Introduction to Prompt Engineering with Llama 2 & 3 (AI Course – Part 1)

Welcome to A4D’s series on Prompt Engineering with Llama 2 & Llama 3. This series will kick off with an introduction to the powerful Llama 2 & 3 models. This includes the specialized Code Llama.

As we move forward, we’ll get hands-on experience. We’ll build multi-turn chatbots and master prompt engineering techniques through practical coding examples.

Later on, we’ll explore the Llama Guard, which is dedicated to the responsible use of AI.

In this article, we will cover the capabilities of Llama 2 and 3 models. We’ll also cover their different variants and sizes. We will, then, discuss how to use them effectively in various generative AI models’ applications, including synthetic data generation.

You’ll also learn about:

• Instruction Tuning
• Specialized Code Llama for coding tasks
• Meta’s initiatives for ethical AI through the Purple Llama project

What Makes Llama Models Different?

Open-Source Accessibility

One of the standout features of the Llama models is Meta’s decision to make the model weights publicly available. This allows developers to download, modify, experiment, and fine-tune the model or models for a variety of applications.

The Llama models’ open-source availability encourages greater innovation and collaboration. This is particularly beneficial for the global developer community. Unlike closed-source models, which are typically only accessible via API calls, Llama models provide more flexibility and access.

This democratization of AI technology significantly accelerates progress and facilitates the development of advanced AI solutions across diverse sectors.

Community Empowerment

The availability of Llama weights has empowered many teams, from large corporations to individual developers, allowing them to innovate and build impressive applications. As a result, the Llama models have seen millions of downloads.

This widespread adoption showcases the community’s eagerness to leverage these models for creating excellent solutions, from automating mundane tasks to developing sophisticated AI applications.

The open-source nature of Llama models also fosters a collaborative environment. This enables developers to share their improvements and insights, further accelerating the progress in AI technology.

Llama’s Model Sets

Base Models

The first set of models we’ll look at in our series on prompt engineering with llama 2 & Llama 3, is the base model set. These language models (LLMs) are pre-trained to predict the next word based on large datasets, such as internet text.

They haven’t received additional training to modify their behavior. This makes them ideal for developers who want to continue training models for specific tasks.

For instance, a developer working on a domain-specific application can fine-tune a base model with domain-relevant data. This will help the dev eloper achieve the desired optimal performance!

Chat Models

Next, we have the chat models. These models are perfect for powering chatbots and following instructions to answer questions or complete tasks. They are designed to handle conversational inputs and provide relevant responses.

Chat models are especially useful in customer service applications, and virtual assistance and interactive educational tools, where engaging and contextually appropriate interactions are crucial.

Code-Specific Models

The last set of models is specifically trained to understand and write computer code. These models are incredibly useful for both software engineers and individuals new to coding. They simplify the process of writing, debugging, and learning code independently.

With the right prompt engineering techniques, a rookie programmer can:

• Generate code snippets
• Understand complex codebases
• Receive step-by-step guidance on solving coding challenges.

Llama 2 Model Sizes

Llama 2 is not a single model but a family of models varying in size and training strategies. The Llama 2 collection includes models with 7 billion (7B), 13 billion (13B), and 70 billion (70B) parameters.

These models are designed to accommodate different application scenarios and computational resources.

In performance benchmarking using the MMLU (Massive Multitask Language Understanding) test, Llama 2 scored 68.9. This places it competitively between the Falcon 40B (55.4) and GPT-3.5 (70.0) models.

Both Llama 2 and Falcon 40B are freely downloadable, whereas GPT-3.5 is accessible via OpenAI. You can access them anytime and begin your journey to mastering prompt engineering.

Model Sizes and Uses

• Llama 2 7B is the smallest model, optimized for environments with limited computational resources. It is ideal for lightweight applications like natural language processing tasks… where efficiency and speed are prioritized over extensive computational power.
• Llama 2 13B: This balanced model provides a middle ground between performance and resource efficiency. It is suitable for a wide range of applications. These include medium-scale data analysis, customer service chatbots, and more complex natural language understanding tasks.
• Llama 2 70B: is the largest and most powerful model, suitable for applications requiring high accuracy and extensive computation. It is best suited for large-scale AI projects. Think advanced AI research, comprehensive data analytics, and applications requiring deep language comprehension and generation capabilities.

Instruction Tuning

Instruction-tuned models are created by further training the base models (foundation models) to better follow human language instructions. This process enhances the model’s ability to perform tasks like summarizing text or generating specific content upon request.

If you have a base Llama model and need it for writing blog posts, you can use prompt engineering to train it. This training helps the model generate content in a specific style. It can also focus on particular topics or use a preferred tone.

Introduction of Code Llama

In August 2023, Meta introduced Code Llama, a specialized extension of the Llama 2 models tailored for coding tasks. Code Llama models come in sizes of 7B, 13B, and 34B parameters, with each size offering a base version and an instruction-tuned version of Llama.

Code Llama Variants

• Base Code Llama: Derived from non-chat Llama models, it is explicitly trained for code generation and understanding. It is perfect for generating boilerplate code or assisting in complex programming tasks.
• Code Llama – Instruct: Fine-tuned for responding to human instructions related to coding tasks. This includes debugging and/or code writing. You might ask it to “debug this Python function” or “write an SQL query to retrieve user data.”
• Code Llama – Python: Specially trained for Python. It provides tools to ensure AI-generated code is safe and secure against cybersecurity threats. This variant is particularly useful for Python developers looking for code suggestions, error fixing, and security best practices.

Responsible AI with Llama: The Purple Llama Project

Meta’s commitment to responsible AI usage is encapsulated in the Purple Llama initiative, focusing on generative AI safety. This initiative includes tools and benchmark datasets to evaluate the cybersecurity risks of AI-generated code. This ensures it is safe against cyber threats.

CyberSecEval

CyberSecEval provides tools and benchmark datasets to evaluate the cybersecurity risks of AI-generated code. This ensures that the AI systems are equipped to guard against cyber threats.

For example, it can help identify whether generated code might expose vulnerabilities, like SQL injection risks or buffer overflows.

Llama Guard

Llama Guard is a safety classifier model. It’s designed to ensure that the inputs and outputs of large language models (LLMs) are safe, honest, and harmless. Think of it as the bouncer of the Llama club—ensuring only safe and appropriate content gets through.

Community and Educational Initiatives

Meta also creates educational modules for developers to understand the importance of cybersecurity in AI development. Additionally, they host hackathons and workshops focused on building secure AI applications, fostering a culture of responsibility and collaboration.

Final Thoughts

The Llama models, with their open-source nature and diverse range of applications, represent a significant step forward in machine learning and AI development. From base models to specialized Code Llama variants, these tools offer unparalleled opportunities for innovation.

Moreover, Meta’s Purple Llama initiative demonstrates a strong commitment to responsible AI use. This ensures that advancements are made with safety and security as priorities.

As we continue this series, you will gain deeper insights and practical knowledge to leverage these powerful models in your projects.

Let’s embark on this exciting journey together!

Stay tuned for my next article in this series. We will discuss how to build Llama 2 API and a multi-turn Chatbot.