Fine-Tuning Ollama Models with Unsloth
In the previous two articles, we explored Host Your Own Ollama Service in a Cloud Kubernetes (K8s) Cluster and Run Your Own OLLAMA in Kubernetes with Nvidia GPU. By now, you should have a decent LLM service hosted by yourself. In this article, we will delve into the fine-tuning process of Ollama models using Unsloth, using Llama3.1 as an example. The same process applies to other models.
Fine-tuning is a process in LLM where a pre-trained model is further trained on a specific dataset to adapt it to a particular task or domain. It’s akin to taking a chef who knows general cooking techniques and training them specifically to cook Italian cuisine.
Fine-tuning enables you to customize a Large Language Model’s (LLM) responses to fit your preferred tone or adapt it to follow domain-specific instructions. This allows the model to leverage the general knowledge it has already acquired while becoming more specialized in the new domain.
RAG (Retrieval-Augmented Generation) is a technique that combines the capabilities of large language models with information retrieval systems. It retrieves relevant documents or data from an external database and uses them to generate more accurate and contextually appropriate responses. It’s like a student who, before answering a question, looks up the most relevant books and articles to provide a well-informed answer.
