Llama Drama: Inside Meta’s New AI Marvel
As someone passionate about Artificial Intelligence (AI), I have been excited to learn about the latest development from Meta, the LLaMA 3. This Large Language Model (LLM) represents a significant breakthrough in the field, as it is one of the most advanced language models available today. In this article, we will delve deeper into Meta’s LLaMA 3 and explore its features.
Table of Contents
1. Introduction
a. The Evolution: Llama’s Growth
b. Key Differentiator: Llamas are Unique!
2. The Model’s Backend
3. Key Features
a. Broad Availability: Llamas for Everyone!
b. Trust and Safety Tools: Because Llamas Can Be Wild
c. Performance and Capabilities: Llama Flexes Its Muscles
4. Future Direction
5. Conclusion
1. Introduction
LLaMA is an acronym for Large Language Model Meta AI, which refers to a collection of cutting-edge language models developed by Meta starting in February 2023. The latest version of LLaMA is LLaMA 3. It was released on April 18, 2024, and has been announced in two sizes: 8B and 70B parameters; we’ll understand what these parameters mean in the coming subsection.
LLaMA 3 comes in pre-trained and instruction-tuned variants. Pre-training gives LLMs fundamental knowledge and training, while instruction-tuning makes the models task-specific. Before diving further into LLaMA 3, let’s look at how far the LLaMA models have come since their inception.
a. The Evolution: Llama’s Growth
To understand the development of the model, let’s first clarify what it means when the company says that the model is trained on, for example, 70 billion parameters. The number of parameters in an LLM refers to the number of weights and biases in the neural network of the model. These parameters are numerical values that start as random coefficients and are adjusted during training to minimize loss. A model with more parameters can typically identify or derive more complex patterns from data, resulting in better outputs.
LLaMA-1, which came out in February 2023, was trained using four different model sizes: 7, 13, 33, and 65 billion parameters and on a data set with 1.4 trillion tokens (Tokens are the smallest text unit that a language model processes, such as a word or phrase.) According to Meta, the 13B parameter model outperformed GPT-3, which has 175B parameters, on most NLP benchmarks. The largest LLaMA-1 model was also competitive with state-of-the-art LLM models like PaLM and Chinchilla. Meta originally released LLaMA-1’s model weights to the research community under a non-commercial license. However, within a week of its release, the weights were leaked to the public on 4chan via BitTorrent.
On July 18, 2023, Meta announced the release of LLaMA-2, the next generation of LLaMA, in partnership with Microsoft. LLaMA-2 was released in three different model sizes: 7, 13, and 70 billion parameters and was trained on a data set with 2 trillion tokens.
Although the model architecture remained largely unchanged from LLaMA-1 models, 40% more data was used to train the foundational models in LLaMA-2. Unlike LLaMA-1, all models were released with weights and were free for multiple commercial use cases.
Fast forward to 2024, Meta released the LLaMA-3 on April 18 in two sizes: 8 and 70 billion parameters.
These models were trained on massive text data gathered from publicly available sources comprising around 15 trillion tokens. Additionally, the models were fine-tuned on publicly available instruction datasets and more than 10 million human-annotated examples.
b. Key Differentiator: Llamas are Unique!
Undoubtedly, the LLaMA is a competitor to Google’s Gemini and Microsoft’s OpenAI. While LLaMA is similar to these platforms from a technical standpoint, the key differentiator, in my opinion, is that LLaMA is free for almost anyone to use for research and commercial purposes.
In a recent interview, Sam Altman, the CEO of OpenAI, said that if startups focus on building on top of OpenAI’s current models, future models will steamroll those startups. While his statement’s intent is definitely debatable, it made it clear to me that one thing is for sure: Meta’s decision to offer LLaMA for free for research and commercial use places them in a strong position in the AI race.
2. The Model’s Backend
LLaMA is a language model that uses the transformer architecture and auto-regressive approach to generate text.
Let’s understand the transformer model first. This architecture has revolutionized the way machines understand and generate human language. Its design is uniquely suited to handle the complexities of natural language because it can process words in relation to all other words in a sentence rather than sequentially.
At its core, the transformer architecture comprises two main components: the encoder and the decoder. Each component consists of layers that are stacks of self-attention and feed-forward neural networks.
- Encoder: The encoder’s job is to process the input text. It reads and converts text into a list of vectors, one vector per input token. What makes the transformer stand out is its use of self-attention mechanisms within the encoder. These mechanisms allow the model to weigh the importance of each word in a sentence in relation to every other word, thus capturing nuances like context, syntax, and semantics. The encoder transforms these insights into a rich, contextualized set of features representing the original input.
- Decoder: While the encoder provides a context understanding, the decoder focuses on producing a coherent and contextually appropriate output. Like the encoder, the decoder also utilizes self-attention mechanisms. However, it also includes a second layer of attention, which helps the decoder focus on relevant parts of the input sentence and facilitates more accurate predictions.
Moving on to auto-regressive models, auto-regressive models help in generating logical and linguistically correct text. They predict each token by considering previous tokens.
When LLaMA 3 generates text, it starts with an initial word or a set of words and predicts the next word based on what it has generated. Once a new word is generated, it is added to the sequence, and the process repeats itself. This step-by-step prediction ensures that each word is chosen in consideration of the previous text, thereby creating a coherent and context-aware output.
Together, the transformer architecture and the auto-regressive approach enable LLaMA 3 to perform complex language tasks with high sophistication and accuracy.
3. Key Features:
a. Broad Availability: Llamas for Everyone!
The LLaMA 3 model is open-source and integrated into Meta AI. The company has expanded the availability of LLaMA 3 with English language support in several countries, including Australia, Canada, Ghana, Jamaica, Malawi, New Zealand, Nigeria, Pakistan, Singapore, South Africa, Uganda, Zambia, and Zimbabwe.
Moreover, you can access Meta AI on Facebook, Instagram, WhatsApp, Messenger, and the web, making it easily accessible to the average consumer. People will soon also be able to try out multimodal Meta AI on the Ray-Ban Meta smart glasses.
Meta has also announced that LLaMA 3 models will soon be available on various platforms such as AWS, Databricks, Google Cloud, Hugging Face, Kaggle, IBM WatsonX, Microsoft Azure, NVIDIA NIM, and Snowflake. Additionally, they will be supported by hardware platforms offered by AMD, AWS, Dell, Intel, NVIDIA, and Qualcomm.
“Llama 3 will be everywhere.” — Meta
b. Trust and Safety Tools: Because Llamas Can Be Wild
LLaMA 3 models might be technically outstanding, but they’re not immune to chaos. Meta recognizes that safety considerations must permeate every stage of AI development. From designing the LLaMA base model to deploying it, they evaluate and address risks systematically.
Meta also provides tools and resources for developers to build responsibly. These include:
- LLaMA Guard 2: A safeguard model aimed toward Human-AI conversation use cases.
- Code Shield: Adds support for inference-time filtering of insecure code produced by LLMs.
- CyberSec Eval 2: A benchmark suite that quantifies LLM security risks and capabilities.
LLaMA 3 also undergoes rigorous red-teaming and fine-tuning to mitigate risks. These efforts focus on critical areas such as cybersecurity, biological, chemical, and other threats.
Finally, The Responsible Use Guide is a valuable resource, outlining considerations for developers as they create their own products using LLaMA 3.
c. Performance and Capabilities: Llama Flexes Its Muscles
LLaMA 3 has two sizes: the 8 and 70 billion parameter models. In the blog post announcing LLaMA 3, they compare the models’ performance on various benchmarks to a few equivalent models.
Although the company has not yet compared LLaMA 3 with the current state-of-the-art models such as GPT-4, Claude Opus, etc., they are on track to release models in LLaMA 3 with over 400B parameters, bringing new capabilities such as multimodality, conversing in multiple languages, and enhanced robustness. We can expect a proper comparison when these models are out.
4. Future Direction
The Llama 3 8B and 70B models only mark the beginning of LLaMA 3. Over the coming months, the company expects to introduce new capabilities, longer context windows, additional model sizes, and enhanced performance in LLaMA 3.
Meta is also developing a multimodal version of LLaMA 3. This will allow it to work with other modalities, like images, handwritten text, video footage, and audio clips. Meta promised to publish a detailed research paper once they are done training Llama 3.
“We’re committed to the continued growth and development of an open AI ecosystem for releasing our models responsibly. We have long believed that openness leads to better, safer products, faster innovation, and a healthier overall market. This is good for Meta, and it is good for society.” — Meta.
Conclusion
As we conclude, it’s evident that LLaMA 3 is not just another version but a significant jump in AI technology. It reflects Meta’s dedication to advancing AI in an open and accessible way, reshaping how we engage with and benefit from machine intelligence.
By prioritizing broad accessibility, integrating multimodal capabilities, and ensuring a strong focus on safety and ethics, LLaMA 3 will impact various fields, such as academic research and commercial applications, making advanced AI tools available to people worldwide.
This move speeds up innovation and democratizes access to cutting-edge technology, ensuring that the advantages of AI advancements are widely shared. As LLaMA 3 continues to evolve and integrate into various platforms and devices, its impact on everyday and specialized tasks is expected to be significant, indicating a new era of AI-driven solutions tailored to meet the diverse needs of users worldwide.
You can access Meta AI on the web here. You can also use Meta AI through Facebook, Instagram, WhatsApp, and Messenger. In case Meta AI isn’t available in your country yet, try chatting with LLaMA 3 in Hugging Face here. Finally, you can visit the LLaMA 3 website to download the models directly to your system.
Thanks for reading!
