LLaMA 2: How to access and use Meta’s versatile open-source chatbot right now

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Facebook parent company Meta made waves in the artificial intelligence industry this week with the launch of LLaMA 2, an open-source large language model meant to challenge the restrictive practices by Big Tech competitors.

Unlike AI systems launched by Google, OpenAI, and others, which are closely guarded in proprietary models, Meta is freely releasing the code and data behind LLaMA 2 to enable researchers worldwide to build upon and improve the technology.

Meta’s CEO, Mark Zuckerberg, has been vocal about the importance of open-source software for stimulating innovation. “Open-source drives innovation because it enables many more developers to build with new technology,” Zuckerberg said in a Facebook post. “It also improves safety and security because when software is open, more people can scrutinize it to identify and fix potential issues.”

LLaMA 2’s open-source nature could very well lead to rapid advancements in AI, as developers worldwide can now access, analyze, and build upon the foundation model. It’s a bold move that could democratize the rapidly advancing field of AI, providing developers with powerful tools to build innovative applications and solutions.


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LLaMA 2 is an open challenge to OpenAI’s ChatGPT and Google’s Bard

LLaMA 2 comes in three sizes: with 7 billion, 13 billion, and 70 billion parameters depending on the model you choose. In comparison, OpenAI’s GPT-3.5 series has up to 175 billion parameters, and Google’s Bard (based on LaMDA) has 137 billion parameters. OpenAI famously did not disclose the number of parameters in GPT4 in its published research. The number of parameters in a model generally correlates with its performance and accuracy, but larger models require more computational resources and data to train.

The training method used for LLaMA 2 is also noteworthy and different from popular alternatives. LLaMA 2 is trained using reinforcement learning from human feedback (RLHF), learning from the preferences and ratings of human AI trainers. In contrast, ChatGPT used supervised fine-tuning, learning from labeled data provided by human annotators.

How to Access and Use LLaMA 2

Given its open-source nature, there are numerous ways to interact with LLaMA 2. Here are jsut a few of the easiest ways to access and begin experimenting with LLaMA 2 right now:

1. Interact with the Chatbot Demo
The easiest way to use LLaMA 2 is to visit llama2.ai, a chatbot model demo hosted by Andreessen Horowitz. You can ask the model questions on any topic you are interested in, or request creative content by using specific prompts. For example, you can ask “Who is the president of France?” or “Write a poem about love.” You can also change the chat mode between balanced, creative, and precise to suit your preferences. This is the best way to get started and to begin stress-testing the new model.

2. Download the LLaMA 2 Code
If you want to run LLaMA 2 on your own machine or modify the code, you can download it directly from Hugging Face, a leading platform for sharing AI models. You will need a Hugging Face account and the necessary libraries and dependencies to run the code. You can find the installation instructions and documentation on the LLaMA 2 repository.

3. Access through Microsoft Azure
Another option to access LLaMA 2 is through Microsoft Azure, a cloud computing service that offers various AI solutions. You can find LLaMA 2 on the Azure AI model catalog, where you can browse, deploy, and manage AI models. You will need an Azure account and subscription to use this service. This method is recommended for more advanced users.

4. Access through Amazon SageMaker JumpStart
You can also experiment with and deploy LLaMA 2 via Amazon SageMaker JumpStart, a popular hub for algorithms, models, and solutions. SageMaker JumpStart simplifies the process of building, training, and deploying machine learning models with just a few clicks. You will need an Amazon Web Services account and subscription to use this service. This is another method that is recommended for advanced users and programmers.

5. Try a variant at llama.perplexity.ai
Perplexity.ai is a web crawler that uses machine learning to generate general answers to your queries and then offer a series of website links. Llama.perplexity.ai combines the power of LLaMA 2 and Perplexity.ai to provide you with general answers and relevant links to your queries using the new model to power its answers. To use it, you can visit llama.perplexity.ai and type in your query in the search box. You will see a short answer from LLaMA 2 followed by a list of links that you can explore further.

Shaping the future of large language models

By launching LLaMA 2, Meta has taken a significant step in opening up AI to developers worldwide. As developers begin to customize and build upon this new model, we can expect to see a surge of innovative AI applications in the near future.

In the context of enterprise data, LLaMA 2 could unlock significant potential for businesses and organizations to develop custom AI solutions tailored to their specific needs. These could range from advanced chatbots to sophisticated data analysis tools, making LLaMA 2 a powerful tool in the enterprise AI toolbox.

Meta’s LLaMA 2 is not just an AI model, it’s a seismic shift in the AI landscape that could spark a new wave of innovation. As we begin using and experimenting with this powerful tool, we are reminded that in the world of AI, the only constant is change — and change has never looked so promising. Good luck experimenting!

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