In this video, we go over how you can fine-tune Llama 3.1 and run it locally on your machine using Ollama! We use the open source repository "Unsloth" to do all of the fine tuning on an SQL dataset!

Throughout this video, we discuss the ins and outs of what fine-tuning an LLM is, how you can work with your data so that the LLM can process it and then importing it into Ollama for you to run locally on your machine!

Ollama is available on all platforms!

Dataset used: https://huggingface.co/datasets/gretelai/synthetic_text_to_sql
Ollama: https://github.com/ollama/ollama
Unsloth: https://github.com/unslothai/unsloth

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TIMESTAMPS
0:00 Intro
0:10 Getting the dataset
0:45 The Tech Stack
1:18 Installing Dependencies
1:48 Fast Language Model Explained
2:35 LORA Adaptes Explained
3:02 Converting your data to fine-tune
3:36 Training the Model....
4:01 Converting to Ollama compatibility
4:11 Creating a Modelfile for Ollama
4:50 Final Output!
5:01 Check out Ollama in 2 minutes!

YouTube

Learn how to fine-tune a large language model (LLM) on your local machine using Ollama and Unsloth. By using the synthetic text-to-SQL dataset and tools like Anaconda, CUDA libraries, and Jupyter notebook, you'll set up an environment to train a small but effective LLM. Benefits include reduced memory usage and easy deployment with Ollama. Ideal for users with or without a GPU, offering practicality using platforms like Google Colab for cloud-based training.

EASIEST Way to Fine-Tune a LLM and Use It With Ollama