8/27/2024

Integrating Ollama with Colab Notebooks

Are you curious about how to bring the power of Ollama into your Colab Notebooks? You're in the right place! In this blog post, we're diving into the exciting world of integration, exploring how Ollama simplifies access to Large Language Models (LLMs) like Mistral, Llama3, and Code Llama! Whether you're scientists, learners, or developers, Ollama brings AI capabilities right to your fingertips.

What is Ollama?

At its core, Ollama is an innovative platform designed to seamlessly run open-source LLMs on your local computer or in the cloud. This means no more painstaking setups or worrying about dependencies! Ollama eliminates the complexities of managing model weights, frameworks, and configurations—allowing you to focus on what really matters: interacting and experimenting with LLMs.

Why Use Colab Notebooks?

Now, why would you want to use Google Colab for this? Well, it provides a FREE cloud-based platform with powerful GPUs that make it ideal for running resource-intensive models. With Google Colab, you can leverage free computational power without breaking the bank!

Step-by-Step Integration of Ollama in Google Colab

Step 1: Install Required Packages

First thing's first! You need to prepare your Colab Notebook. Begin by installing necessary Python packages. Run the following code snippet in a code cell in your notebook:

1
!pip install aiohttp pyngrok

Step 2: Install Ollama

Now it's time to install Ollama itself. You can do so with a simple curl command. This command fetches the installation script directly from Ollama's repository. To execute it, open a new code cell and run:

1
!curl -fsSL https://ollama.ai/install.sh | sh

Step 3: Starting Ollama and Ngrok

Using ngrok allows you to expose your local Ollama server to the internet, enabling access to other users or clients as needed. First, configure ngrok with your authentication token. You can get one from ngrok’s dashboard.

Then, start both Ollama and ngrok in separate threads to avoid blocking your execution. Here's how:

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
import asyncio
import os

# Set the environment variable for the NVIDIA library path to ensure it uses the GPU
os.environ.update({'LD_LIBRARY_PATH': '/usr/lib64-nvidia'})

async def run(cmd):
    print('>>> starting', *cmd)
    p = await asyncio.subprocess.create_subprocess_exec(*cmd,
                                                       stdout=asyncio.subprocess.PIPE,
                                                       stderr=asyncio.subprocess.PIPE)

async def pipe(lines):
    async for line in lines:
        print(line.decode().strip())

async def start_ollama_and_ngrok():
    await asyncio.gather(
        run(['ollama', 'serve']),
        run(['ngrok', 'http', '--log', 'stderr', '11434'])
    )

await start_ollama_and_ngrok()

Step 4: Verify Installation

After you run the above codes, Ollama should be available. Check whether it's running correctly! You should see something that resembles:

2024/01/16 20:19:11 routes.go:930: Listening 127.0.0.1:11434 (version 0.1.20)

This message means your Ollama server is now active and listening for incoming requests!

Step 5: Start Interacting with Ollama

Now that we’ve set everything up, we can start engaging with Ollama's functionalities. For instance, you can run a model like Mistral using:

1
!ollama run mistral

You can also switch between models as per your requirements! Feel free to explore different models available in the Ollama Model Library.

Step 6: Use Models in Your Colab Environment

Equipped with Ollama set up, it's time to utilize models for your projects. You might want to process data, train models, or even create chatbots!

Here's an example of using the Ollama API to interact with a model and get a response:

1
2
3
4
5
6
7
8
9
10
11
12
13
import requests

# Replace URL with your ngrok public URL
url = 'https://{your-public-url}.ngrok.io/api/chat'

payload = {
    'model': 'mistral',
    'messages': [{'role': 'user', 'content': 'Tell me a joke!'}]
}

response = requests.post(url, json=payload)

print(response.json())

Troubleshooting Common Issues

When you’re working with Google Colab and Ollama, you might encounter a few issues. Here are some troubleshooting tips:

Model Loading Issues: If you experience GPU crashes when loading large models, ensure your Colab runtime type is set to GPU. Go to
1Runtime > Change runtime type
and select GPU.
CPU Fallback: Sometimes, if GPU memory isn’t enough, the model may fall back to CPU. You can monitor resources via the Colab interface to check if you are nearing limits.
Ngrok Tunnel Problems: If your ngrok connection drops, make sure your authentication token is still valid and that you're forwarding the correct port.

The Power of Integration in Your Workflow

Integrating Ollama with Colab Notebooks greatly expands your ability to work with LLMs. It's particularly beneficial for various applications:

Data Analysis: Analyze datasets directly in Colab while utilizing LLMs to generate insights.
Interactive Learning: Great for educational purposes—students can learn and practice coding alongside powerful AI models!
Research Developments: Ideal for researchers needing quick access to sophisticated LLMs without investing heavily in infrastructure.

Why Choose Arsturn?

As you discover the power and efficiency of integrating Ollama into Colab, it’s the perfect time to streamline your digital interactions even further. Arsturn is your go-to solution for creating personalized chatbots tailored to engage your audience effectively without any coding hassles. Here's why you should check it out:

No-Code AI Chatbot Builder: Easily create engaging chatbots that enhance audience interaction on your website or social media.
Instant Response System: Provide immediate assistance to users, optimizing their experience.
Analytics and Insights: Utilize data-driven insights to refine your conversational strategies.
Highly Customizable: Make the chatbot reflect your brand identity seamlessly.

With Arsturn, you can integrate chatbots to handle FAQs, collect leads & engage your audience more effectively. You can discover the potential of conversational AI at Arsturn.com.

Conclusion

Integrating Ollama with Colab Notebooks is your gateway to harnessing the full potential of LLMs while providing a flexible environment for creativity and discovery. By following the steps outlined above, you can set up your models quickly, experiment with them freely, and bring AI into your workflows! Happy coding!