Meet Llama 3 — an open-source large language model (LLM) created by Meta that shakes up the generative AI market and can support a wide range of use cases. This guide explores what it is and how it can change the way you work. 

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The world of generative artificial intelligence (AI) is forecast to grow over the coming years, reaching $1.3 trillion in revenue by 2032. With this boom, it’s no wonder so many companies are competing to build the best LLM around.

Meta is no different. In April 2024, it released Llama 3, an innovative and powerful LLM that sets new quality standards for other competitors. What makes this AI model distinct from other tools is that it’s open source and trained on massive sets of data.

But let’s not waste any more time. Dive into this article to explore what Meta Llama 3 is, its key features and use cases, and much more. 

What is Meta Llama 3?

Llama 3 is Meta AI’s latest LLM designed for multiple use cases, such as responding to questions in natural language, writing code, and brainstorming ideas. 

Since this AI assistant is trained on massive amounts of training data, it understands the context and responds like a human, which makes it useful for crafting content and providing information.

Llama 3, unlike other Llama models, comes with pretraining and instruction fine-tuning with 8 billion or 70 billion parameters, which makes it ideal for multiple tasks, including code generation and summarization.  

This open-source model is also freely available on Hugging Face, Microsoft Azure, NVIDIA NIM, AWS, and Google Cloud.

But what makes it different from previous versions? Let’s find out. 

How Does Llama 3 Differ from Llama 2?

What makes Llama 3 better than Llama 2? They shouldn’t be that different, right? 

Well, first of all, Meta’s Llama 3 has a 15 trillion–token dataset (enabling more efficient language encoding and better performance), which is 7x times larger than previous models.

With Llama 3’s tokenizer supporting 128,000 tokens, it makes it more capable than other versions of Llama, offering unmatched accuracy, reasoning, and reliability. 

Moreover, according to Meta, they included 4x as much code and covered 30 languages. They also added Code Shield, a guardrail that catches any faulty code Llama 3 might generate. 

In conclusion, while Llama 3 has the same transformer architecture as Llama 2, it’s better and more efficient than older generations. 

Here’s what a Reddit user has to say about it: 

“Even just from the limited testing that was possible until now, it is already clear that the 70B model is the best open-source model currently. It has already been said that other model sizes and higher context windows will follow.” 

But if Llama 3 is so good at what it does, what are its key features? 

Not a problem; we can explore this topic in the following section. 

What are Llama 3’s Key Features?

There must be something about Llama 3 that attracts so many people. After all, Llama 3 outperforms other competitors like Claude 3 or ChatGPT by a 15% average across AI benchmarks. But what gives Llama 3 the advantage?

Let’s take a look at its key features; they might provide the answer we seek: 

• Parameter models: Meta offers two-parameter models, such as Llama 3 70b and 8b. Outperforming Llama 2 in this area, this next-generation LLM enhances efficiency, improves code generation, and optimizes model performance for real-world scenarios. 
• Training datasets: To make Llama 3 the best, Meta trained it on large, high-quality datasets. Collecting over 15T tokens from public sources, Llama 3 is prepared for plenty of multilingual use cases. Meta created filtering pipelines, such as NSFW and heuristic filters, quality classifiers, and semantic deduplication. 
• Model architectures: Llama 3 maintains its decoder-only transformer architecture but it does come with several upgrades. First, Llama 3 encodes language more efficiently, significantly improving its performance. Second, Llama has integrated Grouped Query Attention (GQA) in both parameter models, which increases inference efficiency. 
• Post-training scaling: Meta developed detailed scaling laws that allowed it to predict the performance of Llama 3 on key tasks, such as code generation evaluated on HumanEval Benchmark. Plus, Meta developed an advanced training stack that automates error handling and maximizes GPU uptime. 
• Instruction fine-tuning: Meta’s new approach to post-training is a mix of rejection sampling, proximal policy optimization (PPO), and direct preference optimization (DPO). This combination improves the quality of the prompts and Llama 3’s performance. 

Well, that was a whirlwind of information. Feel free to read again if you feel something isn’t clear. 😉

If you’re ready to move on, let’s discuss Llama 3’s main use cases. 

What are the Main Llama 3 Use Cases?

From the beginning of the article, you’ve probably asked yourself, “What is Llama 3 actually good at?” This is what this section will attempt to answer. 

So, here are the most common use cases for Llama 3: 

• Chatbots: Since Llama 3 has a deep language understanding, you can use it to automate customer service. As a result, you free your agents’ time so they focus on improving relationships with clients. Your customers will also feel more engaged with your brand.
• Content creation: By using Llama 3, you can generate different types of content, varying from articles and reports to blogs and even stories. This way, you streamline the content creation process and churn out more pieces faster. 
• Email communication: Whenever you’re stumped and can’t find the right words, Llama 3 can assist you with drafting your emails and formulating the right response every time. This way, you maintain a consistent brand tone across all communication channels. 
• Data analysis reports: If you ever need to see how your business performs, Llama 3 can summarize your findings (as well as your long documents) and generate visually appealing reports with the data, so you can make more informed decisions. 
• Code generation: We’ve mentioned this several times throughout the article and it’s one of Llama 3’s main use cases. As a result, developers can generate code snippets and identify bugs. But Llama 3 also offers programming recommendations to improve the process. 

That’s all about Llama’s use cases. 

Moving forward, let’s talk about its security ecosystem. 

What is Llama 3’s Security Ecosystem?

Llama 3 plays with sensitive data, so it’s a given that, in this unstable cyber world, Meta focused on implementing robust security measures to keep that data safe.

Here’s what the Llama 3 ecosystem employs to make it safer to use:  

• Llama Code Shield – In a nutshell, Code Shield excludes the insecure code that Llama generates, making sure it’s not included in the final product. Basically, it classifies and filters unsafe code. 
• Llama Guard 2 – This security measure focuses on analyzing your text, including prompts and responses, and marking it as “safe” or “unsafe” using the MLCommons AI Safety Taxonomy standards. What makes a text unsafe are descriptions that contain discrimination, hate speech, or violence.  
• CyberSec Eval 2 – The purpose of CyberSec Eval 2 is to measure how secure the LLM is, using functionalities like offensive cybersecurity capabilities, susceptibility to prompt injection assessment, and abuse of its code interpreter. 
• torchtune – Meta’s Llama 3 uses a PyTorch-native library for authoring and experimenting with LLMs. Why? Because it offers memory-efficient training recipes for fine-tuning. 

We’re done with the theory section of this article. Now, let’s get practical and learn how to use Llama 3 using Meta AI. 

How Can You Use Llama 3?

You want to use and access Llama 3, but you don’t know where to start. Do you pet it or give it food? Nope — you simply need to fire up the Meta AI app on Facebook, Messenger, WhatsApp, Instagram, or the web. 

It works like ChatGPT, meaning that you’ll have a designated section where you can ask Meta AI anything. 

The bad news is that it’s only available in a few countries as of now, such as: 

• United States
• Australia
• Canada
• Ghana
• Jamaica
• Malawi
• New Zealand
• Nigeria
• Pakistan
• Singapore
• South Africa
• Uganda
• Zambia
• Zimbabwe

So, if you’re not in one of these countries, you might get this unfortunate message: 

However, don’t worry — Meta promises to add more countries to their list, as they’re just getting started on this journey. So stay tuned for when Llama 3 is going to be available in your area. 

For those who have access to it, all you have to do is visit llama.meta.com and click Try Meta AI in the top right corner. 

A new tab will open with Llama 3’s dashboard where you can type your input in the prompt box. 

Similar to ChatGPT, this tool will generate the required text based on your prompt.

You can also use Llama 3 via other platforms, such as Hugging Face, Perplexity AI, Replicate, GPT4All, Ollama, ChatLabs, or locally. 

Over To You!

Meta has plenty in store for Llama 3, including dabbling with multimodality and developing its largest model yet (over 400B parameters). 

This AI software has the potential to revolutionize the market and set new quality standards for other competitors. But you know who else has the potential to change the way you work? 

Guru! 

It’s an enterprise AI search, intranet, and wiki platform that improves your team’s productivity. In a nutshell, you can search everything, from chats to apps to company knowledge, and get quick answers to all queries. 

All without switching apps. 

Try Guru now to discover more. 

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