Fine-tuning a LoRA Model to Create a Kansai-ben LLM and Publishing it on Hugging Face

This article details the process of fine-tuning a Large Language Model (LLM) to respond in the Kansai dialect of Japanese. It leverages the LoRA (Low-Rank Adaptation) technique on the Gemma 2 2B IT model, a high-performance open model developed by Google. The article focuses on the technical aspects of the fine-tuning process and the subsequent publication of the resulting model on Hugging Face. This approach highlights the potential of customizing LLMs for specific regional dialects and nuances, demonstrating a practical application of advanced AI techniques. The article's focus is on the technical implementation and the availability of the model for public use.

• •The article describes the creation of an LLM that responds in the Kansai dialect.
• •It utilizes the LoRA technique for efficient fine-tuning of the Gemma 2 2B IT model.
• •The resulting model is published on Hugging Face for public access.