Getting Started With Meta Llama 3.2 And Its Variants With Groq And Huggingface

Hello everyone, my name is Krisha Naik, and welcome to my YouTube channel! Today, I am excited to share with you a significant update in the field of generative AI, especially regarding LLM (Large Language Model) models. Meta has recently launched Llama 3.2, an open-source AI model you can use to fine-tune, distill, and deploy anywhere. What makes Llama 3.2 unique is that it offers several different versions, currently available in:

• 1 billion parameters
• 3 billion parameters
• 11 billion parameters
• 90 billion parameters

If you've used Llama 3.1 previously, you'll know how impressive its capabilities were. But now, with Llama 3.2, there are two different types: lightweight and multimodel.

Lightweight and Multimodel Variants

The lightweight variant includes:

• 1 billion parameters
• 3 billion parameters

This variant can be run on mobile or any Edge devices, which offers great flexibility.

The multimodel variant, on the other hand, is quite amazing because it allows reasoning with high-resolution images. Recently, I've been closely following open-source models, and I'm genuinely excited about Meta's continuous innovation in this area.

Flagship Model and Its Use Cases

Meta also has a Flagship Foundation model with 405 billion parameters. This model allows for most text-related tasks, even providing reasoning capabilities with high-resolution images. Here are three main uses of this model:

• On-Device: Summarizing discussion from phone calls, using on-device tools like calendar, etc.
• Multimodel: You can transform existing images into something new or get more information from an image of your surroundings.
• Coding: With the Llama stack, you can directly code and implement these models seamlessly.

Accessing the Models

To access these models, you can visit Meta's website. For example, you can explore the various benchmarks they've made available:

• Meta Llama 3.2
• Llama 3.2 lightweight versions
• Gamma 2

All these models are open-source and have shown incredible performance in different benchmarks, such as MMLU Open, Eval GSK, Math Arc Challenge, and many more.

Using Hugging Face

A great way to access Llama 3.2 is through Hugging Face. Simply search for Meta Llama 3 on their platform, and you will find:

• Llama 3.2 1 billion parameters
• Llama 3.2 3 billion parameters
• Llama 3.2 vision with 11 billion parameters

To use any of these models, you will need to get access and set them up on a platform like Google Colab. Here’s a quick step-by-step guide:

pip install --upgrade transformers
import transformers
from transformers import AutoPreProcessor, AutoModel
model = AutoModel.from_pretrained("meta-llama-3.2")

# Running sample code in Google Colab
# Connect to the runtime
!pip install transformers
token_id = "your_hugging_face_token"
model = AutoModel.from_pretrained("meta-llama-3.2")
pre_processor = AutoPreProcessor.from_pretrained("meta-llama-3.2")

This way, you can easily access and run these models. For fine-tuning, you can incorporate techniques such as LoRA and QLoRA, which I plan to cover in upcoming videos.

Exploring Groq

Besides Hugging Face, you can also explore Groq. They offer a variety of models, including Llama 3.1 and Gamma 2, which I've discussed in my generative AI course on Udemy.

• Access models via Groq client
• Log in and execute model APIs

Conclusion

In conclusion, Meta's continuous efforts in the open-source AI space with Llama 3.2 are truly groundbreaking. Whether you’re using Groq or Hugging Face, there are abundant resources to explore these powerful models. I'm keen to see what the next version, possibly Llama 3.3, will offer.

To stay updated with the latest in generative AI, make sure to watch the full video by following this link:

Thank you for reading, and I hope you found this post helpful!