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

Transcribed Jun 14, 2026 Watch on YouTube ↗

Intermediate 2 min read For: Developers and ML enthusiasts with basic knowledge of Python and LLMs.

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AI Summary

This video demonstrates how to fine-tune a large language model (LLM) using Unsloth and Llama 3.1, then run it locally with Ollama. The project focuses on creating a model that generates SQL from table data, using the synthetic text-to-SQL dataset.

Chapters

1 Introduction and Dataset Selection 0:00 2 Hardware and Tools Overview 0:45 3 Environment Setup 1:18 4 Loading the Model and PEFT 1:48 5 Data Formatting and Training 2:57 6 Conversion and Ollama Deployment 3:59

[0:14]

Importance of dataset selection

Choosing a relevant dataset allows a small LLM to outperform larger models on specific tasks.

[0:26]

Project goal

Create a small, fast LLM that generates SQL based on provided table data.

[0:33]

Dataset used

Synthetic text-to-SQL dataset with over 105,000 records, including prompt, SQL content, complexity, and more.

[1:01]

Tools used

Unsloth for efficient fine-tuning (80% less memory) and Llama 3.1 as the base model.

[1:48]

Setup steps

Install dependencies, create a conda environment, install PyTorch, CUDA, Unsloth, and Jupyter.

[1:55]

Loading the model

Import FastLanguageModel from Unsloth, specify Llama 3.1 8-bit, max sequence length 2048, load in 4-bit.

[2:35]

PEFT and LoRA adapters

Load PEFT model with LoRA adapters to update only 1-10% of parameters, reducing training cost.

[3:02]

Data formatting

Format the dataset into Alpaca prompt style for Llama 3.1, focusing on SQL, prompt, and explanation.

[3:34]

Training setup

Use SFTTrainer from Hugging Face with parameters like max steps, seed, and warmup steps.

[4:00]

Conversion and Ollama deployment

Convert the trained model using Unsloth's one-liner, create a Modelfile, and run with Ollama.

By following these steps, you can fine-tune an LLM locally and deploy it with Ollama, enabling use via an OpenAI-compatible API.

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Mentioned in this Video

Unsloth

tool

Ollama

tool

Llama 3.1

tool

Anaconda

tool

CUDA

tool

Jupyter

tool

Hugging Face SFTTrainer

tool

Warp terminal

tool

Google Colab

service

Tutorial Checklist

1 1:18 Install Anaconda and CUDA libraries (CUDA 12.1, Python 3.10).

2 1:33 Create a new conda environment and install PyTorch, CUDA, Unsloth, and Jupyter.

3 1:48 Launch Jupyter notebook and verify installed packages.

4 1:55 Import FastLanguageModel from Unsloth and load Llama 3.1 8-bit model with max_seq_length=2048 and load_in_4bit=True.

5 2:35 Load PEFT model with LoRA adapters using Unsloth's recommended settings.

6 3:02 Format your dataset into Alpaca prompt style for Llama 3.1.

7 3:34 Set up SFTTrainer with parameters like max_steps, seed, and warmup_steps, then train the model.

8 4:00 Convert the trained model using Unsloth's one-liner.

9 4:15 Create a Modelfile with a system prompt (e.g., 'You are an SQL generator...').

10 4:38 Run 'ollama create' command to create the model, then use it locally.

Study Flashcards (7)

What is the main benefit of using Unsloth for fine-tuning?

easy Click to reveal answer

It reduces memory usage by about 80%.