How To Use Meta Llama3 With Huggingface And Ollama

Hello all, my name is Krish Naik and welcome to my YouTube channel. In this blog post, I will guide you through the process of using the Meta Llama3 model with both Huggingface and Ollama. Meta Llama3 comes in two variants - 8 billion parameters and 70 billion parameters, allowing it to solve various use cases like text generation, question answering, and many more.

Getting Started

To run Meta Llama3, we have two main options: Huggingface and Ollama. Additionally, Kaggle can be used but access is currently limited. Let’s dive into each method one by one.

Using Huggingface

First, let’s explore how to use Meta Llama3 through Huggingface.

• Click on Get Started.
• You'll be presented with three options: Meta Llama3, Meta Llama2, and Meta Code Llama70B.
• Select the specific model you want to use, for example, the 8 billion parameters model.
• Fill out the access form and submit it. Once granted access, you can start using the model.

Setting Up the Environment

To work with Huggingface, you will need to install the necessary libraries. Below is an example of how to set up the environment:

pip install transformers
pip install torch
pip install accelerate
pip install huggingface_hub
    

After installing the required libraries, you will need a Huggingface token. The code snippet below demonstrates how to use the token for text generation:

import transformers

model_id = "meta-lama3-8b"
token = "your_huggingface_token"
pipeline = transformers.pipeline(
    "text-generation",
    model=model_id,
    tokenizer=model_id,
    framework='pt',
    device=0  # Assumes CUDA-enabled GPU is available
)

result = pipeline("Hey, how are you doing today?")
print(result)
    

In this example, we use the model ID 'meta-lama3-8b' for text generation. The text will be generated as chunks, and once completed, you will see the results in your console.

Additional Features

Huggingface's pipeline offers a wide variety of functionalities:

• Audio Classification
• Automatic Speech Classification
• Question Answering
• Summarization
• Text Classification
• Text Generation

Each of these tasks can be executed using the specific pipeline for that task. Huggingface's documentation provides all the necessary details to explore these functionalities.

Using Ollama

Next, let’s see how to use Meta Llama3 through Ollama. One notable advantage of using Ollama is its quantisation feature, which makes working with large models like Meta Llama3 more feasible.

Setting Up AMA

ama run lama3
    

When you run the command, it will download the entire Meta Llama3 model. You can then interact with the model to generate responses, execute code, and much more. Here’s a simple demo session:

Q: "Who are you?"
A: "I'm Llama, an AI assistant developed by Meta AI..."

Q: "Write me a Python code to perform binary search."
A: [Generated Python Code]
    

The model provides comprehensive responses to your queries. When integrated with end-to-end projects, Ollama proves to be an invaluable tool.

Conclusion

Meta Llama3 is a powerful model with diverse applications, from text generation to complex NLP tasks. Using Huggingface and Ollama provides flexible avenues to leverage this technology efficiently. While setting up the environment might require some initial efforts, the end results make it all worthwhile.

For more detailed steps and hands-on demonstrations, be sure to check out the full video tutorial on YouTube.

Call to Action

If you found this guide helpful, don't forget to watch the complete video for more insights and detailed explanations:

Thank you for reading and happy coding! If you have any questions or feedback, feel free to leave a comment on the video. See you in the next post!