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

#AI Tools
2025-09-01 18:309 min read

Content Introduction

This video tutorial guides viewers through the process of fine-tuning a large language model (LLM) locally using UNS Sloth and Llama 3. It emphasizes the importance of selecting the right dataset, introduces the synthetic text to SQL dataset, and explains how to set up the necessary environment on a machine with an Nvidia GPU or through Google Colab. The presenter covers the tools and libraries required for the setup and demonstrates how to format prompts for generating SQL code from the model. Viewers will learn about the supervised fine-tuning process, including setting parameters and using adapters to streamline the training without the need for retraining the entire model. Finally, the video shows how to run the model locally using Olama and provides additional resources for further learning.

Key Information

  • The video discusses fine-tuning a large language model (LLM) and running it locally on your machine.
  • The importance of choosing the right dataset is highlighted, as it can allow smaller models to outperform larger ones.
  • The tutorial involves creating a small, fast LLM that generates SQL data based on a synthetic text dataset.
  • The presenter uses a Nvidia 4090 GPU and Ubuntu for the setup but mentions that Google Colab can also be used for those without a GPU.
  • Installation of dependencies and tools like UNS Sloth for efficient fine-tuning is emphasized.
  • The setup involves configuring the environment with Anaconda, Cuda 12.1, and Python 3.10.
  • Parameters for the training module include key configurations for training steps and seed generation.
  • Additional steps include converting the trained model for local running with Olama and creating model configuration files.
  • The final model allows local use of an SQL generator based on user queries, integrating with the OpenAI compatible API.

Timeline Analysis

Content Keywords

Fine-tune Language Model

The video explains how to fine-tune a large language model and run it locally on your machine.

Data Set Importance

It emphasizes the importance of finding the right data set for training a small language model, which can outperform larger models.

Synthetic Text to SQL

The speaker mentions using a dataset called 'synthetic text to SQL,' which has over 105,000 records to generate SQL data.

Nvidia 4090 GPU

The tutorial uses an Nvidia 4090 GPU and Ubuntu for the training process, with alternatives like Google Colab for those without a GPU.

UNS Sloth

UNS Sloth is introduced as a tool that allows efficient fine-tuning of open-source models with reduced memory usage.

Llama 3

The tutorial uses Llama 3, a commercial and research language model known for high performance, for model training.

CUDA and Python

The speaker mentions using CUDA 12.1 and Python 3.10 for the project, along with Anaconda and other dependencies required for the setup.

Jupyter Notebook

Once the setup is complete, the users are directed to run their Jupyter notebooks to check for installed requirements.

Fine-tuning Trainer

The process involves using a fine-tuning trainer from Hugging Face, with parameters explained in separate videos.

Model Configuration

Towards the end, the speaker guides viewers on how to configure a model file to generate SQL queries based on user input.

Olama Usage

The tutorial concludes with instructions on using Olama to run locally deployed models and encourages viewers to check out additional resources.

Related questions&answers

What is the purpose of fine-tuning a large language model?

Fine-tuning a large language model allows it to adapt to specific tasks or domains, improving its performance on relevant data.

Can I run a fine-tuned model on my local machine?

Yes, you can run a fine-tuned model on your local machine, provided it meets the hardware and software requirements.

What are the hardware requirements for fine-tuning models?

For fine-tuning, it's recommended to use a powerful GPU, like the Nvidia 4090, but you can also use cloud services like Google Colab for less powerful setups.

What is the significance of selecting the right dataset?

Choosing the right dataset is crucial as it can enhance the model's ability to perform well in specific tasks, sometimes even outperforming larger models.

What software do I need to run the fine-tuning process?

You will need a compatible operating system (like Ubuntu), Anaconda, Python, and any specific libraries required by your project.

What is UNS Sloth and how does it help in fine-tuning?

UNS Sloth is a tool that allows you to fine-tune multiple open-source models efficiently, reducing memory usage significantly.

Why is using 4-bit representation beneficial?

Using 4-bit representation reduces memory usage while maintaining performance by using fewer bits than the typical 16 or 32 bits.

What is the role of the PFT model?

The PFT model, which includes Laura adapters, helps in fine-tuning by minimizing the number of parameters that need to be updated.

How do I run my fine-tuned model locally?

After setting up your environment and creating the necessary model configuration file, you can run the model using command line instructions in your terminal.

What should I do if I want to generate SQL queries with my model?

You need to set up your model to recognize the SQL format and provide relevant prompts for generating SQL queries based on user inputs.

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