Weekend Project: Hugging Face 🤗

The AI community building the future...

By the End of This Newsletter, You’ll:

✅ Know exactly what Hugging Face is and why it’s important in the AI space.
✅ Learn how to download and run a model locally - Deepseek-R1 🐳
✅ Understand how to use the Transformers library - Level 1 For building AI applications and workflows.

Start here: What is Hugging Face?

Hugging Face is a free platform for AI and machine learning with:

• Pre-trained models

• User-friendly APIs

• Cloud integration

• An open-source ecosystem

• A vibrant community

It’s the best place to start with AI in my opinion, if you’re looking to move past chatbots.

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Let’s take a look:

First things first I encourage you to head over to the website and just start poking around. This is my approach to learning anything - Just get stuck in and if you break something, all the better.

Along the top, you’ll see some interesting tabs. Here’s what they do:

• Models: Thousands of pre-trained models for tasks like text generation, image recognition, and more.

• Datasets: Datasets curated for AI training and experimentation, covering everything from language to images.

• Spaces: Demos and custom AI apps built by the community, or host your own projects.

• Posts: Blog posts, news, and insights from the Hugging Face team and community.

• Docs: Documentation to help you get started or troubleshoot like a pro.

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Buzzwords Aside: How can I actually use it?

If you’re new to AI, the best starting point is downloading and running a model. This simple step lays the foundation for understanding how AI works.

We’ll start with Ollama, a lightweight tool that makes running LLM’s locally super easy - no coding or cloud setup required.

Head over to Ollama and download:

Run through the set-up wizard:

You’ll then be presented with a cmd to run your first model - let’s do this. Open up your terminal and copy pasta the cmd. This command tells Ollama to download and run the specified model, llama3.2

Pulling the model’s data (manifest and other files) may take a while, depending on your internet speed.

We now want to run the model we’ve just downloaded. How well you can run this is hardware dependant. You don't need a GPU to run this model but it will make it faster especially when you have at least 16GB of VRAM.

We can do this by running ollama run llama3.2
Tip: (cntrl + D) if you want to exit.

Easy as that! 🎉

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Running a custom model from Hugging Face

This is where the fun starts. Head on over to Models Tab on the hugging face website.

From here we can find some weird and wonderful models to download, lets’s download DeepSeek-R1, you’ve probably heard about Deepseek in the news recently.

We actually need the GGUF version to use with Ollama so go here: DeepSeek-R1-GGUF - GGUF is optimised for: Local inference (no cloud required) and has Faster loading and execution than older formats.

Click on the small square copy button next to the Model Title and paste this into your terminal:

ollama run hf.co/unsloth/DeepSeek-R1-Distill-Llama-8B-GGUF

Once it’s downloaded we can run it with:

Super simple right!? You now know how to find, download and run a model locally from Hugging Face! This is only the start…

Let’s take things to the next level 🚀

If you're looking to start and build your own AI powered applications or build and LLM into your existing workflows - This is a great starting point.

In this next part, i’ll get you started with Hugging Face’s Transformers library:

• What Transformers?

• How to install and set up the library.

• How to run your first model as part of a script.

By the end, you’ll be equipped with the skills to start building AI applications, not just using them. Let’s do this! 💡