How to Use DeepSeek

Introduction

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⚡️ 30 second Summary

Welcome to this project on DeepSeek 🐋

You might've seen the news that DeepSeek, a company that develops Large Language Models (LLMs), has shaken up the AI industry. Their latest model, DeepSeek R1, is now OpenAI’s closest competitor in performance - and it’s open-source. In a nutshell, this means you have access to one of the most advanced LLMs in the world for free.

How can you make the most of this new opportunity? Should you start using DeepSeek, or is your current LLM still the better fit for you?

This project will show you how to explore DeepSeek, get hands on with its features, and decide whether this LLM should be your new favourite.

In this project, get ready to...

• 🧠 Test a range of prompts with DeepSeek.
• 🦙 Install Ollama and Chatbox to host DeepSeek locally.
• 🌡️ Customize DeepSeek to match your preferences and hardware.
• 💎 Review DeepSeek vs. ChatGPT - who's better?

Want a complete demo of how to do this project, from start to finish? Check out our 🎬 walkthrough with Natasha 🎬

If you're up for a bit of a challenge, quiz yourself on the key concepts up ahead in this project.

Use DeepSeek On Your Browser

We’ll start with the easiest way to use DeepSeek - its web app.

If you’ve used ChatGPT or any other LLM in your browser, this will feel familiar. But, as we dive into more advanced prompts, you’ll start noticing what makes DeepSeek stand out.

In this step, you're going to:

• Create a DeepSeek account.
• Run some fun prompts and see what makes DeepSeek stand out.
• Spot some limitations of DeepSeek on your browser.

Create a DeepSeek Account

• Open your web browser and head to DeepSeek's website.

• Enter your details, like your email address and password.

I don't want to enter my email

You could consider using a temporary email address instead.

For example, you could use a tool like TempMail to set up a temporary email address that receives mail for 1-2 hours, and deletes itself right after. Just make sure to keep this tab open until the end of the project, so you don't lose the inbox.

• Select Send code.

• Check your inbox - you should get a code sent to your inbox.

I'm not getting a code

You might not get a code if DeepSeek's registration is busy. You could try signing up with Google instead.

If Google isn't an option or isn't working either, you could skip this step for now and head to the next step. The first step is great for easy access to DeepSeek, but you can always come back to it later!

• Enter the code in the #Code section of the sign-up form.
• Select Sign Up.

Run a Prompt

• Once you're logged in, enter your first prompt:

Summarize the fall of the Roman Empire using only text abbreviations and emojis.

Tip

You can choose another prompt to give DeepSeek. We'd recommend a short and easy request (e.g. ask for a 100 word summary instead of a 1000 word essay), so you won't be waiting too long for an answer!

• Select Send (or press Enter on your keyboard).
• Nice! DeepSeek gives us an answer right away.

• If you've used other AI-powered chatbot like ChatGPT or Claude, DeepSeek might feel pretty familiar. Great! That's because the web app uses DeepSeek's V-3 model by default, which is not DeepSeek's latest model, called R1. Let's try using R1 now.
• Select the DeepThink (R1) button in your chat.

• Enter the same prompt again. You can stay in the same chat window.

Summarize the fall of the Roman Empire using only text abbreviations and emojis.

• Woah! This time, you can see DeepSeek's thinking process before it writes an answer.

What is DeepThink (R1)?

DeepThink (R1) is DeepSeek's latest AI model. It stands out for displaying its real-time reasoning process before generating a response.

Reasoning is a big deal in LLMs. Unlike older models that simply predict the next word in a sequence, DeepThink - as well as OpenAI’s o1 model - is one of the first models to actively reviews its own responses as it generates them.

In R1's thinking process, spot for intermediate thinking steps like self-doubt ("hmm") and verification checks ("wait"), which gives you a lot of transparency into its problem-solving approach. Reasoning also makes an LLM much more efficient - they're more likely to solve a problem in one go, without requiring lots of back and forth between you and the LLM.

Test DeepSeek vs ChatGPT on Advanced Reasoning

Let's challenge DeepSeek to a harder prompt that requires more reasoning, and see how it compares with another LLM (e.g. OpenAI).

Congratulations

Secret Mission unlocked!

• At the bottom of your current chat, select New chat. This opens a new chat window with DeepSeek, so you can start over with a new request.
• Input the following prompt:

Write a python script for a bouncing yellow ball within a square.

Make sure the square is slowly rotating. Make sure the ball stays within the square but is bouncing inside and doesn't escape the square.

Why are we using this prompt?

This Python prompt combines multiple complex requirements: real-time animation, rotation mathematics (rotating square), and collision detection (the bouncing ball must stay inside the square).

An LLM like DeepSeek would need a very deep understanding of animation with Python and physics principles to do this right.

In more technical terms, the model must generate mathematically correct coordinate transformations while maintaining game loop logic. This makes it a deep reasoning challenge for the LLM - how can you get both shapes to move correctly, and combine them in a functioning Python script?

• While you're waiting for DeepSeek's response, open ChatGPT (or your choice of LLM).

• Enter the same prompt.

Write a python script for a bouncing yellow ball within a square.

Make sure the square is slowly rotating. Make sure the ball stays within the square but is bouncing inside and doesn't escape the square.

• Select Send (or press Enter).
• Unlike DeepSeek R1, you might notice that ChatGPT wrote a response instantly.

How did I get an answer straight away from ChatGPT?

OpenAI's strengths are in its big cloud infrastructure - which means there are lots of servers in the background processing requests and giving rapid responses.

DeepSeek currently doesn't have the same level of infrastructure, but they prioritize transparent reasoning instead. R1's real-time visualization adds time, but is a great choice if you're wanting to solve a complex problem (instead of getting a quick answer).

• In terms of speed, it looks like ChatGPT is the winner and gives you something to work with right away!
• Next up, we'll put the quality and accuracy of their responses to the test.
• Copy the code provided by ChatGPT.

• Open Trinket, a tool that lets you run Python code live.
• Paste ChatGPT's code into the code window.

• Select Run.

• Watch ChatGPT's code render!

What do you think - is it accurate? Do you see a rotating square, with a bouncing circle inside?

In this example, the square is rotating... but there's no bouncing ball inside.

ChatGPT's code worked for me!

This is totally possible! AI models do not always generate the same response at every run, so you might find that ChatGPT's code works. That's part of the art and science of LLMs (and also why reviewing which one is 'better' is not a straightforward task).

• Switch tabs back to DeepSeek.
• You might notice that DeepSeek is still generating its answer. If that's the case, you'll have to keep waiting until the response has finished generating.
• When it's ready, copy DeepSeek's code.

• Switch tabs back to Trinket.

• Select Stop to stop the current rendering of ChatGPT's code.

• Replace the code in the code window with DeepSeek's code.
• Select Run again.
• What do you see in the rendering now - how does it compare with ChatGPT's code?

In this example, the render is showing the perfect result. There's a rotating circle, with a bouncing ball inside.

What does this tell us about DeepSeek vs ChatGPT?

In a nutshell, while ChatGPT gave you a response in a much shorter amount of time, DeepSeek's R1 model prioritizes logic and reasoning - and aims to give you the correct answer from its first go.

🙋‍♀️ I'm actually seeing the same results, or ChatGPT's code was better!

That's a well-deserved point to ChatGPT, if its code worked better!

If that's the case, it tells you that OpenAI and DeepSeek's models are truly neck and neck - there is no consistently better model between the two, and it might be DeepSeek's lower pricing (at a similar performance level) that makes it a big competitor to OpenAI.

Self Host DeepSeek

Now that you have a feel of how DeepSeek works, let's see how we can host it locally without relying on the web app.

What are the downsides of using a web app?

Ooo good question! Web-based LLMs, like ChatGPT and DeepSeek online:

1. Require constant internet connection (you can't go offline)
2. Introduce latency (web apps run slower when there are lots of people sending requests)
3. Process queries through external servers - which might raise privacy concerns around how the data is stored and used.

💡 What does it mean to host DeepSeek locally?

Running DeepSeek on your own computer means you don’t need the web app at all. Your device does all the processing, so no external servers are involved.

That means you can use DeepSeek offline, keep all your data private, and get faster responses since there's no waiting on the internet.

In this step, you're going to:

• Install a tool (called Ollama) that lets you host DeepSeek on your computer.

Download Ollama

Note

Note: If you already have Ollama installed, you can skip ahead to the next step.

• Visit Ollama.

What is Ollama?

Ollama is a tool that makes it easy to host LLMs, like DeepSeek, on your own computer. You can start chatting with LLMs over your computer's terminal!

Ollama takes care of downloading, installing, and running the models, so you don't have to worry about the complex setup that comes with hosting an LLM locally.

Ollama also gives you more control around the LLM you're using. We'll experiment with a setting called temperature later in this project to see the benefits of having wider control.

• Select the Download button at the centre of the page.
• Select your operating system.

• Select the Download option for your operating system.
• While we're waiting for Ollama to download...

Install Ollama

Next up, installing Ollama! Installation instructions depend on your operating system.

Haven't I already installed Ollama?

So far you've just downloaded Ollama's installation files, which means Ollama is like a package that's been delivered to your door - but you haven't opened the package yet.

You'll need to open the package and set up permissions to start using Ollama's software in your computer.

MacOS

• Open your Mac's Downloads folder.
• Select the Ollama zip file.

• Open the Ollama app.

• Select Move to Applications.

Nice! Now Ollama will take you through the process of installing the software locally.

• Select Install.

• Enter the password you use to unlock your Mac. This gives Ollama the permission to install the software into your computer.
• In the next panel, we'll ignore the command that Ollama gives us. The command lets us use another open-source LLM, but we'll go straight to using DeepSeek.
• Open your Terminal

Windows

• After downloading the executable file, simply run it, and Ollama will be installed automatically.
• If you're stuck or need any troubleshooting, we'd recommend checking out the Windows instructions on Ollama's GitHub.

Linux

Note

For the most up to date instructions, we'd recommend visiting Ollama's GitHub

• To install Ollama, run the following command:

curl -fsSL https://ollama.com/install.sh | sh

Manual install

Note

[!NOTE] If you are upgrading from a prior version, you should remove the old libraries with sudo rm -rf /usr/lib/ollama first.

Download and extract the package:

curl -L https://ollama.com/download/ollama-linux-amd64.tgz -o ollama-linux-amd64.tgz
sudo tar -C /usr -xzf ollama-linux-amd64.tgz

Start Ollama:

ollama serve

In another terminal, verify that Ollama is running:

ollama -v

AMD GPU install

If you have an AMD GPU, also download and extract the additional ROCm package:

curl -L https://ollama.com/download/ollama-linux-amd64-rocm.tgz -o ollama-linux-amd64-rocm.tgz
sudo tar -C /usr -xzf ollama-linux-amd64-rocm.tgz

ARM64 install

Download and extract the ARM64-specific package:

curl -L https://ollama.com/download/ollama-linux-arm64.tgz -o ollama-linux-arm64.tgz
sudo tar -C /usr -xzf ollama-linux-arm64.tgz

Adding Ollama as a startup service (recommended)

Create a user and group for Ollama:

sudo useradd -r -s /bin/false -U -m -d /usr/share/ollama ollama
sudo usermod -a -G ollama $(whoami)

Create a service file in /etc/systemd/system/ollama.service:

[Unit]
Description=Ollama Service
After=network-online.target

[Service]
ExecStart=/usr/bin/ollama serve
User=ollama
Group=ollama
Restart=always
RestartSec=3
Environment="PATH=$PATH"

[Install]
WantedBy=default.target

Then start the service:

sudo systemctl daemon-reload
sudo systemctl enable ollama

• Run the following command to confirm you've installed Ollama correctly:

ollama --version

• If Ollama is installed correctly, this command will print the version of Ollama you installed. Great success!

Access DeepSeek in the Terminal

Now that we've installed Ollama, how do we use it to access DeepSeek locally?

In this step, you're going to:

• Find different DeepSeek models in Ollama.
• Use DeepSeek in your local terminal.
• Run some test prompts.

Find and Install DeepSeek R1

• In a new tab, head to the Ollama library.

• Before we look for DeepSeek, search for OpenAI

Why can't I find OpenAI's models on Ollama?

Ollama focuses on open-source models like DeepSeek.

OpenAI's models are closed systems, so the underlying architecture, codebase, and datasets used to develop OpenAI models are confidential. Because they're confidential, it's not possible to use OpenAI locally in your machine.

• Search for and select deepseek-r1

• Select the dropdown labelled 1.5b.

What are these different dropdown options?

The different dropdown options represent different model sizes for R1. Think of DeepSeek R1 in the web app as R1 at full capacity - if you wanted to run this version of R1 locally, you would need a computer with very large processing power and storage space (the dropdown tells us it requires 404GB of storage). This is far beyond what most computers can handle, as computers typically have less available storage and memory.

Model sizes let you choose a smaller, more accessible version of DeepSeek R1 for local use.

Smaller models (like 1.5b) are faster and require less memory to run locally, while larger models (like 8b) have deeper reasoning abilities and are more accurate. We're installing 1.5b first as a quick start, but we'll use a larger model next to the difference in performance.

💡 What does "1.5b" mean?

In AI models like DeepSeek, "1.5 b" means the model has 1.5 billion parameters to learn patterns from data.

Think of parameters as tiny decision-makers inside the model, each helping it recognize patterns, analyze data, and improve reasoning. More parameters generally mean the model can handle more complex tasks, but bigger isn’t always better - it also depends on how well the model is trained.

• Copy the Ollama command to the right of the model dropdown.

What does this command do?

This command sets up DeepSeek's smallest model, i.e. the 1.5 billion parameter model, locally in your computer. Because the command uses run, your terminal will transform into a chat session with DeepSeek R1 too.

• Head back to your computer's terminal.

• Paste the code in a new line, and select Enter on your keyboard.

• Ollama will take a minute to install 1.5b.
• While we wait...

Extra for Experts: The terminal response starts by 'pulling manifest' - what does that mean?

When you run the Ollama command, it fetches the DeepSeek model's manifest, which is like a blueprint that tells your computer how to set up and run the model. It includes instructions for downloading and configuring everything correctly.

The actual brain of DeepSeek is the model itself, which gets downloaded after the manifest. Think of the manifest as the setup guide, while the model is the intelligence your computer will use to process prompts and generate responses.

• When you're ready, you'll see a prompt that tells you to send a message.
• Say less! Enter your first prompt for DeepSeek over the terminal.
• Enter Hello

• Press Enter on your keyboard.
• Woohooo! Well done - DeepSeek has just responded to your request.

• Enter /bye in the terminal. This will end your session with DeepSeek.

Use Another DeepSeek R1 Model

• Head back to the Ollama tab on DeepSeek R1.
• Select another model that your computer can run with its storage capacity.

🙋‍♀️ How do I know how much storage my computer has?

MacOS

• Click the Apple logo in the top-left corner of your screen.
• Select About This Mac.
• Navigate to the Storage tab.
• You'll see a breakdown of your storage usage and available space.

Windows

• Option 1: Settings
  • Click on the Start menu and select Settings (the gear icon).
  • Navigate to System > Storage.
  • You'll see a breakdown of your storage usage and available space.
• Option 2: File Explorer
  • Open File Explorer.
  • Click on This PC in the left sidebar.
  • Under Devices and drives, you'll see your storage devices with their available space.

Linux

• Option 1: Terminal Command
  • Open a terminal window.
  • Type the following command and press Enter: df -h
  • This will display a list of your file systems along with their used and available space.
• Option 2: Disk Usage Analyzer
  • Use the Disk Usage Analyzer tool (also known as Baobab).
  • If it's not installed, you can install it using your package manager.
  • Once opened, it provides a graphical representation of your disk usage.

If you're stuck picking a model size, we'd recommend going for the 8b option. If there are any issues with using it, you can always switch to the 7b option instead.

• Back in your terminal, run the command that starts your larger DeepSeek model.

Test Prompts

• This time, let's try a more complex prompt and do this offline.
• Turn your computer's Wifi off - and remember to turn it back on after running the prompt below!
• Type Can I use DeepSeek while offline? and press Enter on your keyboard.

• Nice, you'll see that DeepSeek can still craft a response - even when Wifi is off!

Why can I still access DeepSeek while offline?

Local hosting through Ollama means you don't need another server to process your prompt. The DeepSeek model is running entirely on your device without needing internet connectivity.

💡 What are the <think> tags?

The <think> tags are a terminal version of DeepSeek's real-time reasoning display, so you can still see how DeepSeek is generating its response.

You might've noticed that the the think tags were empty in your previous request. That's because Hello was a more straightforward prompt, so deep thinking (which triggers this real-time reasoning display) wasn't required.

• Enter /bye in the terminal. This will end your session with DeepSeek.
• Turn Wifi back on.

<!--

Distilled DeepSeek Models

Now that we have Ollama and the DeepSeek models installed, let's run DeepSeek directly in our terminal.

In this step, you're going to:

• Start another DeepSeek model in the terminal.
• Run some test prompts.

-->

Use Chatbox with Ollama

While the terminal is great for quick tests, you might miss the look of the web app. It does a much better job of organizing your chats and making conversations user friendly!

No worries, you can use a tool called Chatbox to organize your conversations in the terminal to look like the web app too. Let's set that up!

In this step, you're going to:

• Install Chatbox.
• Connect Chatbox to Ollama.
• Chat with both DeepSeek models.

Install and Configure Chatbox

• Visit the Chatbox home page.

• Select Download.

• Select your operating system.

How do I find the correct option for my operating system?

• 🍎 MacOS: Select the Apple icon from the top left hand corner of your computer's menu bar. Select About this Mac, and note whether your Chip says Apple (pick Apple Silicon) or Intel.
• 🖼️ Windows: Select the Start button and search for System Information. Note whether your System Type says x64-based or ARM-based PC.
• 🐧 Linux: Open a terminal and run uname -m. Match the output to the correct Linux package option.

• Open Chatbox in the Downloads folder.

• Add Chatbox to your Applications folder.

• Open the Chatbox app.
• Open your Chatbox Settings in the bottom left hand corner.

• Select Ollama API as the Model provider.
• Keep the default API Host.

What does the Model provider setting do?

In Chatbox, the Model provider determines the API that will connect you to the LLM model you want to use. We're using the Ollama API, since Ollama is the tool we're using to run DeepSeek locally.

💡 Why are we leaving the API Host as the default?

To connect you with your local LLM, Ollama needs to set up an endpoint, which is like an address within your computer to run DeepSeek. Ollama sets up LLMs at a default location (127.0.0.1:11434), so we'll keep the default value in Chatbox. This setup lets Chatbox communicate directly with your locally-hosted DeepSeek model.

• For the Model setting, choose your lighter model i.e. deepseek-r1:1.5b to start with.

• Select Save.

Chat with DeepSeek

• Select New Chat in the bottom left corner of Chatbox.

• Send the prompt: How many r's are in strawberry?

• While we're waiting for DeepSeek's response...

• Read DeepSeek's response, does everything look correct?

DeepSeek made an error!

You might notice that the 1.5b model gave you an incorrect answer! Instead of three r's in strawberry, it only found two.

The 1.5b model is the most lightweight R1 model, so it's less able to analyse text and conduct proper reasoning.

🙋‍♀️ DeepSeek didn't make an error!

How good is that! It's great if DeepSeek got that right with a smaller model. A comparison where both models produce the right result is still a great experiment. You could always try other problems or prompts to test the limits 🔥

• Let's compare this with your larger local DeepSeek model!
• Select another New Chat in the bottom left corner of Chatbox.
• This time, let's switch the DeepSeek model from 1.5b to 8b.

• Send the same prompt: How many r's are in strawberry?
• Notice that your DeepSeek 8b model might take longer to conduct a deeper chain of reasoning. But, you might also get a more though-out answer!

Temperature Test

Let's explore an advanced setting, called temperature, to see how you can customize DeepSeek R1 depending on the use case.

In this step, you're going to:

• Run the same prompt with different temperature settings in Chatbox.

High Temperature Test

• In Chatbox, select your Settings from the bottom left hand corner again.

• For the Model setting, switch to your heavier, more advanced model i.e. deepseek-r1:8b.
• At the bottom of the panel, dial the Temperature setting up to 2

What is temperature?

Temperature controls the randomness of an LLM's output.

A higher temperature, like 2, gives you more creative and unpredictable responses, while a lower temperature, like 0, gives more focused and logical responses.

This is a detailed setting that you might not have access to over a web app, but possible with local hosting and APIs. Chatbox makes it easy for you to edit and customise your AI model's temperature.

• Select Save.
• Let's put our higher temperature to the test!
• Start a New Chat in Chatbox.
• Input the following prompt:

Create a recipe for a dessert that includes avocados, chocolate, and sea salt.

• While we wait for a response...

• Review DeepSeek's response - can you spot some quirky phrases, ingredients or instructions in the response? 🕵️‍♀️

In this example, you might notice that the recipe uses orange juice! Interesting ingredient choice...

• Select your Settings from the bottom left hand corner again.
• Change the temperature to 0 and select Save.

Low Temperature Test

• Let's put our lower temperature to the test!
• Start a New Chat in Chatbox.
• Input the same prompt:

Create a recipe for a dessert that includes avocados, chocolate, and sea salt.

• While we wait for DeepSeek to reason and write a response, head back into your browser.

Set up a Third Chat

• Open ChatGPT or your favourite LLM (e.g. Gemini, Claude, maybe even DeepSeek) in your browser.

Why are we opening ChatGPT?

This new chat will act as the judge of the two responses we generate - can another AI tell the difference between high and low temperature responses?

You'll also get to learn a breakdown of how to detect low vs high temperature text along the way.

• Enter a prompt to ChatGPT to make it your temperature test judge.

You are an AI master. I will give you two pieces of generated text that received the same prompt. One was generated with a high temperature, the other was generated with a low temperature.

You are to identify which one was generated with a higher temperature setting.

• Great! Let's tell ChatGPT that the first response (your high temperature test results) is ready now, but not the second:

The second response is still generating, but I have the first response ready for you now. 

Can you read the first response, then wait for the second response after?

• Head back into Chatbox.
• Copy the first response.

• Send the first response to ChatGPT.

• Now head back to the DeepSeek chat where your second respoonse has been generating.
• Your second response might take more time to generate, so you might need to wait a few more minutes.
• Review DeepSeek's response - do you see any difference in the tone of voice and choice of words? 🕵️‍♀️

• Let's see if our judge can tell the difference.
• Copy the second response.
• Head back into ChatGPT.
• Send the second response to ChatGPT in the same window.
• Let's see the analysis!

Oooo, nice work ChatGPT. You might notice that ChatGPT can correctly point out the first response had a higher temperature setting, and why.

Why does temperature matter?

Different temperature settings work great for different scenarios.

• Translation services and code commenting usually use a low temperature setting (0.2-0.4) to prioritize accuracy.
• Chatbots usually use a low-mid temperature setting (0.5), so that responses are true and accurate.
• For summarising meetings or writing email responses, you might opt for a mid-high temperature setting (0.6-0.8) to add a bit of variety and generated insights.
• For creative writing and brainstorming, opt for a higher temperature setting (0.9-1.5) to get a wide range of results.

💡 Extra for Experts: I want to try another temperature experiment! We got you! Challenge your DeepSeek model to generate two responses - one with high temperature (2), one with low temperature (0) - to the following prompt:

Write a short 100 word story set in a world where gravity changes direction every day.

See the differences between a creative high-temperature story, versus a logical low-temperature story!

Secret mission

Welcome to your 🤫 exclusive 🤫 secret mission! Are you ready for the ultimate test?

Your mission, should you choose to accept it, is to expose how efficiently DeepSeek and OpenAI use tokens. This lets you know which model gives you the most value. Let’s dive in!

In this secret mission, you're going to:

• Set up API access to OpenAI (for free).
• Compare DeepSeek and OpenAI's efficiency at the same prompts.
• Showcase advanced understanding of LLMs in your documentation!

The Token Efficiency Showdown

Clean Up

Clean Up

Now that we've explored the world of LLMs with DeepSeek and Ollama, it's time to clean up. This is important to keep your systems tidy.

Resources to delete:

• Remove the DeepSeek models from Ollama (optional).
• Uninstall Ollama (optional).
• Uninstall Chatbox (optional).

Remove DeepSeek Models

If you no longer plan to use the DeepSeek models, you can remove them to free up disk space.

• In your terminal, run the following commands to remove the DeepSeek models:

ollama rm deepseek-r1:1.5b
ollama rm deepseek-r1:8b

• Run ollama list to see what models are left in your computer.
• You should see an empty table!

Uninstall Ollama

• In your terminal, run the following commands to stop and remove Ollama. You'll also remove any files Ollama has stored in your system:

🍎 MacOS

ollama stop
sudo rm -rf /usr/local/bin/ollama
sudo rm -rf ~/.ollama
sudo rm -rf /Library/LaunchDaemons/com.ollama.ollama.plist
sudo launchctl bootout system /Library/LaunchDaemons/com.ollama.ollama.plist

If you've installed Ollama via Homebrew, you can also uninstall it using

brew uninstall ollama
brew cleanup

To check if Ollama is fully removed, run:

which ollama

If it returns ollama not found, Ollama is completely uninstalled.

🖼️ Windows

The Ollama Windows installer registers an Uninstaller application.

Under Add or remove programs in Windows Settings, you can uninstall Ollama.

🐧 Linux

• Remove the Ollama service:

sudo systemctl stop ollama
sudo systemctl disable ollama
sudo rm /etc/systemd/system/ollama.service

• Remove the Ollama binary from your bin directory (either /usr/local/bin, /usr/bin, or /bin):

sudo rm $(which ollama)

• Remove the downloaded models and Ollama service user and group:

sudo rm -r /usr/share/ollama
sudo userdel ollama
sudo groupdel ollama

Uninstall Chatbox

• To uninstall Chatbox, simply drag the app from your Applications folder and into your computer's Trash.

That's a wrap!

That's a wrap!

You've journeyed into the fascinating world of LLMs, and emerged victorious! 🏆

You've learned how to:

• 🐋 Use DeepSeek both online and locally.
• 🌡️ Control the creativity of DeepSeek with temperature settings.
• 💎 Thoroughly compare DeepSeek vs OpenAI.

p.s. Does it say "Still tasks to complete!" at the bottom of the screen?

This means you still have screenshots left to upload, or questions left to answer!

1. Press Ctrl+F (Windows) or Command+F (Mac) on your keyboard.
2. Search for the text Return to later.
3. Jump straight to your incomplete tasks!
4. 🙋‍♀️ Still stuck? Ask the community!