How to Use LLaMA 2 Online

The field of artificial intelligence is advancing at an incredible pace thanks to the efforts of the open source community. One of the most exciting new developments is LLaMA 2, an open source machine learning model created by Meta. We’ll walk you through how to access and use LLaMA 2 directly in your web browser.

LLaMA 2 demonstrates the power of open source AI. By making the code freely available, researchers around the world can build upon and improve the model. The open source approach leads to rapid innovation, as the collective knowledge and creativity of the community is leveraged. With each new release, LLaMA 2 becomes more capable.

Using LLaMA 2 is now easier than ever thanks to the user-friendly web interface. In just a few simple steps, anyone can tap into advanced natural language processing right from their browser. Whether you’re a student learning about AI, a developer building an application, or simply AI-curious, this blog post will show you how to get started with LLaMA 2.

How to Use LLama 2 on Cloud

Thanks to the open source release, there are now many easy ways to access LLaMA 2 online without needing to install anything locally. Platforms like HuggingFace provide access to the models through APIs, but there are some more user-friendly options that are great for testing out capabilities.

One easy way is through llama‘s website. You can chat with the Claude assistant backed by LLaMA 2 right in your browser. This is a great way to see the model in action and get a feel for its abilities.

Another option is Perplexity’s AI Playground which provides a simple text box interface for querying LLaMA 2. You can select which base size model you want to use as well.

Finally, the Poe assist app created by Quora leverages the LLaMA 2 model. With Poe, you can have conversations with many bots and see the impressive conversational abilities. The app makes AI accessible in a chatbot interface. For our tutorial we will use the model in Perplexity.

Also note that on the llama2 site you can upload PDFs and edit your settings such sa temperature and token output.

Working with the LLama2 AI Model

Now let’s test the LLama2 AI Model, arguably one of the best open source models out there.

Testing with Coding

We can start by asking Llama 2 to Invert a binary tree in Java.

public static TreeNode invert(TreeNode root) {
    if (root == null) {
        return null;

    // Swap the left and right child nodes
    TreeNode temp = root.left;
    root.left = root.right;
    root.right = temp;

    // Invert the left and right subtrees
    root.left = invert(root.left);
    root.right = invert(root.right);

    return root;

We get pretty good results, albeit this is a fairly easy task, it still doesn’t compare to closed sourced models at coding, but can still do impressive tasks.

Building Frontends

We will prompt it “Using tailwind css create a header”

After testing it on Tailwind Play we can see it has some issues. It’s not entirely perfect at least with its navbar spacing, also I would not have picked those color since it makes it hard to read with the background of header. Nonetheless a decent overall job, could probably be fixed with a bit more prompting.

How does Llama 2 do With Reasoning Problems?

Let’s ask it a coding problem. For example we’ll prompt it with “You are given a list of integers, and you need to write a Python function that returns the sum of the even numbers in the list. However, there’s a catch: you must implement the function using a for loop, and you are not allowed to use the modulo operator (%) or any built-in functions like sum() or filter()

This problem requires the AI to think creatively and use Python’s control structures and basic arithmetic operations to solve it. You can ask an AI to provide a Python code solution that adheres to the given constraints and returns the sum of even numbers in the list.

Here is our results:

def sum_even_numbers(numbers):
    sum = 0
    for num in numbers:
        if num % 2 == 0:
            sum += num
    return sum

The code it provided is not correct for the problem statement, which specifies not using the modulo operator (%).

Compare that to ChatGPT which solved it and gave us this:

def sum_of_even_numbers(numbers):
    # Initialize a variable to store the sum of even numbers
    even_sum = 0
    # Iterate through the list of numbers
    for num in numbers:
        # Check if the number is even
        # We can do this by subtracting the number by its floor division by 2 and checking if the result is 0
        if num - (num // 2 * 2) == 0:
            even_sum += num
    return even_sum

# Example usage:
numbers = [1, 2, 3, 4, 5, 6, 7, 8]
result = sum_of_even_numbers(numbers)
print("Sum of even numbers:", result)

Testing Basic Math

Llama 2 demonstrates acceptable performance in basic math operations, a notable constraint for many language models.

Conclusion about LLama2 

Our experience with Llama 2 has been nothing short of impressive. This open-source model has proven its mettle in various applications, demonstrating its versatility and competitiveness in the realm of online AI resources. Throughout our journey of exploring its capabilities, we found that Llama 2 for the most part delivered promising results and lived up to our expectations.

One of the standout features of Llama 2 is its accessibility. Thanks to its open-source nature, there are numerous ways to harness its power online. Whether you need to integrate it into a web application, utilize it for research, or enhance your AI-driven projects, Llama 2 provides the flexibility and convenience needed for a wide range of applications. Moreover, its ability to run locally on your computer makes it even more convenient and efficient, especially for tasks that require sensitive data handling or specific hardware configurations.

What sets Llama 2 apart from many other open-source models is its competitive performance. With consistent updates and improvements, it has become one of the best choices for those seeking cutting-edge AI capabilities. As the AI community continues to grow and evolve, it’s clear that Llama 2 remains at the forefront, delivering impressive results that meet and often exceed expectations.

The future of open-source AI models looks promising, and Meta’s emphasis on open-source technology further underscores the potential and importance of models like Llama 2. By continually fostering the development of these resources, we can look forward to even more exciting advancements and innovations in the AI field.

Overall, Llama 2 is a powerful, versatile, and competitive open-source model that has proven its worth in various applications. Whether you’re a developer, researcher, or enthusiast, Llama 2’s capabilities and Meta’s commitment to open source make it an excellent choice for your AI endeavors. So, dive in, explore its potential, and witness the impressive results for yourself.


Google Announces A Cost Effective Gemini Flash

At Google's I/O event, the company unveiled Gemini Flash,...

WordPress vs Strapi: Choosing the Right CMS for Your Needs

With the growing popularity of headless CMS solutions, developers...

JPA vs. JDBC: Comparing the two DB APIs

Introduction The eternal battle rages on between two warring database...

Meta Introduces V-JEPA

The V-JEPA model, proposed by Yann LeCun, is a...

Mistral Large is Officially Released – Partners With Microsoft

Mistral has finally released their largest model to date,...

Subscribe to our AI newsletter. Get the latest on news, models, open source and trends.
Don't worry, we won't spam. 😎

You have successfully subscribed to the newsletter

There was an error while trying to send your request. Please try again.

Lusera will use the information you provide on this form to be in touch with you and to provide updates and marketing.