Zuck's new Llama is a beast
TLDRMark Zuckerberg's Meta has released Llama 3.1, a large language model with 405 billion parameters, surpassing GPT-40 and Claude 3.5 in some benchmarks. Although the model is open-source with a caveat for apps with over 700 million monthly active users, the training data remains proprietary. Llama 3.1, available in three sizes, has shown mixed results in initial feedback, with the smaller versions outperforming the largest. The model's real potential lies in its ability to be fine-tuned with custom data, promising future advancements in AI capabilities.
Takeaways
- π Mark Zuckerberg's company, Meta, has released a large language model called Llama 3.1, which is free and arguably open source.
- π° The model was trained on 16,000 Nvidia h100 GPUs, costing hundreds of millions of dollars and using a significant amount of electricity.
- π’ Llama 3.1 comes in three sizes: 8B, 70B, and 405B, with 'B' referring to billions of parameters.
- π The model is considered superior to Open AI's GPT-40 and even beats Claude 3.5 Sonet on some benchmarks.
- π€ Despite being open source, the training data used for Llama is not open source and might include personal data from various sources.
- π¨βπ» The training code for Llama is simple, consisting of only 300 lines of Python and PyTorch, utilizing the Fair Scale library.
- π» The model weights are open, allowing developers to build AI-powered apps without needing to pay for the GPT-4 API.
- π Llama 3.1 can be self-hosted, but the model's size (230 GB) and computational requirements make it expensive to run on personal hardware.
- π³ Initial feedback suggests that the larger Llama model is somewhat disappointing, while the smaller versions are more impressive.
- π Llama's potential lies in its ability to be fine-tuned with custom data, which could lead to powerful applications in the future.
- π€ The advancements in AI models from different companies seem to be plateauing, with no significant leap towards artificial superintelligence.
Q & A
What is the main topic of the video script?
-The main topic of the video script is the release of Meta's new large language model, Llama 3.1, and its comparison with other AI models like Open AI's GPT 40 and Claude 3.5 Sonet.
How many different sizes does Llama 3.1 come in?
-Llama 3.1 comes in three sizes: 8 billion parameters, 70 billion parameters, and 405 billion parameters.
What is the significance of the model being open source?
-The significance of the model being open source is that developers can use and modify the model without having to pay for an API, which can lead to more innovation and accessibility in AI applications.
What are the limitations of using Llama 3.1 if your app has a large user base?
-If your app has 700 million monthly active users, you need to request a license from Meta to use Llama 3.1, as it is not fully open source for such large-scale applications.
How much did the training of Llama 3.1 cost in terms of electricity and resources?
-The training of Llama 3.1 used 16,000 Nvidia H100 GPUs and consumed enough electricity to power a small country, likely costing hundreds of millions of dollars.
What is the length of the context that Llama 3.1 can handle?
-Llama 3.1 can handle a context length of 128,000 tokens.
What is the difference between Llama 3.1 and other models like Mixol in terms of architecture?
-Llama 3.1 uses a relatively simple decoder-only Transformer architecture, unlike Mixol, which uses a mixture of experts approach.
What is the size of Llama 3.1's model weights?
-The model weights of Llama 3.1 weigh 230 GB.
How can one try Llama 3.1 without self-hosting it?
-One can try Llama 3.1 for free on platforms like Meta or other platforms like Hugging Face's Inference API or NVIDIA's AI Playground.
What is the reviewer's opinion on the performance of Llama 3.1 in creative tasks?
-The reviewer finds Llama 3.1 to be decent in coding and creative writing and poetry, but not the best they have ever seen, as it still lags behind Claude 3.5 Sonet in some aspects.
What is the reviewer's perspective on the current state of AI advancements?
-The reviewer believes that despite multiple companies training massive models, there have only been small incremental gains, and the promised advancements to artificial super intelligence have not yet materialized.
Outlines
πββοΈ Meta's Llama 3.1: A Free and Open Source AI Model
Mark Zuckerberg's Meta has released Llama 3.1, a large language model that is free and arguably open source. This model, trained on 16,000 Nvidia H100 GPUs, is a massive 405 billion parameter model with a 128,000 token context length. It is considered superior to Open AI's GPT-40 and even beats Claude 3.5 Sonet in some benchmarks. However, the model's true effectiveness is tested through interaction. Llama 3.1 comes in three sizes: 8B, 70B, and 405B, with B representing billions of parameters. The model is open source, but with restrictions on commercial use if the app has over 700 million monthly active users. The training data is not open source and may include a wide range of user-generated content. The model's code is simple, consisting of only 300 lines of Python and PyTorch, and uses the Fair Scale library for GPU distribution. The model weights are open, which is a significant advantage for developers. Users can try Llama 3.1 for free on platforms like Meta or Gro, or Nvidia's Playground.
Mindmap
Keywords
π‘Mark Zuckerberg
π‘Large Language Model
π‘16,000 Nvidia h100 gpus
π‘405 billion parameter model
π‘Open AI
π‘Llama 3.1
π‘Open source
π‘Fair scale
π‘Model weights
π‘Fine-tuning
π‘AI hype
Highlights
Meta released its biggest and baddest large language model, 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.
The model has 405 billion parameters and a 128,000 token context length, outperforming OpenAI's GPT-40 and Claude 3.5 Sonet in some benchmarks.
Llama 3.1 is available in three sizes: 8B, 70B, and 405B, with 'B' referring to billions of parameters.
More parameters can capture more complex patterns, but it doesn't always equate to a better model.
GPT-4 is rumored to have over 1 trillion parameters, but the true numbers are not confirmed.
Llama is open source, allowing for monetization unless the app has 700 million monthly active users, in which case a license is needed from Meta.
The training data is not open source and may include personal information from various sources.
The code used to train the model consists of only 300 lines of Python and PyTorch, with the Fairscale library for GPU distribution.
The model weights are open, which is beneficial for developers building AI-powered apps.
Self-hosting the model is not cost-effective due to its large size of 230 GB.
Initial feedback suggests that the larger Llama model is disappointing, while the smaller ones are impressive.
Llama's real power lies in its ability to be fine-tuned with custom data.
The model failed to correctly build a 'spelt 5 web application with runes' in a single shot, unlike Claude 3.5 Sonet.
Llama performs decently in coding tasks but is still behind Claude in overall performance.
AI advancements have plateaued with multiple companies reaching similar capability levels.
Meta is considered the only big tech company actively contributing to the AI space.
The video suggests that artificial super intelligence is not yet a reality, contrary to some expectations.
Llama is seen as a step forward for AI, despite not living up to the hype of replacing programmers or achieving light speed advancements.