AI uses large language models (LLMs) to understand and generate natural language. LLMs can learn from huge amounts of text and create texts on various topics. They can do amazing things like writing code, generating lyrics, summarizing articles and more. But some LLMs are costly and closed, while others are open but limited. That’s why Meta has launched Llama 2, its new open-source LLM.
Meta Llama 2 is designed to rival other prominent language models like ChatGPT from OpenAI and Google Bard but with a distinctive approach. In this article we will explore what Llama 2 is, what are its benefits, how it was developed and how you can get started with it.
What is a Large Language Model (LLM)?
A large language model (LLM) is a type of artificial neural network that can learn from huge amounts of text data and generate natural language texts on various topics. LLMs are trained on corpora that contain billions or trillions of words from different sources, such as books, websites, social media posts, news articles, etc.
LLMs can perform various natural language processing (NLP) tasks, such as text classification, sentiment analysis, question answering, machine translation, text summarization, text generation, etc. Some examples of popular LLMs are ChatGPT from OpenAI, Google’s Bard, Microsoft’s Turing-NLG, IBM’s Project CodeNet, etc.
LLMs are based on a technique called self-attention, which allows them to capture long-range dependencies and contextual information in texts. Self-attention is implemented using a module called Transformer, which consists of multiple layers of encoder-decoder pairs. Each layer applies self-attention to the input text and produces an output text that is more refined and relevant.
The size of an LLM is measured by the number of parameters it has. Parameters are numerical values that determine how the neural network processes the input and produces the output. The more parameters an LLM has, the more complex and powerful it is. However, more parameters also mean more computational resources and energy consumption.
How can you get started with Llama 2?
If you are interested in using Llama 2 for your own projects or experiments, you can get started by following these steps:
Read the technical overview and the research paper. These documents will give you more information about the architecture, training, performance, and evaluation of Llama. You will also learn about the challenges and limitations of LLMs and how to address them.
Follow the responsible use guide and join the open innovation community. These resources will help you use Llama 2 in a safe and ethical way and connect you with other users and developers who share your interests and goals.
What are the Benefits of Llama 2?
Llama 2 is the next generation of Meta’s open-source large language model. It is a family of pretrained and fine-tuned models that range from 7 billion to 70 billion parameters. Meta Llama 2 has several benefits that make it stand out from other open source LLMs.
Llama 2 is free for research and commercial use
One of the main advantages of Llama 2 is that it is available for free for both research and commercial use. Unlike its predecessor, Llama, which had a non-commercial license and was leaked to torrent sites, Meta Llama 2 has a commercial license that allows anyone to integrate it into their products and services.
This means that Llama 2 can be used for a variety of purposes, such as building chatbots, generating content, creating voice assistants, etc. Meta Llama 2 can also be customized and fine-tuned for specific domains and tasks, such as healthcare, education, finance, etc.
However, there are some restrictions on the use of Meta Llama 2. For example, potential licensees with more than 700 million monthly active users must request special permission from Meta to use it. Moreover, Meta Llama 2 users must follow Meta’s responsible use guide and respect the privacy and rights of others.
Llama 2 has a range of models with different sizes and capabilities
Another benefit of Llama 2 is that it offers a range of models with different sizes and capabilities. Depending on the needs and resources of the users, they can choose from the following models:
- Llama-7B: The smallest model with 7 billion parameters. It is suitable for low-resource devices and applications.
- Llama-14B: A medium-sized model with 14 billion parameters. It is suitable for general-purpose applications and tasks.
- Llama-28B: A large model with 28 billion parameters. It is suitable for high-performance applications and tasks.
- Llama-56B: A very large model with 56 billion parameters. It is suitable for advanced applications and tasks that require more complexity and diversity.
- Llama-70B: The largest model with 70 billion parameters. It is suitable for state-of-the-art applications and tasks that require the highest quality and performance.
All these models are pretrained on 2 trillion tokens of online data and have a context window of 4,096 tokens. Additionally, Meta provides a fine-tuned model called Llama-2-chat, which is optimized for conversational applications. Llama-2-chat has been trained on over 1 million human annotations and can generate fluent and engaging responses.
How was Llama 2 developed?
Llama 2 was developed by Meta AI, the research division of Meta (formerly Facebook). Meta AI is dedicated to advancing the field of artificial intelligence through open innovation and collaboration. Meta AI has a team of world-class researchers and engineers who work on various aspects of AI, such as computer vision, natural language processing, speech recognition, etc.
Llama 2 was built on top of Meta’s previous open-source large language model, Llama, which was released in February this year. Llama was pretrained on publicly available online data sources using Meta’s PyTorch framework. However, Llama had a non-commercial license and was only available to academics with certain credentials.
Llama was soon leaked to torrent sites and spread widely in the AI community. Many hobbyists and developers used Llama to create their own fine-tuned models for various purposes, such as Alpaca for chatbots, Camel for code generation, Vicuna for text summarization, etc.
Meta decided to embrace this open innovation approach and released Llama 2 with a commercial license, allowing anyone to use it for research and commercial purposes. Llama 2 was pretrained on 2 trillion tokens of online data, which is twice as much as Llama. Meta Llama 2 also has a larger context window of 4,096 tokens, which is double the size of Llama’s context window.
Meta Llama 2 was fine-tuned on over 1 million human annotations, which were collected from various sources, such as publicly available instruction datasets and Meta’s own crowdsourcing platform. The fine-tuned model, Llama-2-chat, was optimized for conversational applications and can generate fluent and engaging responses.
Llama 2 is the next generation of Meta’s open-source large language model. It is a family of pretrained and fine-tuned models that range from 7 billion to 70 billion parameters. Meta Llama 2 is free for research and commercial use, has a range of models with different sizes and capabilities, and outperforms other open source LLMs on many benchmarks.
Meta Llama 2 is a powerful and versatile tool that can help you create amazing natural language applications and experiences. Whether you want to build a chatbot, generate content, create a voice assistant, or anything else, Llama 2 can help you achieve your goals. So, what are you waiting for? Download Meta Llama 2 today and unleash your creativity!