<h2>About this course</h2>
<p>The Llama Cookbook is Meta's official guide to building with Llama, one of the most capable open-source AI models available today. This course walks you through real, production-ready examples for inference, <a href="/topics/fine-tuning">fine-tuning</a>, and Retrieval-Augmented Generation (<a href="/topics/rag">RAG</a>)—three essential skills for anyone working with modern language models. Created by Meta's team, you're learning directly from the builders who made Llama.</p>

<h2>What you'll learn</h2>
<ul>
<li>Run Llama models efficiently for inference on different hardware setups</li>
<li>Fine-tune Llama on your own data to create specialized, domain-specific AI models</li>
<li>Build Retrieval-Augmented Generation (RAG) systems that let models reference external knowledge</li>
<li>Optimize model performance for speed and cost in production environments</li>
<li>Work with <a href="/topics/prompt-engineering">prompt engineering</a> techniques specific to Llama's strengths</li>
<li>Deploy and integrate Llama into real applications and workflows</li>
<li>Understand the practical trade-offs between model size, speed, and accuracy</li>
</ul>

<h2>Who this is for</h2>
<p>This course is designed for <a href="/for/developers">developers</a> and engineers who want hands-on experience building with cutting-edge AI models. You're ready if you have some coding experience and want to move beyond chatbots into building custom AI systems.</p>
<ul>
<li>Software engineers — develop production AI applications that run efficiently on your infrastructure</li>
<li><a href="/for/data-scientists">Data scientists</a> — fine-tune models on proprietary datasets to unlock custom intelligence</li>
<li>AI <a href="/for/researchers">researchers</a> — experiment with open-source models and contribute to the broader AI community</li>
<li>Startup founders — build AI-powered features without vendor lock-in or massive infrastructure costs</li>
</ul>

<h2>Prerequisites</h2>
<p>Solid comfort with Python programming, basic understanding of how <a href="/topics/neural-networks">neural networks</a> work, and familiarity with command-line tools. A GPU isn't required to start, but you'll appreciate having one for faster training and inference.</p>

<h2>Why this matters for Indian learners</h2>
<p>Open-source AI models like Llama are reshaping how Indian tech companies build and deploy AI—from fintech startups optimizing for low-bandwidth regions to enterprises building customer-facing AI without expensive cloud licensing. Companies like Infosys, TCS, and dozens of Indian AI startups are actively hiring engineers who can work with models like Llama. Learning to fine-tune and deploy these models positions you for roles that command competitive salaries, especially in Bangalore, Pune, and Hyderabad's growing AI ecosystem.</p>

<h2>Frequently asked questions</h2>
<h3>Is this course really free?</h3>
<p>Yes, completely free. The course is hosted on GitHub and maintained by Meta with no paywalls or hidden costs.</p>
<h3>How long will it take to complete?</h3>
<p>Plan for around 10 hours total. That works well as a 2–3 hour per week commitment over a month, giving you time to practice between sessions.</p>
<h3>Will I get a certificate?</h3>
<p>This course doesn't offer an official certificate, but you'll gain hands-on portfolio pieces—fine-tuned models, working RAG systems, and deployed applications—that matter far more to employers than a credential.</p>

Llama, PyTorch, open-source AI from Meta.

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