Understand and implement the attention mechanism, a key element of transformer-based LLMs, using PyTorch.

Learn how the attention mechanism in LLMs helps convert base token embeddings into rich context-aware embeddings. Learn how the attention mechanism in LLMs helps convert base token embeddings into rich context-aware embeddings.Understand the Query, Key, and Value matrices, what they are for, how to produce them, and how to use them in attention. Understand the Query, Key, and Value matrices, what they are for, how to produce them, and how to use them in attention.Learn the difference between self-attention, masked self-attention, and cross-attention, and how multi-head attention scales the algorithm. Learn the difference between self-attention, masked self-attention, and cross-attention, and how multi-head attention scales the algorithm._This course clearly explains the ideas behind the attention mechanism. It walks through the algorithm itself and how to code it in Pytorch. Attention in Transformers: Concepts and Code in PyTorch, was built in collaboration with StatQuest, and taught by its Founder and CEO, Josh Starmer.The attention mechanism was a breakthrough that led to transformers, the architecture powering large language models like ChatGPT. Transformers, introduced in the 2017 paper “Attention is All You Need” by Ashish Vaswani and others, revolutionized AI with their scalable design. Learn how this foundational architecture works to improve your intuition about building reliable, functional, and scalable AI applications.What you’ll do: Understand the evolution of the attention mechanism, a key breakthrough that led to transformers.Learn the relationships between word embeddings, positional embeddings, and attention.Learn about the Query, Key, and Value matrices, how to produce them, and how to use them in attention.Go through the math required to calculate self-attention and masked self-attention to learn how and why the equation works the way it does.Understand the difference between self-attention and masked self-attention, and how one is used in the encoder to build context-aware embeddings and the other is used in the decoder for generative outputs.Learn the details of the encoder-decoder architecture, cross-attention, and multi-head attention, and how they are incorporated into a transformer.Use PyTorch to code a class that implements self-attention, masked self-attention, and multi-head attention._Anyone who has basic Python knowledge and wants to learn how the attention mechanism in LLMs like ChatGPT works.

Attention in Transformers: Concepts and Code in PyTorch

Systematically improve the accuracy of LLM applications with evaluation, prompting, and memory tuning.

Understand development steps, from evaluation, through prompting, self-reflection, and fine-tuning, to improve your model’s reliability and accuracy. Understand development steps, from evaluation, through prompting, self-reflection, and fine-tuning, to improve your model’s reliability and accuracy.Learn how memory tuning can increase your model performance by embedding facts into your model to reduce hallucination. Learn how memory tuning can increase your model performance by embedding facts into your model to reduce hallucination.Use the Llama 3-8b model to build an LLM application that converts text to SQL with a custom schema. Use the Llama 3-8b model to build an LLM application that converts text to SQL with a custom schema._Join our new short course, Improving Accuracy of LLM Applications with Lamini and Meta. Learn from Sharon Zhou, Co-founder & CEO of Lamini, and Amit Sangani, Senior Director of Partner Engineering, Meta.Many developers have experienced frustration with inconsistent results when working with LLM applications. This course offers a systematic approach to enhance the accuracy and reliability of your LLM applications.You will build an SQL agent, add evaluation metrics to measure performance, and use prompt engineering and self-reflection to make the model perform better. Finally, you will fine-tune the model with techniques like LoRA and memory tuning that embeds facts in model weights to reduce hallucinations.In this course, you’ll use Llama’s family of open-source models.What you’ll do: Build a text to SQL agent and simulate situations where it hallucinates to begin the evaluation process.Build an evaluation framework to systematically measure performance, including criteria for good evaluations, best practices, and how to develop an evaluation score.Learn how instruction fine-tuning enhances pre-trained LLMs to follow instructions, and how memory fine-tuning embeds facts to reduce hallucinations. Break fine-tuning myths and see how Performance-Efficient Fine-tuning (PEFT) techniques like Low-Rank Adaptation(LoRA) reduce training time by 100x and Mixture of Memory Experts (MoME) reduces it even further. Go through an iterative process of generating training data and fine-tuning, learning practical tips such as adding examples, generating variations, and filtering generated data to increase model accuracy.Start improving the accuracy of LLM applications today! _This course is ideal for anyone with intermediate Python knowledge and familiarity with large language models (LLMs) looking to build more factual and precise LLM applications. 

Improving Accuracy of LLM Applications

Understand the transformer architecture that powers LLMs to use them more effectively.

Gain an understanding of the key components of transformers, including tokenization, embeddings, self-attention, and transformer blocks, to build a strong technical foundation. Gain an understanding of the key components of transformers, including tokenization, embeddings, self-attention, and transformer blocks, to build a strong technical foundation.Understand recent transformer improvements to the attention mechanism such as KV cache, multi-query attention, grouped query attention, and sparse attention. Understand recent transformer improvements to the attention mechanism such as KV cache, multi-query attention, grouped query attention, and sparse attention.Compare tokenization strategies used in modern LLMS and explore transformers in the Hugging Face Transformers library. Compare tokenization strategies used in modern LLMS and explore transformers in the Hugging Face Transformers library._Introducing “How Transformer LLMs Work,” created with Jay Alammar and Maarten Grootendorst, authors of the “Hands-On Large Language Models” book. This course offers a deep dive into the main components of the transformer architecture that powers large language models (LLMs). The transformer architecture revolutionized generative AI. In fact, the “GPT” in ChatGPT stands for “Generative Pre-Trained Transformer.” Originally introduced in the groundbreaking 2017 paper Attention Is All You Need, by Ashish Vaswani and others, transformers were a highly scalable model for machine translation tasks. Variants of this architecture now power today’s LLMs such as those from OpenAI, Google, Meta, Cohere, and Anthropic.In their book, Jay and Maarten beautifully illustrated the underlying architecture of LLMs through insightful and easy-to-understand explanations.In this course, you’ll learn how a transformer network architecture that powers LLMs works. You’ll build the intuition of how LLMs process text and work with code examples that illustrate the key components of the transformer architecture.Key topics covered in this course include:The evolution of how language has been represented numerically, from the Bag-of-Words model through Word2Vec embeddings to the transformer architecture that captures word meanings in full context.How LLM inputs are broken down into tokens, which represent words or pieces before they are sent to the language model.The details of a transformer and the three main stages, consisting of tokenization and embedding, the stack of transformer blocks, and the language model head.The details of the transformer block, including attention, which calculates relevance scores followed by the feedforward layer, which incorporates stored information learned in training.How cached calculations make transformers faster, how the transformer block has evolved over the years since the original paper was released, and how they continue to be widely used.Explore an implementation of recent models in the Hugging Face transformer library.By the end of this course,  you’ll have a deep understanding of how LLMs process language and you’ll be able to read through papers describing models and understand the details that are used to describe these architectures. This intuition will help improve your approach to building  LLM applications._Anyone interested in understanding the inner workings of transformer architectures that power today’s LLMs.