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Congratulations!

1. Congratulations!

You've now reached the end of this course on working with Llama 3. Well done!

2. Let's recall

Over the past chapters, we've explored how to use Llama 3 for various tasks. We started by learning how to run Llama locally using llama-cpp-python, allowing us to generate responses directly on our own machines without relying on external APIs.

3. Let's recall

Next, we explored tuning decoding parameters, like temperature, top-k, and top-p, to control response behavior. We then looked at

4. Let's recall

assigning roles within Llama conversations, customizing the assistant's behavior for specific tasks. To make responses more relevant, we guided

5. Let's recall

unstructured outputs using precise prompts, stop words, and prompting techniques. We also explored structured

6. Let's recall

JSON responses, helpful in formatting Llama's output for automation and data processing. Finally, we built

7. Let's recall

multi-turn conversations, allowing Llama to track conversation history and provide context-aware responses.

8. What's next?

If you'd like to continue your learning journey with Llama, we have more in store! As part of our Llama skill track, you'll be able to complete a course that takes you through the process of fine-tuning Llama, as well as a hands-on project where you'll use Llama to automate tasks.

9. Thank you!

Thank you for joining us on this learning journey. We look forward to seeing what you build with Llama 3!

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