Llama 3, NEW AI Model by Meta just got Released! Is it Better?

Mervin Praison
18 Apr 202403:37

TLDRMeta has released Llama 3, a new AI model that is set to become the most capable openly available language model to date. It will be accessible through various platforms like AWS Data Bricks, Google Cloud, Hugging Face, and Kaggle. Llama 3 comes in two versions: an 8 billion parameter model that outperforms Gemma 7B and Myal 7B, and a 70 billion parameter model that surpasses Gerini Pro 1.5 and CLo 3 Sonet on numerous benchmarks. The model is praised for its improved architecture and large vocabulary size of 128,000 tokens, which allows it to encompass multiple languages. It is pre-trained on over 15 trillion tokens, seven times larger than its predecessor Llama 2, and includes 5% high-quality non-English data. Llama 3 is also codeveloped with Torch Tune, a new PyTorch native library for efficient and customizable fine-tuning. The model's safety features include input and output safeguards. Meta is also working on a 400 billion parameter model and has released a repository with detailed information on how to download and use the model. The presenter is excited to demonstrate Llama 3 in an upcoming video and encourages viewers to subscribe for more AI-related content.

Takeaways

  • 🚀 **Meta Llama 3 Release**: A new AI model by Meta has been released, marking a significant advancement in AI capabilities.
  • 🔍 **Availability**: Llama 3 will be available on multiple platforms including AWS Data Bricks, Google Cloud, Hugging Face, and Kaggle.
  • 📏 **Model Versions**: The model comes in two versions: an 8 billion parameter model and a 70 billion parameter model.
  • ⚖️ **Performance**: The 8 billion parameter model outperforms other models like Gemma 7B and Myal 7B, while the 370 billion parameter model surpasses Gerini Pro 1.5 and CLO 3.
  • 🌐 **Multilingual Support**: Llama 3 supports multiple languages thanks to its large vocabulary size of 128,000 tokens.
  • 📈 **Training Data**: The model is pre-trained on over 15 trillion tokens, seven times larger than the data used for Llama 2.
  • 🌟 **Non-English Data**: 5% of Llama 3's dataset consists of high-quality non-English data covering over 30 languages.
  • 🔧 **Co-Development with TorchTune**: Llama 3 is co-developed with TorchTune, a PyTorch native library for authoring, fine-tuning, and experimenting with LMs.
  • 🛡️ **Safety Features**: The model includes input and output safeguards to enhance user safety.
  • 🔮 **Upcoming Models**: Meta plans to release more models in the Llama 3 series, including a 400 billion parameter model currently in training.
  • 📚 **Resources**: Detailed information on how to download and run the model using Transformers is available in the provided Llama 3 repository.

Q & A

  • What is the significance of the Meta Llama 3 AI model release?

    -The Meta Llama 3 AI model is significant as it is the most capable openly available language model to date, offering improved performance over previous models and being accessible on various platforms like AWS Data Bricks, Google Cloud, Hugging Face, Kaggle, and more.

  • How many versions of the Llama 3 model are available?

    -The Llama 3 model is available in two different versions: an 8 billion parameter model and a 70 billion parameter model.

  • Which model version of Llama 3 outperforms other models like Gemini 7B and Myal 7B?

    -The 8 billion parameter model of Llama 3 outperforms Gemini 7B instruction fine-tuned and Myal 7B instruction.

  • How does the Llama 3 70 billion parameter model compare to other models in benchmarks?

    -The Llama 3 70 billion parameter model outperforms GeriNi Pro 1.5 and CLO 3 Sonet on many of the benchmarks.

  • What is the size of the vocabulary in Llama 3's tokenizer?

    -Llama 3 uses a tokenizer with a vocabulary size of 128,000 tokens, which allows for the inclusion of many different languages within the model.

  • How much pre-training data did Meta use for Llama 3?

    -Meta pre-trained Llama 3 on over 15 trillion tokens, which is seven times larger than the data used for Llama 2.

  • What percentage of Llama 3's training data set consists of high-quality non-English data?

    -5% of Llama 3's data set consists of high-quality non-English data that covers over 30 languages.

  • What is the role of Torch Tune in the development of Llama 3?

    -Llama 3 is co-developed with Torch Tune, a new PyTorch native library designed for easy authoring, fine-tuning, and experimenting with language models.

  • What safety features does Llama 3 have?

    -Llama 3 includes safety features such as input safeguard and output safeguard to ensure the model's responses are appropriate when provided with user inputs and when generating outputs.

  • What is the next step for the Llama 3 model?

    -The next step for Llama 3 includes the release of the 8B and 70B models as the beginning of what Meta plans to release for Llama 3, with a 400 billion parameter model currently in training.

  • Where can one find more information on how to download and run the Llama 3 model?

    -More information on how to download and run the Llama 3 model can be found in the Llama 3 repository, which is linked in the video description.

  • What are the special tokens used in Llama 3?

    -The special tokens used in Llama 3 include those for the beginning of text, end of message, in a turn, start header, and end header.

  • How can viewers stay updated with the latest information on Llama 3?

    -Viewers can stay updated by subscribing to the presenter's YouTube channel, clicking the Bell icon, and liking the video to help it reach a wider audience.

Outlines

00:00

🚀 Introduction to Meta's LLaMa 3 Model

The video introduces the Meta LLaMa 3 model, an advanced language model with two versions: an 8 billion parameter model and a 70 billion parameter model. The 8 billion parameter model is noted to outperform other models like Gemma 7B and Myal 7B. The 70 billion parameter model surpasses benchmarks set by Gerini Pro 1.5 and CLo 3. The model's architecture is based on a standard decoder-only Transformer architecture with significant improvements over its predecessor, LLaMa 2. It uses a tokenizer with a vocabulary size of 128,000 tokens, allowing it to encompass a wide range of languages. The video also discusses the model's pre-training on over 15 trillion tokens, which is seven times larger than the data used for LLaMa 2, and includes 5% high-quality non-English data covering over 30 languages. The video promises a demonstration of the model's capabilities in a subsequent video and encourages viewers to subscribe for updates.

Mindmap

Keywords

💡Meta

Meta refers to the parent company of Facebook, which is known for its ventures into technology and artificial intelligence. In the context of the video, Meta is the developer of the new AI model called 'Llama 3'. The company's involvement signifies the model's potential for integration with social media platforms and other technologies under Meta's umbrella.

💡Llama 3 Model

The Llama 3 Model is a new AI model developed by Meta. It is described as the most capable openly available language model to date. The model is significant because it is set to be available on various platforms like AWS Data Bricks, Google Cloud, Hugging Face, Kaggle, etc., indicating its wide accessibility for developers and researchers.

💡Parameter Model

In the context of AI and machine learning, a parameter model refers to the size and complexity of the model, often measured by the number of parameters it contains. The Llama 3 Model comes in two versions: an 8 billion parameter model and a 70 billion parameter model. These parameters are the model's learned variables that it uses to make predictions or generate responses.

💡Benchmarks

Benchmarks are standardized tests or comparisons used to evaluate the performance of a system, in this case, the AI model. The video mentions that Llama 3 outperforms other models on many benchmarks, which means it achieves better results in various tests designed to measure its capabilities.

💡Decoder-only Transformer Architecture

This refers to a type of neural network architecture used in natural language processing. The Llama 3 Model utilizes a decoder-only Transformer architecture, which is a simplified version of the standard Transformer model that omits the encoder part. This architecture is efficient for generating text and is mentioned as a key improvement in Llama 3 compared to its predecessor.

💡Tokenizer

A tokenizer is a tool that breaks down text into individual units, known as tokens, which are then used by the AI model for processing. Llama 3 uses a tokenizer with a vocabulary size of 128,000 tokens, allowing it to include many different languages, which is a significant feature for multilingual support.

💡Pre-training Data

Pre-training data refers to the dataset used to train the AI model before it is fine-tuned for specific tasks. The Llama 3 Model is pre-trained on over 15 trillion tokens, which is seven times larger than the data used for Llama 2. This extensive pre-training data contributes to the model's improved performance.

💡Non-English Data

This term refers to the portion of the training data that is not in English. The Llama 3 Model includes 5% high-quality non-English data covering over 30 languages, enhancing its ability to understand and generate content in multiple languages beyond English.

💡Torch Tune

Torch Tune is a new PyTorch-native library mentioned in the video that facilitates easy authoring, fine-tuning, and experimenting with large models like Llama 3. It is described as efficient and hackable, allowing for more flexible and customized use of the AI model.

💡Safety Features

Safety features in AI models are designed to prevent the model from generating harmful or inappropriate content. The video mentions input and output safeguards for Llama 3, which are mechanisms to ensure that both the input provided to the model and the output generated by it are safe for use.

💡Model Card

A model card is a document that provides important information about a machine learning model, including its purpose, performance, and usage guidelines. The video mentions that a model card for Llama 3 is available, which would include details such as special tokens for text beginnings and endings, which are crucial for the model's operation.

Highlights

Meta has released Llama 3, a new AI model that is the most capable openly available language model to date.

Llama 3 will be available on multiple platforms including AWS Data Bricks, Google Cloud, Hugging Face, Kaggle, and more.

The model comes in two versions: an 8 billion parameter model and a 70 billion parameter model.

The 8 billion parameter model outperforms Gemma 7B and Myal 7B on instruction fine-tuning.

The 370 billion parameter model of Llama 3 surpasses Gerini Pro 1.5 and CLO 3 Sonet on various benchmarks.

Llama 3 is comparatively better than models like CLoe Sonet, Mistal Medium GPD 3.5, and Llama 270B based on human evaluation.

The pre-trained Llama 3 model outperforms Gemini 1.0 and Mixel 8 22B on all benchmarks.

Llama 3 uses a standard decoder-only Transformer architecture with several key improvements over Llama 2.

The model utilizes a tokenizer with a vocabulary size of 128,000 tokens, allowing for the inclusion of many different languages.

Llama 3 is pre-trained on over 15 trillion tokens, seven times larger than the data used for Llama 2.

5% of Llama 3's dataset consists of high-quality non-English data covering over 30 languages.

Llama 3 is co-developed with Torch Tune, a new PyTorch native library for easy authoring, fine-tuning, and experimenting with language models.

The model includes safety features such as input and output safeguards.

Meta plans to release more for Llama 3, including a 400 billion parameter model that is currently in training.

The Llama 3 repository provides information on how to download and run the model using Transformers.

A follow-up video will demonstrate how to test and run the Llama 3 model.

The presenter encourages viewers to subscribe to their YouTube channel for more content on Artificial Intelligence.

The video includes a call to action for viewers to like, share, and subscribe for future updates.

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