LLM Engineering - Master AI and LLMs

Your 8 week journey to proficiency starts today

I'm so happy you're joining me on this path. We'll be building immensely satisfying projects in the coming weeks. Some will be easy, some will be challenging, many will ASTOUND you! The projects build on each other so you develop deeper and deeper expertise each week. One thing's for sure: you're going to have a lot of fun along the way.

A note before you begin

I'm here to help you be most successful with your learning! If you hit any snafus, or if you have any ideas on how I can improve the course, please do reach out in the platform or by emailing me direct (ed@edwarddonner.com). It's always great to connect with people on LinkedIn to build up the community - you'll find me here:
https://www.linkedin.com/in/eddonner/

An important point on API costs

During the course, I'll suggest you try out the leading models at the forefront of progress, known as the Frontier models. I'll also suggest you run open-source models using Google Colab. These services have some charges, but I'll keep cost minimal - like, a few cents at a time.

Please do monitor your API usage to ensure you're comfortable with spend; I've included links below. There's no need to spend anything more than a couple of dollars for the entire course. You may find that AI providers such as OpenAI requires a minimum credit like $5 for your region; we should only spend a fraction of it, but you'll have plenty of opportunity to put it to good use in your own projects. During Week 7 you have an option to spend a bit more if you're enjoying the process - I spend about $10 myself and the results make me very happy indeed! But it's not necessary in the least; the important part is that you focus on learning.

How this Repo is organized

There are folders for each of the "weeks", representing modules of the class, culminating in a powerful autonomous Agentic AI solution in Week 8 that draws on many of the prior weeks.
Follow the setup instructions below, then open the Week 1 folder and prepare for joy.

The most important part

The mantra of the course is: the best way to learn is by DOING. You should work along with me, running each cell, inspecting the objects to get a detailed understanding of what's happening. Then tweak the code and make it your own. There are juicy challenges for you throughout the course. I'd love it if you wanted to push your code so I can follow along with your progress, and I can make your solutions available to others so we share in your progress. While the projects are enjoyable, they are first and foremost designed to be educational, teaching you business skills that can be put into practice in your work.

Setup instructions

I should confess up-front: setting up a powerful environment to work at the forefront of AI is not as simple as I'd like. For most people these instructions will go great; but in some cases, for whatever reason, you'll hit a problem. Please don't hesitate to reach out - I am here to get you up and running quickly. There's nothing worse than feeling stuck. Message me, email me or LinkedIn message me and I will unstick you quickly!

The recommended approach is to use Anaconda for your environment. It's a powerful tool that builds a complete science environment. Anaconda ensures that you're working with the right version of Python and all your packages are compatible with mine, even if we're on different platforms.

Update Some people have had problems with Anaconda - horrors! The idea of Anaconda is to make it really smooth and simple to be working with the same environment. If you hit any problems with the instructions below, please skip to near the end of this README for the alternative approach using pip with virtualenv, and hopefully you'll be up and running fast. And please do message me if I can help with anything.

We'll be mostly using Jupyter Lab in this course. For those new to Jupyter Lab / Jupyter Notebook, it's a delightful Data Science environment where you can simply hit shift+return in any cell to run it; start at the top and work your way down! When we move to Google Colab in Week 3, you'll experience the same interface for Python runtimes in the cloud.

For Windows Users

1. Install Git (if not already installed):

• Download Git from https://git-scm.com/download/win
• Run the installer and follow the prompts, using default options

2. Open Command Prompt:

• Press Win + R, type cmd, and press Enter

3. Navigate to your projects folder:

If you have a specific folder for projects, navigate to it using the cd command. For example:
cd C:\Users\YourUsername\Documents\Projects

If you don't have a projects folder, you can create one:

mkdir C:\Users\YourUsername\Documents\Projects
cd C:\Users\YourUsername\Documents\Projects

(Replace YourUsername with your actual Windows username)

3. Clone the repository:

• Go to the course's GitHub page
• Click the green 'Code' button and copy the URL
• In the Command Prompt, type this, replacing everything after the word 'clone' with the copied URL: git clone <paste-url-here>

4. Install Anaconda:

• Download Anaconda from https://docs.anaconda.com/anaconda/install/windows/
• Run the installer and follow the prompts
• A student mentioned that if you are prompted to upgrade Anaconda to a newer version during the install, you shouldn't do it, as there might be problems with the very latest update for PC. (Thanks for the pro-tip!)

5. Set up the environment:

• Open Anaconda Prompt (search for it in the Start menu)
• Navigate to the cloned repository folder using cd path\to\repo (replace path\to\repo with the actual path to the llm_engineering directory, your locally cloned version of the repo)
• Create the environment: conda env create -f environment.yml
• Wait for a few minutes for all packages to be installed
• Activate the environment: conda activate llms

You should see (llms) in your prompt, which indicates you've activated your new environment.

6. Start Jupyter Lab:

• In the Anaconda Prompt, from within the llm_engineering folder, type: jupyter lab

...and Jupyter Lab should open up, ready for you to get started. Open the week1 folder and double click on day1.ipnbk.

For Mac Users

1. Install Git if not already installed (it will be in most cases)

• Open Terminal (Applications > Utilities > Terminal)
• Type git --version If not installed, you'll be prompted to install it

2. Navigate to your projects folder:

If you have a specific folder for projects, navigate to it using the cd command. For example: cd ~/Documents/Projects

If you don't have a projects folder, you can create one:

mkdir ~/Documents/Projects
cd ~/Documents/Projects

3. Clone the repository

• Go to the course's GitHub page
• Click the green 'Code' button and copy the URL
• In Terminal, type this, replacing everything after the word 'clone' with the copied URL: git clone <paste-url-here>

4. Install Anaconda:

• Download Anaconda from https://docs.anaconda.com/anaconda/install/mac-os/
• Double-click the downloaded file and follow the installation prompts

5. Set up the environment:

• Open Terminal
• Navigate to the cloned repository folder using cd path/to/repo (replace path/to/repo with the actual path to the llm_engineering directory, your locally cloned version of the repo)
• Create the environment: conda env create -f environment.yml
• Wait for a few minutes for all packages to be installed
• Activate the environment: conda activate llms

You should see (llms) in your prompt, which indicates you've activated your new environment.

6. Start Jupyter Lab:

• In Terminal, from within the llm_engineering folder, type: jupyter lab

...and Jupyter Lab should open up, ready for you to get started. Open the week1 folder and double click on day1.ipnbk.

When we get to it, creating your API keys

Particularly during weeks 1 and 2 of the course, you'll be writing code to call the APIs of Frontier models (models at the forefront of progress). You'll need to join me in setting up accounts and API keys.

• GPT API from OpenAI
• Claude API from Anthropic
• Gemini API from Google

Initially we'll only use OpenAI, so you can start with that, and we'll cover the others soon afterwards. The webpage where you set up your OpenAI key is here. See the extra note on API costs below if that's a concern. One student mentioned to me that OpenAI can take a few minutes to register; if you initially get an error about being out of quota, wait a few minutes and try again. Another reason you might encounter the out of quota error is if you haven't yet added a valid payment method to your OpenAI account. You can do this by clicking your profile picture on the OpenAI website then clicking "Your profile." Once you are redirected to your profile page, choose "Billing" on the left-pane menu. You will need to enter a valid payment method and charge your account with a small advance payment. It is recommended that you disable the automatic recharge as an extra failsafe. If it's still a problem, see more troubleshooting tips in the Week 1 Day 1 notebook, and/or message me!

Later in the course you'll be using the fabulous HuggingFace platform; an account is available for free at HuggingFace - you can create an API token from the Avatar menu >> Settings >> Access Tokens.

And in Week 6/7 you'll be using the terrific Weights & Biases platform to watch over your training batches. Accounts are also free, and you can set up a token in a similar way.

When you have these keys, please create a new file called .env in your project root directory. This file won't appear in Jupyter Lab because it's a hidden file; you should create it using something like Notepad (PC) or nano (Mac / Linux). I've put detailed instructions at the end of this README.

It should have contents like this, and to start with you only need the first line:

OPENAI_API_KEY=xxxx
GOOGLE_API_KEY=xxxx
ANTHROPIC_API_KEY=xxxx
HF_TOKEN=xxxx

This file is listed in the .gitignore file, so it won't get checked in and your keys stay safe. If you have any problems with this process, there's a simple workaround which I explain in the video.

Starting in Week 3, we'll also be using Google Colab for running with GPUs

You should be able to use the free tier or minimal spend to complete all the projects in the class. I personally signed up for Colab Pro+ and I'm loving it - but it's not required.

Learn about Google Colab and set up a Google account (if you don't already have one) here

The colab links are in the Week folders and also here:

• For week 3 day 1, this Google Colab shows what colab can do
• For week 3 day 2, here is a colab for the HuggingFace pipelines API
• For week 3 day 3, here's the colab on Tokenizers
• For week 3 day 4, we go to a colab with HuggingFace models
• For week 3 day 5, we return to colab to make our Meeting Minutes product
• For week 7, we will use these Colab books: Day 1 | Day 2 | Days 3 and 4 | Day 5

Monitoring API charges

You can keep your API spend very low throughout this course; you can monitor spend at the dashboards: here for OpenAI, here for Anthropic and here for Google Gemini.

The charges for the exercsies in this course should always be quite low, but if you'd prefer to keep them minimal, then be sure to always choose the cheapest versions of models:

1. For OpenAI: Always use model gpt-4o-mini in the code instead of gpt-4o
2. For Anthropic: Always use model claude-3-haiku-20240307 in the code instead of the other Claude models
3. During week 7, look out for my instructions for using the cheaper dataset

And that's it! Happy coding!

Alternative Setup Instructions if Anaconda is giving you problems

First please run: python --version
To find out which python you're on. Ideally you'd be using Python 3.11.x, so we're completely in sync. You can download python at
https://www.python.org/downloads/

Here are the steps:

After cloning the repo, cd into the project root directory llm_engineering. Then:

1. Create a new virtual environment: python -m venv venv
2. Activate the virtual environment with
   On a Mac: source venv/bin/activate
   On a PC: venv\Scripts\activate
3. Run pip install -r requirements.txt
4. Create a file called .env in the project root directory and add any private API keys, such as below. (The next section has more detailed instructions for this, if you prefer.)

OPENAI_API_KEY=xxxx
GOOGLE_API_KEY=xxxx
ANTHROPIC_API_KEY=xxxx
HF_TOKEN=xxxx

5. Run jupyter lab to launch Jupyter and head over to the intro folder to get started.

Let me know if you hit problems.

Guide to creating the .env file

For PC users:

1. Open the Notepad (Windows + R to open the Run box, enter notepad)

2. In the Notepad, type the contents of the file, such as:

OPENAI_API_KEY=xxxx
GOOGLE_API_KEY=xxxx
ANTHROPIC_API_KEY=xxxx
HF_TOKEN=xxxx

Double check there are no spaces before or after the = sign, and no spaces at the end of the key.

3. Go to File > Save As. In the "Save as type" dropdown, select All Files. In the "File name" field, type ".env". Choose the root of the project folder (the folder called llm_engineering) and click Save.

4. Navigate to the foler where you saved the file in Explorer and ensure it was saved as ".env" not ".env.txt" - if necessary rename it to ".env" - you might need to ensure that "Show file extensions" is set to "On" so that you see the file extensions. Message or email me if that doesn't make sense!

For Mac users:

1. Open Terminal (Command + Space to open Spotlight, type Terminal and press Enter)

2. cd to your project root directory

cd /path/to/your/project

(in other words, change to the directory like /Users/your_name/Projects/llm_engineering, or wherever you have cloned llm_engineering).

3. Create the .env file with

nano .env

4. Then type your API keys into nano:

OPENAI_API_KEY=xxxx
GOOGLE_API_KEY=xxxx
ANTHROPIC_API_KEY=xxxx
HF_TOKEN=xxxx

5. Save the file:

Control + O
Enter (to confirm save the file)
Control + X to exit the editor

6. Use this command to list files in your file

ls -a

And confirm that the .env file is there.

Please do message me or email me at ed@edwarddonner.com if this doesn't work or if I can help with anything. I can't wait to hear how you get on.