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             This open-source book is here to changes that. Starting from the basics, we'll walk you through the knowledge necessary to scale the training of large language models from one GPU to tens, hundreds and even thousands of GPUs, illustrating theory with practical code examples and reproducible benchmarks.
         </p>
 
-        <p>As the size of the clusters used to train these models grew, various techniques such as data parallelism, tensor parallelism, pipeline parallelism or context parallelism as well as ZeRO or kernel fusion have been invented to makes sure that GPUs are highly utilized at all times. This significantly reduces training time and makes the best use of this expensive hardware. Even more, as the challenge of scaling up AI training goes beyond just building the initial models and teams have found that fine-tuning large models on specialized data often produces the best results, generally involving the same distributed training techniques. In this book we'll progressively go over all of these techniques –from the simplest to the most raffined one– while keeping a single story-line to understand where each method comes from.</p>
+        <p>As the size of the clusters used to train these models grew, various techniques such as data parallelism, tensor parallelism, pipeline parallelism or context parallelism as well as ZeRO or kernel fusion have been invented to makes sure that GPUs are highly utilized at all times. This significantly reduces training time and makes the best use of this expensive hardware. Even more, as the challenge of scaling up AI training goes beyond just building the initial models and teams have found that fine-tuning large models on specialized data often produces the best results, generally involving the same distributed training techniques. In this book we'll progressively go over all of these techniques –from the simplest to the most refined one– while keeping a single story-line to understand where each method comes from.</p>
 
         <aside>If you have questions or remarks open a discussion on the <a href="https://huggingface.co/spaces/nanotron/ultrascale-playbook/discussions?status=open&type=discussion">Community tab</a>!</aside>
 
-        <p>We'll assumes you have some simple basic knowledge about current LLM architecture and are roughtly familiar with how deep learning model are trained, but you can be generally new to distributed training. If needed, the basics of model training can be found in great courses found at <a href="https://www.deeplearning.ai">DeepLearning.ai</a> or on the <a href="https://pytorch.org/tutorials/beginner/basics/intro.html">PyTorch tutorial sections</a>. This book can be seen as the second part of a trilogy following our first blog on processing data for pre-training, the so-called “<a href="https://huggingface.co/spaces/HuggingFaceFW/blogpost-fineweb-v1">FineWeb blog post</a>”. Having read both blog posts, you should have almost all the core knowledge needed to fully understand how how performing LLMs are being built nowadays, just missing some final spices regarding data mixing and architecture choices to complete the recipe (stay tuned for part three…).</p>
+        <p>We'll assume you have some simple basic knowledge about current LLM architecture and are roughtly familiar with how deep learning model are trained, but you can be generally new to distributed training. If needed, the basics of model training can be found in great courses found at <a href="https://www.deeplearning.ai">DeepLearning.ai</a> or on the <a href="https://pytorch.org/tutorials/beginner/basics/intro.html">PyTorch tutorial sections</a>. This book can be seen as the second part of a trilogy following our first blog on processing data for pre-training, the so-called “<a href="https://huggingface.co/spaces/HuggingFaceFW/blogpost-fineweb-v1">FineWeb blog post</a>”. Having read both blog posts, you should have almost all the core knowledge needed to fully understand how how performing LLMs are being built nowadays, just missing some final spices regarding data mixing and architecture choices to complete the recipe (stay tuned for part three…).</p>
 
         <aside>We are extremely thankful to the whole <a href="https://distill.pub/">distill.pub</a> team for creating
             the template on which we based this blog post.</aside>