LLM Decoding Strategies Tutorial

Join Harpreet Sahota in this video as he delves into decoding strategies for language models. From a practical, code-first perspective, Harpreet explores the intricacies of tokenization and prediction and dives into the parameters you've likely heard about but may not fully understand - temperature, top K, and top P. By examining various decoding strategies, such as greedy search, multinomial search, beam search, and contrastive search, you’ll see firsthand how these parameters and strategies significantly influence a model's token selection process. 📚 What You'll Learn: • Understanding Tokenization: Gain insights into the process that transforms text into integers, setting the stage for model processing. • Decoding Parameters Demystified: Explore how temperature, top K, and top P affect the token selection process, offering you more control over your model's output. • Decoding Strategies Explored: Delve into different decoding strategies, including greedy search and beam search, and learn how to apply them for more coherent and creative text generation. • Code-First Demonstrations: Follow along with Harpreet's intuitive, code-based walkthroughs to see these strategies in action, enhancing your understanding through practical application. Why This Video Is Crucial: If you've ever faced the frustration of a language model spewing nonsensical output, this video is for you. Decoding strategies offer a way to steer model outputs toward more meaningful, coherent, and creative results. This video not only demystifies the underlying mechanics of these strategies but also equips you with the knowledge to apply them effectively in your own projects. Resources & Community: 🧑‍🔬 Google Colab notebook https://colab.research.google.com/drive/196B_NFgon06xNXZ6VPwMsl0uLlJlSjUy?usp=sharing Deep Learning Daily Discord Community https://discord.com/invite/p9ecgRhDR8/ 🕵️‍♂️ Interested in using different decoding strategies to "vibe check" LLMs? Check out Harpreet's series on vibe checking LLMs, where he explores models in the 1, 3, 7, and 13 weight classes, offering unique insights into their behaviors and capabilities: Open Source LLMs Part 1: Testing 1B Parameter Models https://www.youtube.com/watch?v=craYnxLjlnc Open Source LLMs Part 2: Testing 3B Parameter Models https://www.youtube.com/watch?v=Ek7AgDlVoTY Open Source LLMs Part 3: Testing 7B Parameter Models https://www.youtube.com/watch?v=ZWoSPpNwwFw Open Source LLMs Part 4: Testing 13B Parameter Models https://www.youtube.com/watch?v=aT2XCWPH4BM We Want Your Feedback: 📢 Share your feedback or questions in the comments to help us create more engaging and informative content. Subscribe for More AI Insights: 🔔 Don't miss out on our upcoming videos on AI advancements and practical tutorials. Subscribe and share to stay informed and support the community. #LanguageModels #DecodingStrategies #Tokenization #AIEngineering #MachineLearning#HarpreetSahota#DeciLM