insights into its structure.
Text Generation: The trained model is used to generate text responses for given instructions. This demonstrates the model's ability to understand prompts and generate coherent responses based on learned patterns.
Model Configuration and Training: The model's configuration is updated to enhance performance, including enabling Locally Reversible Architecture (LoRA) and adjusting sequence length. The model is compiled with appropriate loss and optimizer settings, followed by training on the provided data for one epoch.
This code exemplifies the utilization of GemmaCausalLM for natural language processing tasks, showcasing its capability to understand and generate text responses effectively.