Mixtral-8x7B + GPT-3 + LLAMA2 70B = The Winner

While everyone’s focused on the release of Google Gemini quietly in the background Mixtral-8 x 7 Billion releases their open-source model.

So, In this Article, we’re diving into some of the latest AI developments from major players like llama2 70 Billion, GPT-3, and Mixtral-8 x 7 Billion.

Mixtral-8 x 7 Billion outperforms the Llama 2 70B model in most benchmarks while delivering six times faster inference. Plus, it’s an open-weight model released with an Apache 2.0 license, meaning anyone can access and use it for their projects.

In this step-by-step guide, we will cover what Mixtral-8x7B is, what is a sparse mixture of experts, how to use Mixtral-8x7B and Why Mixtral-8x7B is so much better than GPT-3 and Llama2 70 Billion

My homepage is like my personal lab for playing around with large language models — and yeah, it’s as fun as it sounds! , it’s the new Mixtral-8x7B model that truly excites me.

I highly recommend you read this article to the end is a game changer in your chatbot that will realize the power of OpenHermes 2.5!

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WHAT IS Mixtral-8x7B?

Mixtral 8x7b, the latest language model developed by Mistral AI, is making waves in the field of artificial intelligence. This groundbreaking model boasts incredible capabilities and a unique architecture that sets it apart from its predecessors.

Unlike traditional language models, Mixtral 8x7b utilizes a sparse Mixture of Expert (MoE) layer instead of feed-forward layers

WHAT IS A SPARSE MIXTURE OF EXPERT?

MoE (Mixtures of Experts) is an approach that combines multiple neural networks. Create efficient models by preparing multiple networks specialized for specific tasks or domains and learning and inferring while switching between them.

In MoE, the model consists of two subnetworks: a gating network and multiple expert networks. The gate part determines which expert is selected for the input, and only the selected expert is responsible for the output.

This provides two benefits: improved performance and reduced learning/inference time. Regarding performance improvement, each expert can be optimized for different tasks, making it possible to handle a wider range of tasks.

FEATURE OF MIXTRAL — 8 x 7 B

Context Handling: Mixtral adeptly manages a context of 32k tokens, offering a robust foundation for diverse applications.

Multilingual Support: With capabilities spanning English, French, Italian, German, and Spanish, Mixtral ensures broad accessibility and usability.

Code Generation Prowess: Exhibiting robust performance in code generation tasks, Mixtral is poised to enhance various programming-related applications.

Fine-Tuning Excellence: Mixtral can be fine-tuned into an instruction-following model, achieving an impressive score of 8.3 on MT-Bench, showcasing its adaptability and versatility.

BENCHMARKS RESULTS :

Built using this groundbreaking method, Mixtral-8x7B is much faster than larger models (Llama2 70B, GPT-3.5) and uses fewer resources to deliver the same or better performance. It has come true.

This is the result of the benchmark, but you may be wondering what the actual performance is.

From here, we will use it to compare and verify the performance of Mixtral-8x7B.

First, I will explain how to use it.

HOW TO USE MIXTRAL_8 x 7B

If you want to simply use the instruction-tuned version via a chat interface, you can use the Perplexity Labs playground. Just choose it from the model selection dropdown in the bottom right corner.

let’s try Mixtral 8 x 7 B Ask them to introduce themselves by completing the prompts below.

Introduce yourself

Now that we know that it can be used, we will compare it with Llama2 70B and GPT-3.5, which were also compared in the benchmark and verify the difference in output.

let’s compare Mixtral 8 x 7 Billion AND LLAMA2 70B and GPT-3

Verify that you can output working code by running the prompt below.

write bubble sort in python

Mixtral 8 x 7 Billion

python
def bubble_sort(arr):
    n = len(arr)

    for i in range(n):
        # Create a flag that will allow the function to terminate early if there's nothing left to sort
        swapped = False

        for j in range(0, n-i-1):
            # If the element found is greater than the next element swap them
            if arr[j] > arr[j+1]:
                arr[j], arr[j+1] = arr[j+1], arr[j]
                swapped = True

        # If no two elements were swapped by inner loop, then the array is already sorted.
        if not swapped:
            break

Llama2 70 Billion

def bubble_sort(arr):
    n = len(arr)
    for i in range(n-1):
        for j in range(0, n-i-1):
            if arr[j] > arr[j+1]:
                arr[j], arr[j+1] = arr[j+1], arr[j]
    return arr

GPT-3.5

def bubble_sort(arr):
    n = len(arr)
    
    # Traverse through all array elements
    for i in range(n):
        # Flag to indicate whether any swaps were made in this pass
        swapped = False
        
        # Last i elements are already in place, so we don't need to compare them again
        for j in range(0, n-i-1):
            # Swap if the element found is greater than the next element
            if arr[j] > arr[j+1]:
                arr[j], arr[j+1] = arr[j+1], arr[j]  # Swap elements
                swapped = True
        
        # If no two elements were swapped in this pass, the array is already sorted
        if not swapped:
            break

# Example usage:
arr = [64, 34, 25, 12, 22, 11, 90]
bubble_sort(arr)
print("Sorted array is:", arr)

As a result, the Mixtral-8x7B and GPT-3.5 output codes work correctly, but only Llama2 70B works as is. It outputs a code that does not exist.

Llama2 70B’s version has obvious elementary mistakes, such as libraries not being imported.

From this result, it can be seen that Mixtral-8x7B has a higher performance than Llama2 70B and is equivalent to GPT-3.5.

Knowledge

Enter the following prompt and verify that it outputs the correct information.

Give me a list of the highest mountain elevations in France, ranking the top 5

Mixtral-8x7B

Mont Blanc - 4,810 meters (15,781 feet)
Monte Cervin (Matterhorn) - 4,478 meters (14,692 feet)
Mont Rosa - 4,634 meters (15,203 feet)
Pic du Midi d'Ossau - 2,884 meters (9,462 feet)
Aiguille Verte - 4,122 meters (13,524 feet)

Llama2 70 Billion

Sure, here are the top 5 highest mountain elevations in France, 
ranked in order:
Mont Blanc - 4,810 meters (15,781 feet)
Mont Blanc du Tacul - 4,248 meters (13,934 feet)
Mont Maudit - 4,465 meters (14,649 feet)
Dôme du Goûter - 4,304 meters (14,120 feet)
Aiguille du Midi - 3,842 meters (12,606 feet)

GPT-3.5

1- Mont Blanc: Mont Blanc, located in the French Alps on the border between France and Italy, is the highest mountain in France and Western Europe. Its elevation is approximately 4,810 meters (15,781 feet).

2- Dôme du Goûter: Dôme du Goûter is another prominent peak in the Mont Blanc massif. It has an elevation of about 4,304 meters (14,118 feet).

3- Barre des Écrins: Barre des Écrins is the highest peak in the Ecrins National Park in the French Alps. It stands at an elevation of approximately 4,102 meters (13,458 feet).

4- Mont Collon: Mont Collon is part of the Pennine Alps and is located near the border between France and Switzerland. Its elevation is approximately 4,363 meters (14,314 feet).

5- La Meije: La Meije is also located in the Ecrins National Park and is known for its challenging climbing routes. It has an elevation of about 3,984 meters (13,071 feet).

The output of the answer by Mixtral-8x7B is mixed in with Monte Cervin, which is not a mountain in French, so I’ll look into it. if you take a look at Google, you will see Monte Cervin, located in Switzerland

To summarize the results of this verification, we found that Mixtral-8x7B certainly outputs a little better answers than Llama2 70B, and has a performance that can be said to be equivalent to GPT-3.5.

This model has the highest performance among the open-source models currently available.

Summary:

Mixtral 8x7B signifies a leap forward in the AI landscape, offering not just cutting-edge performance but a commitment to openness, innovation, and community-driven development. As Mistral AI continues its mission, Mixtral sets a new benchmark for the collaborative evolution of artificial intelligence.

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