Fine-Tune Llama 3.1 with Ollama

In this video, ‘warpdotdev’ provides a detailed tutorial on how to fine-tune the Llama 3.1 model and run it locally using Ollama. The presenter emphasizes the importance of selecting the right dataset for effective training, demonstrating the use of the ‘synthetic text to SQL’ dataset with over 105,000 records. The tutorial covers the installation of necessary dependencies, including Anaconda and CUDA libraries, and introduces the Unsloth framework, which allows for efficient fine-tuning with reduced memory usage. Viewers learn how to prepare their data, set up the training environment, and utilize Lora adapters to minimize the need for extensive retraining. The video also explains how to convert the fine-tuned model for compatibility with Ollama, enabling users to run their custom LLMs locally. By the end of the tutorial, viewers gain a comprehensive understanding of the fine-tuning process and how to effectively deploy their models for various applications.