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There has been a lot of new research on the multimodal LLM front, including the latest Llama 3.2 vision models, which employ diverse architectural strategies to integrate various data types like text and images. For instance, The decoder-only method uses a single stack of decoder blocks to process all modalities sequentially. On the other hand, cross-attention methods (for example, used in Llama 3.2) involve separate encoders for different modalities with a cross-attention layer that allows these encoders to interact. This article explains how these different types of multimodal LLMs function. Additionally, I will review and summarize roughly a dozen other recent multimodal papers and models published in recent weeks to compare their approaches.

This article shows you how to transform pretrained large language models (LLMs) into strong text classifiers.��But why focus on classification? First, finetuning a pretrained model for classification offers a gentle yet effective introduction to model finetuning. Second, many real-world and business challenges revolve around text classification: spam detection, sentiment analysis, customer feedback categorization, topic labeling, and more.

This tutorial is aimed at coders interested in understanding the building blocks of large language models (LLMs), how LLMs work, and how to code them from the ground up in PyTorch. We will kick off this tutorial with an introduction to LLMs, recent milestones, and their use cases. Then, we will code a small GPT-like LLM, including its data input pipeline, core architecture components, and pretraining code ourselves. After understanding how everything fits together and how to pretrain an LLM, we will learn how to load pretrained weights and finetune LLMs using open-source libraries.

There are hundreds of LLM papers each month proposing new techniques and approaches. However, one of the best ways to see what actually works well in practice is to look at the pre-training and post-training pipelines of the most recent state-of-the-art models. Luckily, four major new LLMs have been released in the last months, accompanied by relatively detailed technical reports. In this article, I focus on the pre-training and post-training pipelines of the following models: Alibaba's Qwen 2, Apple Intelligence Foundation Language Models, Google's Gemma 2, Meta AI's Llama 3.1.

This article covers a new, cost-effective method for generating data for instruction finetuning LLMs; instruction finetuning from scratch; pretraining LLMs with instruction data; and an overview of what's new in Gemma 2.

This is an overview of the LLM development process. This one-hour talk focuses on the essential three stages of developing an LLM: coding the architecture, implementing pretraining, and fine-tuning the LLM. Lastly, we also discuss the main ways LLMs are evaluated, along with the caveats of each method.

This article covers three new papers related to instruction finetuning and parameter-efficient finetuning with LoRA in large language models (LLMs). I work with these methods on a daily basis, so it's always exciting to see new research that provides practical insights.

What a month! We had four major open LLM releases: Mixtral, Meta AI's Llama 3, Microsoft's Phi-3, and Apple's OpenELM. In my new article, I review and discuss all four of these major transformer-based LLM model releases, followed by new research on reinforcement learning with human feedback methods for instruction finetuning using PPO and DPO algorithms.

What are the different ways to use and finetune pretrained large language models (LLMs)? The three most common ways to use and finetune pretrained LLMs include a feature-based approach, in-context prompting, and updating a subset of the model parameters. First, most pretrained LLMs or language transformers can be utilized without the need for further finetuning. For instance, we can employ a feature-based method to train a new downstream model, such as a linear classifier, using embeddings generated by a pretrained transformer. Second, we can showcase examples of a new task within the input itself, which means we can directly exhibit the expected outcomes without requiring any updates or learning from the model. This concept is also known as prompting. Finally, it���s also possible to finetune all or just a small number of parameters to achieve the desired outcomes. This article discusses these types of approaches in greater depth

It's another month in AI research, and it's hard to pick favorites. This month, I am going over a paper that discusses strategies for the continued pretraining of LLMs, followed by a discussion of reward modeling used in reinforcement learning with human feedback (a popular LLM alignment method), along with a new benchmark. Continued pretraining for LLMs is an important topic because it allows us to update existing LLMs, for instance, ensuring that these models remain up-to-date with the latest information and trends. Also, it allows us to adapt them to new target domains without having them to retrain from scratch. Reward modeling is important because it allows us to align LLMs more closely with human preferences and, to some extent, helps with safety. But beyond human preference optimization, it also provides a mechanism for learning and adapting LLMs to complex tasks by providing instruction-output examples where explicit programming of correct behavior is challenging or impractical.