Zuck's new Llama is a beast

Fireship
24 Jul 202404:13

TLDRMeta has released its largest language model, Llama 3.1, with a 405 billion parameter model that is mostly superior to Open AI's GPT-40 and beats Claude 3.5 Sonet on some benchmarks. It's open source, allowing developers to build AI-powered apps without relying on expensive APIs. The model can be fine-tuned with custom data, showing potential for future advancements.

Takeaways

  • 🚀 Meta has released a large language model called Llama 3.1, which is a 405 billion parameter model with a 128,000 token context length.
  • 💰 The training of Llama 3.1 required significant resources, including 16,000 Nvidia h100 GPUs and a substantial amount of electricity.
  • 🆓 Llama 3.1 is available for free, which is a notable move in the competitive AI space.
  • 🔍 The model is arguably open source, with certain restrictions on commercial use, especially for apps with a large user base.
  • 📚 The training code for Llama 3.1 is publicly available, consisting of only 300 lines of Python and PyTorch, showcasing simplicity in its approach.
  • 🔑 The model weights are open, allowing developers to build AI-powered apps without relying on costly APIs from other providers.
  • 💡 Llama 3.1 comes in three sizes: 8B, 70B, and 405B, with 'B' referring to billions of parameters, indicating the model's capacity for complex pattern recognition.
  • 🤔 Initial feedback suggests that the larger Llama model is somewhat disappointing, while the smaller versions are more impressive.
  • 🛠 The real potential of Llama lies in its ability to be fine-tuned with custom data, hinting at future capabilities for specialized applications.
  • 📝 In creative tasks like coding, writing, and poetry, Llama 3.1 shows promise but is still behind other models like Claude 3.5 in performance.
  • 🌐 The AI industry has seen a plateau in advancements, with Meta's Llama 3.1 being a significant but not revolutionary step forward in AI capabilities.

Q & A

  • What is the significance of Meta's release of the large language model known as 'Llama 3.1'?

    -Meta's release of 'Llama 3.1' is significant as it is one of the largest language models ever created, boasting 405 billion parameters and a 128,000 token context length. It is also notable for being free and arguably open source, which allows developers to use it without incurring costs for API usage.

  • How long did it take to train the Llama 3.1 model and what resources were used?

    -The training of Llama 3.1 took months and utilized 16,000 Nvidia H100 GPUs, which likely cost hundreds of millions of dollars. The electricity used for training was enough to power a small country.

  • What are the different sizes of the Llama 3.1 model and what do the 'B' in the sizes represent?

    -Llama 3.1 comes in three sizes: 8B, 70B, and 405B. The 'B' stands for billions of parameters, which are the variables the model uses to make predictions.

  • What is the open-source status of Llama 3.1 and what are the restrictions?

    -Llama 3.1 is open source in the sense that developers can use it to make money off of it, as long as their app does not have 700 million monthly active users. In such cases, a license from Meta is required.

  • What is not open source about the Llama 3.1 model?

    -The training data used for Llama 3.1 is not open source. It may include a wide range of user-generated content such as blogs, GitHub repositories, Facebook posts, and possibly even WhatsApp messages.

  • How complex is the code used to train the Llama 3.1 model?

    -The code used to train Llama 3.1 is relatively simple, consisting of only 300 lines of Python and PyTorch, along with a library called Fairscale for distributing training across multiple GPUs.

  • What is the advantage of the model weights being open for developers?

    -The open model weights are a huge advantage for developers as they can self-host their own model instead of paying for the GPT 4 API, which can be costly.

  • What are the limitations of self-hosting the Llama 3.1 model?

    -Self-hosting the Llama 3.1 model is not cost-effective due to the large size of the model weights, which are 230 GB. Even with high-end hardware like an RTX 490, running the model can be challenging.

  • How can one try Llama 3.1 for free without self-hosting?

    -Llama 3.1 can be tried for free on platforms provided by Meta or other platforms like Hugging Face's Inference API or NVIDIA's AI Playground.

  • What is the initial feedback on the performance of the Llama 3.1 model?

    -The initial feedback suggests that the larger Llama 3.1 model is somewhat disappointing, while the smaller versions are quite impressive. However, the real power of Llama lies in its ability to be fine-tuned with custom data.

  • How does Llama 3.1 perform in creative tasks such as writing and poetry?

    -Llama 3.1 performs well in creative writing and poetry, but it is not considered the best among the models tested, as it still lags behind Claude 3.5 in some benchmarks.

  • What is the current state of AI advancement according to the script?

    -According to the script, despite the training of massive models by different companies, there has been a plateau in capability levels. The leap from GPT 3 to GPT 4 was significant, but since then, the gains have been incremental. Artificial super intelligence is not yet a reality, and AI has not replaced programmers as some had predicted.

Outlines

00:00

🤖 Meta's Llama 3.1 AI Model Release

The video discusses Meta's release of its largest language model, Llama 3.1, which is available for free and is arguably open-source. It took significant resources to train, utilizing 16,000 Nvidia H100 GPUs and consuming a vast amount of electricity. The model boasts 405 billion parameters and a 128,000 token context length, outperforming OpenAI's GPT-40 and even beating Claude 3.5 Sonet on certain benchmarks. However, the video suggests that benchmarks may not tell the whole story and proposes to 'vibe' with the model to determine its true capabilities. The model comes in three sizes, with the largest being 405 billion parameters, and while it's open source with some limitations, the training data remains proprietary. The video also touches on the simplicity of the training code and the potential for developers to use the model without relying on expensive APIs.

Mindmap

Keywords

💡Mark Zuckerberg

Mark Zuckerberg is the co-founder and CEO of Meta Platforms, Inc., formerly known as Facebook. In the context of the video, he is mentioned as an individual who is not only involved in personal activities but also actively engaged in the competitive landscape of artificial intelligence, indicating his influence in the tech industry.

💡Large Language Model

A large language model refers to a type of artificial intelligence system designed to process and generate human-like text based on vast amounts of data. The video discusses Meta's release of such a model, emphasizing its size and capabilities in comparison to other models like OpenAI's GPT.

💡Nvidia h100 GPUs

Nvidia h100 GPUs are high-performance graphics processing units used for tasks requiring intense computational power, such as training large AI models. The script mentions their use in training Meta's AI model, highlighting the significant resources invested in developing advanced AI technologies.

💡405 billion parameters

In the context of AI, parameters are variables that the model uses to make predictions. A model with 405 billion parameters, as mentioned in the video, is extraordinarily complex and capable of understanding and generating language at a highly sophisticated level.

💡Open Source

Open source refers to a type of software or model where the source code is made available to the public, allowing anyone to view, modify, and distribute it. The video discusses the open-source nature of Meta's AI model, with certain restrictions, and its implications for developers and the AI community.

💡Fine-tuning

Fine-tuning in AI involves adjusting a pre-trained model to perform better on a specific task by retraining it with new data. The script suggests that Meta's AI model can be fine-tuned with custom data, indicating its adaptability and potential for specialized applications.

💡Gro and Nvidia's Playground

These platforms are mentioned in the video as places where users can try out Meta's AI model for free. They represent the democratization of AI technology, allowing a broader audience to experiment with and benefit from advanced AI capabilities.

💡Incremental Gains

Incremental gains refer to small, gradual improvements. The video script uses this term to describe the current state of AI development, suggesting that despite the hype, the advancements in AI capabilities have been relatively minor and not revolutionary.

💡Artificial Super Intelligence

Artificial Super Intelligence (ASI) is a hypothetical form of AI that surpasses human intelligence in virtually every aspect. The video script discusses the current absence of ASI, contrasting it with the expectations and fears that have been expressed in the tech community.

💡Silicon Valley Mafia

The term 'Silicon Valley Mafia' is used colloquially in the video to refer to influential tech leaders and companies in Silicon Valley. It is used in a somewhat critical tone to suggest that their predictions and fears about AI advancements may be exaggerated or sensationalized.

💡Zuckerberg's Redemption Arc

In the context of the video, 'Zuckerberg's Redemption Arc' is a metaphorical way of saying that Meta's AI model could be seen as a step towards restoring public trust or reputation for Mark Zuckerberg and his company, following past controversies.

Highlights

Meta has released its biggest and baddest large language model, Llama 3.1, which is free and arguably open source.

The model was trained on 16,000 Nvidia h100 GPUs, costing hundreds of millions of dollars and using enough electricity to power a small country.

Llama 3.1 is a 405 billion parameter model with a 128,000 token context length, outperforming GPT-40 and Claude 3.5 on some benchmarks.

Llama 3.1 comes in three sizes: 8B, 70B, and 405B, where 'B' stands for billions of parameters.

The model's training data might include personal blogs, GitHub repositories, Facebook posts, and WhatsApp messages.

The training code for Llama is only 300 lines of Python and PyTorch, using the Fairscale library for GPU distribution.

Llama's model weights are open, allowing developers to build AI-powered apps without paying for the GPT-4 API.

Llama 3.1's large model is not cheap to self-host, with weights weighing 230 GB.

Initial feedback suggests that the smaller Llama models are more impressive than the larger one.

Llama 3.1 can be fine-tuned with custom data, offering potential for uncensored fine-tune models in the future.

Llama 3.1 failed to build a spelt 5 web application with runes, a yet-to-be-released feature.

In creative writing and poetry, Llama 3.1 performs well but is not the best compared to other models.

Despite advancements, AI has not yet replaced programmers, and the leap to artificial superintelligence has not occurred.

Meta is recognized as the only big tech company actively contributing to the AI space.

Llama 3.1 represents a small step for man and a giant leap for Mark Zuckerberg's redemption in the AI field.