How to DOWNLOAD Llama 3.1 LLMs

1littlecoder
23 Jul 202404:37

TLDRThis tutorial outlines the process of downloading and utilizing Llama 3.1 models, emphasizing the impracticality of running the 405 billion parameter model due to immense RAM requirements. It guides viewers to Hugging Face for model access, detailing the form-filling process for approval. Once approved, the models can be downloaded and used with Transformers, and even run on platforms like MAA AI and Hugging Chat, showcasing the model's capabilities and availability across various interfaces.

Takeaways

  • 😀 The tutorial explains how to download and use Llama 3.1 models.
  • 🤔 The 405 billion parameter model is impractical due to its massive RAM requirements.
  • 🔗 Visit the Hugging Face website via the link provided in the YouTube description to access the models.
  • 💻 Create an account on Hugging Face if you don't already have one.
  • 📝 Fill out a form with details like name, affiliation, date of birth, and country to request model access.
  • ⏱ Approval for model access might take some time and is not automated.
  • 🚀 Once approved, you can download and use the model with the Transformers library.
  • 💻 The model can be run on Google Colab without quantization.
  • 🌐 MAA AI has made it easy to interact with the model through a cloud platform.
  • 📱 You can also access the model via WhatsApp in the US by adding Meta AI as a contact.
  • 🔍 Hugging Face's Hugging Chat (HF Doco Chat) uses the default 405 billion parameter model, instruct fp8.
  • 📚 The tutorial suggests creating a separate Google Colab tutorial for detailed instructions on running the model.

Q & A

  • What is the main topic of the tutorial?

    -The main topic of the tutorial is how to download and use Llama 3.1 models.

  • Why can't we use the 405 billion parameter model?

    -We can't use the 405 billion parameter model because it requires an insane amount of RAM, which is almost impossible to provide for local inference.

  • How much RAM is needed for the 405 billion parameter model with full precision?

    -With full precision, 16-bit full precision requires 8810 GB of RAM.

  • What is the minimum RAM requirement for running the 405 billion parameter model with 8-bit precision?

    -With 8-bit precision, the minimum RAM requirement is 405 GB.

  • What is the process to access Llama 3.1 models on Hugging Face?

    -The process involves going to the Hugging Face landing page for Llama 3.1, selecting the desired model, filling out a form with details like name, affiliation, date of birth, and country, and waiting for approval.

  • How can you run the Llama 3.1 model on Google Colab?

    -You can run the Llama 3.1 model on Google Colab using a simple Transformers code snippet after you have been granted access and downloaded the model.

  • What is the alternative way to interact with the Llama 3.1 model without downloading it?

    -An alternative way is to use cloud platforms like MAA AI, where you can interact with the model by chatting with it on the platform.

  • Is there a WhatsApp option to try out the Llama 3.1 model?

    -Yes, if you are in the US, you can try out the Llama 3.1 model using WhatsApp, where Meta AI appears as one of your contacts.

  • What is the default model on Hugging Chat?

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

  • How can you access the Llama 3.1 model through other API providers?

    -The Llama 3.1 model is also available through other API providers like Grock, Together AI, and Fireworks AI.

  • What is the first step recommended to get started with the Llama 3.1 model?

    -The first step recommended is to get access to the model by requesting and waiting for approval from Hugging Face.

Outlines

00:00

🤖 Accessing and Using LLaMA 3.1 Models

This tutorial provides a step-by-step guide 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 immense RAM requirements. It guides viewers to request access to the models via Hugging Face, emphasizing the need for an account and the process of filling out a form for approval. Once access is granted, the tutorial suggests using the Transformers library to run the model on platforms like Google Colab and mentions the possibility of trying out the model through various interfaces such as MAA AI, WhatsApp, and Hugging Chat.

Mindmap

Keywords

💡Llama 3.1

Llama 3.1 refers to a series of machine learning models developed for natural language processing tasks. In the context of the video, it is the subject of the tutorial, which aims to guide users on how to download and utilize these models. The script mentions different versions of the Llama models, including a 405 billion parameter model, which is too large to run on standard hardware.

💡RAM

RAM, or Random Access Memory, is the hardware in a computer that temporarily stores data for quick access by the processor. The video script emphasizes the immense amount of RAM required to run the 405 billion parameter Llama model, highlighting the practical limitations for users without specialized hardware.

💡Hugging Face

Hugging Face is a company that provides a platform for developers to share and collaborate on machine learning models. In the script, it is the place where users are directed to access the Llama 3.1 models, requiring an account and sometimes a waiting period for approval to download the models.

💡Model ID

A Model ID is a unique identifier for a specific machine learning model. In the context of the video, once a user has access to the Llama 3.1 model on Hugging Face, they need to use the Model ID to download and utilize the model via the Transformers library.

💡Transformers

Transformers is an open-source library developed by Hugging Face that allows users to easily work with different pre-trained models for natural language processing. The script provides a simple code snippet showing how to import and use the Transformers library to run the Llama model.

💡Google Colab

Google Colab is a cloud-based platform that provides free access to computing resources, including GPUs, for running Jupyter notebooks. The video mentions using Google Colab to run the Llama model without the need for local hardware, suggesting it as an alternative for users with limited resources.

💡Quantization

Quantization in machine learning refers to the process of reducing the precision of the numbers used to represent model parameters, which can help in reducing model size and memory usage. The script briefly touches on the possibility of running the Llama model with quantization to make it more accessible for users with less powerful hardware.

💡API Providers

API Providers are services that offer Application Programming Interfaces for accessing certain functionalities or data. The script mentions several API providers such as MAA AI, GPTQ, and others that offer access to the Llama model, allowing users to interact with the model through their platforms.

💡Parameter

In the context of machine learning, a parameter is a value that is learned during the training process of a model. The script discusses different models with varying numbers of parameters, indicating the complexity and size of the Llama models, with the 405 billion parameter model being particularly large.

💡Overloaded

Overloaded refers to a system or service that is experiencing high demand, leading to potential performance issues or delays. The script mentions that the Llama model might be overloaded when many users are trying to access it simultaneously, affecting the performance of the service.

💡Hugging Chat

Hugging Chat is a service provided by Hugging Face that allows users to interact with machine learning models through a chat interface. The script mentions using Hugging Chat to test the Llama model's capabilities, such as creating jokes, although it notes potential issues due to high demand.

Highlights

Tutorial on how to download and use Llama 3.1 models.

Cannot use the 405 billion parameter model due to immense RAM requirements.

Details on RAM requirements for different precision levels of the 405 billion parameter model.

Instructions to access the Llama 3.1 models via Hugging Face.

Need to create an account on Hugging Face if you don't have one.

Process of selecting and requesting access to a specific Llama 3.1 model.

Filling out a form with details for model access request.

Waiting for approval to access the model.

How to download the model once access is granted.

Using the model with Transformers library in Python.

Running the model on Google Colab without quantization.

Potential creation of a separate tutorial for Google Colab setup.

Using the model through cloud platforms like MAA AI.

Chatting with the model on platforms without needing to log in.

Model's capability to create a snake game in Python demonstrated.

Availability of the model on WhatsApp for US users.

Accessing the model through Hugging Chat and other API providers.

Reminder to get access to the model before attempting to use it.

Promise of a separate tutorial for Google Colab and checking for interest from viewers.

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