How to DOWNLOAD Llama 3.1 LLMs

1littlecoder
23 Jul 202404:37

TLDRThis tutorial explains how to download and use Llama 3.1 models. It highlights the impracticality of running the 405 billion parameter model due to immense RAM requirements. The guide suggests visiting Hugging Face for model access, creating an account if necessary, and filling out a form to request model access. Once approved, users can download and utilize the model with Transformers code or explore cloud options like MAA AI and Hugging Chat. The video promises a follow-up tutorial on running the model with Google Colab.

Takeaways

  • 😲 The 405 billion parameter Llama 3.1 model requires an immense amount of RAM, making it nearly impossible for most users to run locally.
  • 🔗 To access Llama 3.1 models, one must visit a link provided in the video description, which leads to the Hugging Face platform.
  • 📝 Users need to create an account on Hugging Face if they do not already have one.
  • 📚 After navigating to the Llama 3.1 landing page, users can select the model they wish to use and fill out a form with personal details to request access.
  • ⏳ Approval for model access may take some time and is not automated.
  • 🚀 Once approved, users can download the model and utilize it with the Transformers library in Python.
  • 💻 The script suggests that the model can be run on Google Colab without quantization.
  • 🌐 MAA AI offers a cloud version of the model where users can interact with it through a chat interface.
  • 📲 The model is also accessible via WhatsApp for users in the US, appearing as a contact named 'Meta AI'.
  • 🤖 Hugging Face's 'Hugging Chat' platform uses the 405 billion parameter Llama 3.1 model by default, allowing users to test its capabilities.
  • 🔑 The initial step for using the Llama 3.1 model is to gain access to it, which is crucial before attempting to download or use it in any platform.
  • 🔍 The video creator plans to create a separate Google Colab tutorial for those interested in learning more about running the model.

Q & A

  • What is the primary focus of this tutorial?

    -The tutorial focuses on how to download and use the Llama 3.1 models.

  • Why is it difficult to use the 405 billion parameter model locally?

    -It is difficult because it requires an immense amount of RAM, with full precision needing 8810 GB, 8-bit precision needing 405 GB, and quantized versions needing 203 GB.

  • What is the first step to access the Llama 3.1 models?

    -The first step is to go to the provided link in the YouTube description, which will take you to Hugging Face, and then create an account if you don't have one.

  • What information is required to fill out the form on Hugging Face?

    -You need to provide your name, affiliation, date of birth, and country.

  • What do you need to do after filling out the form on Hugging Face?

    -After filling out the form, you need to submit your request and wait for approval to access the model.

  • How can you use the Llama 3.1 model with Hugging Face Transformers?

    -You can use it by importing Transformers and using the model ID provided. It can be run on Google Colab without any quantization.

  • What is the alternative method mentioned for running the Llama 3.1 model if you don't want to use Google Colab?

    -You can use Meta AI's platform, where you can chat with the model after logging in with a Facebook account.

  • What is the default model on Hugging Chat?

    -The default model on Hugging Chat is Meta Llama 3.1 405 billion parameter model instruct fp8.

  • Can you access the Llama 3.1 model on other platforms besides Hugging Face and Meta AI?

    -Yes, it is available on other API providers like Grok, Together AI, Fireworks AI, and others.

  • What does the presenter offer to create if there is interest?

    -The presenter offers to create a separate Google Colab tutorial with all the details on how to run the model.

Outlines

00:00

🤖 Downloading and Using LLaMA 3.1 Models

This paragraph introduces a tutorial on how to download and utilize the LLaMA 3.1 models, with a focus on the impracticality of running the 405 billion parameter model due to its massive RAM requirements. The video explains that while the largest model is unfeasible for most users, other models such as the 8 billion and 70 billion parameter versions are accessible. It guides viewers to request access through the Hugging Face platform, emphasizing the need for an account and the process of submitting a form for model access. The tutorial promises further instructions on downloading the model and using it with the Transformers library, as well as hints at a future tutorial on running the model via Google Colab.

Mindmap

Keywords

💡Llama 3.1

Llama 3.1 refers to a series of language models developed by Meta AI, which are capable of understanding and generating human-like text. In the video, Llama 3.1 models are the central topic, with a focus on how to download and utilize them for various applications, such as creating games or generating jokes.

💡Model Parameters

Model parameters are the variables within a machine learning model that are learned from data. The video discusses different models with varying parameters, such as 405 billion, 8 billion, and 70 billion, indicating the complexity and size of the models. The larger the number of parameters, the more data the model can process and potentially the more intelligent it can be.

💡RAM

RAM, or Random Access Memory, is the hardware in a computer that temporarily stores data while the computer is running. The video emphasizes the need for a large amount of RAM to run the 405 billion parameter Llama 3.1 model, highlighting the computational demands of such advanced AI models.

💡Hugging Face

Hugging Face is a company that provides a platform for developers to share and collaborate on machine learning models. In the context of the video, Hugging Face is the place where users can request access to the Llama 3.1 models and download them for use in their projects.

💡Quantization

Quantization in the context of AI models refers to the process of reducing the precision of the numbers used to represent the model's parameters, which can help in reducing the model's memory usage and improving speed. The video mentions different levels of quantization, such as 16-bit full precision and 8-bit precision, and their impact on RAM requirements.

💡Transformers

Transformers is a library developed by Hugging Face that allows for easy implementation of various pre-trained models in machine learning tasks. The video script provides an example of using the Transformers library to run the Llama 3.1 model on Google Colab.

💡Google Colab

Google Colab is a cloud-based platform provided by Google that allows users to write and run Python code in a browser, using Google's infrastructure. The video suggests using Google Colab to run the Llama 3.1 model without the need for local hardware with high RAM capacity.

💡API Providers

API, or Application Programming Interface, providers are companies or platforms that offer access to their functionality through a set of rules and protocols. In the video, the speaker mentions various API providers that offer access to the Llama 3.1 model, such as MAA AI, GPTQ, and others.

💡Overloaded Model

An overloaded model refers to a situation where a machine learning model is being used by so many people at the same time that it cannot handle all the requests efficiently. The video mentions that the Llama 3.1 model is currently overloaded due to high demand.

💡Meta AI

Meta AI is the artificial intelligence division of Meta Platforms, Inc., formerly known as Facebook, Inc. The video discusses the capabilities of Meta AI, particularly in relation to the Llama 3.1 model, and how users can interact with it through different platforms like WhatsApp and Hugging Chat.

Highlights

This tutorial explains how to download and use Llama 3.1 models.

The 405 billion parameter model requires an enormous amount of RAM.

For full precision, the 405 billion parameter model needs 810 GB of RAM.

With 8-bit precision, the 405 billion parameter model requires 405 GB of RAM.

Even with quantization, the model still needs 203 GB of RAM.

Running the 405 billion parameter model locally is almost impossible due to hardware requirements.

To run smaller models, like the 8 billion or 70 billion parameter models, go to Hugging Face.

Create an account on Hugging Face if you don't have one.

On the Llama 3.1 landing page, select the model you want to use.

Fill out the form with your name, affiliation, date of birth, and country to request access.

It may take some time to get approval for model access.

Once approved, you can download and use the model with Hugging Face Transformers.

The code to use the model with Hugging Face Transformers is straightforward.

You can run the model on Google Colab without quantization.

Meta AI offers a platform to run the model without creating a Facebook account.

You can try out the model on WhatsApp if you are in the US.

Hugging Chat also provides access to the 405 billion parameter model.

Other API providers like Grok, Together AI, and Fireworks AI offer access to the model.

The first step is to get access approval; otherwise, using the model is difficult.

A separate Google Colab tutorial may be created for more details on using the model.

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