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This article focuses on improving the modeling performance of LLMs by finetuning them using carefully curated datasets. Specifically, this article highlights strategies that involve modifying, utilizing, or manipulating the datasets for instruction-based finetuning rather than altering the model architecture or training algorithms (the latter will be topics of a future article). This article will also explain how you can prepare your own datasets to finetune open-source LLMs.

Large language models (LLMs) offer one of the most interesting opportunities for developing more efficient training methods. A few weeks ago, the NeurIPS 2023 LLM Efficiency Challenge launched to focus on efficient LLM finetuning, and this guide is a short walkthrough explaining how to participate in this competition. This article covers everything you need to know, from setting up the coding environment to making the first submission.

Peak memory consumption is a common bottleneck when training deep learning models such as vision transformers and LLMs. This article provides a series of techniques that can lower memory consumption by approximately 20x without sacrificing modeling performance and prediction accuracy.

Finetuning allows us to adapt pretrained LLMs in a cost-efficient manner. But which method should we use? This article compares different parameter-efficient finetuning methods for the latest top-performing open-source LLM, Falcon. Using parameter-efficient finetuning methods outlined in this article, it's possible to finetune an LLM in 1 hour on a single GPU instead of a day on 6 GPUs.

Training and using large language models (LLMs) is expensive due to their large compute requirements and memory footprints. This article will explore how leveraging lower-precision formats can enhance training and inference speeds up to 3x without compromising model accuracy.

Pretrained large language models are often referred to as foundation models for a good reason: they perform well on various tasks, and we can use them as a foundation for finetuning on a target task. As an alternative to updating all layers, which is very expensive, parameter-efficient methods such as prefix tuning and adapters have been developed. Let's talk about one of the most popular parameter-efficient finetuning techniques: Low-rank adaptation (LoRA). What is LoRA? How does it work? And how does it compare to the other popular finetuning approaches? Let's answer all these questions in this article!

In the rapidly evolving field of artificial intelligence, utilizing large language models in an efficient and effective manner has become increasingly important. Parameter-efficient finetuning stands at the forefront of this pursuit, allowing researchers and practitioners to reuse pretrained models while minimizing their computational and resource footprints. This article explains the broad concept of finetuning and discusses popular parameter-efficient alternatives like prefix tuning and adapters. Finally, we will look at the recent LLaMA-Adapter method and see how we can use it in practice.

Previously, I shared an article using multi-GPU training strategies to speed up the finetuning of large language models. Several of these strategies include mechanisms such as model or tensor sharding that distributes the model weights and computations across different devices to work around GPU memory limitations. However, many of us don't have access to multi-GPU resources. So, this article illustrates a simple technique that works as a great workaround to train models with larger batch sizes when GPU memory is a concern: gradient accumulation.

When it comes to productivity workflows, there are a lot of things I'd love to share. However, the one topic many people ask me about is how I keep up with machine learning and AI at large, and how I find interesting papers.

This blog post outlines techniques for improving the training performance of your PyTorch model without compromising its accuracy. To do so, we will wrap a PyTorch model in a LightningModule and use the Trainer class to enable various training optimizations. By changing only a few lines of code, we can reduce the training time on a single GPU from 22.53 minutes to 2.75 minutes while maintaining the model's prediction accuracy. Yes, that's a 8x performance boost!