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 ## Introduction 
 
 The `transformers` library, built with `PyTorch`, supports all state-of-the-art LLMs, many VLMs, task-specific vision language models, video models, audio models, table models, classical encoders, to a global count of almost 400 models. 
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 The name of the library itself is mostly majority driven as many models are not even transformers architectures, like Mamba, Zamba, RWKV, and convolution-based models.
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 Regardless, each of these is wrought by the research and engineering team that created them, then harmonized into a now famous interface, and callable with a simple `.from_pretrained` command.
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 Inference works for all models, training is functional for most. The library is a foundation for many machine learning courses, cookbooks, and overall, several thousands other open-source libraries depend on it. All models are tested as part of a daily CI ensuring their preservation and reproducibility. Most importantly, it is _open-source_ and has been written by the community for a large part.
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 This isn't really to brag but to set the stakes: what does it take to keep such a ship afloat, made of so many moving, unrelated parts? 
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 The ML wave has not stopped, there's more and more models being added, at a steadily growing rate. `Transformers` is widely used, and we read the feedback that users post online. Whether it's about a function that had 300+ keyword arguments, duplicated code and helpers, and mentions of `Copied from ... ` everywhere, along with optimisation concerns. Text-only models are relatively tamed, but multimodal models remain to be harmonized.
 
 Here we will dissect what is the new design philosophy of transformers, as a continuation from the existing older [philosophy](https://huggingface.co/docs/transformers/en/philosophy) page, and an accompanying [blog post from 2022](https://huggingface.co/blog/transformers-design-philosophy) . Some time ago I dare not say how long, we discussed with transformers maintainers about the state of things. A lot of recent developments were satisfactory, but if we were only talking about these, self-congratulation would be the only goalpost. Reflecting on this philosophy now, as models pile up, is essential and will drive new developments. 

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         <h2>Introduction</h2>
-<p>The <code>transformers</code> library, built with <code>PyTorch</code>, supports all state-of-the-art LLMs, many VLMs, task-specific vision language models, video models, audio models, table models, classical encoders, to a global count of almost 400 models.</p>
-<p>The name of the library itself is mostly majority driven as many models are not even transformers architectures, like Mamba, Zamba, RWKV, and convolution-based models.</p>
-<p>Regardless, each of these is wrought by the research and engineering team that created them, then harmonized into a now famous interface, and callable with a simple <code>.from_pretrained</code> command.</p>
-<p>Inference works for all models, training is functional for most. The library is a foundation for many machine learning courses, cookbooks, and overall, several thousands other open-source libraries depend on it. All models are tested as part of a daily CI ensuring their preservation and reproducibility. Most importantly, it is <em>open-source</em> and has been written by the community for a large part.</p>
-<p>This isn’t really to brag but to set the stakes: what does it take to keep such a ship afloat, made of so many moving, unrelated parts?</p>
-<p>The ML wave has not stopped, there’s more and more models being added, at a steadily growing rate. <code>Transformers</code> is widely used, and we read the feedback that users post online. Whether it’s about a function that had 300+ keyword arguments, duplicated code and helpers, and mentions of <code>Copied from ... </code> everywhere, along with optimisation concerns. Text-only models are relatively tamed, but multimodal models remain to be harmonized.</p>
+<p>The <code>transformers</code> library, built with <code>PyTorch</code>, supports all state-of-the-art LLMs, many VLMs, task-specific vision language models, video models, audio models, table models, classical encoders, to a global count of almost 400 models.
+The name of the library itself is mostly majority driven as many models are not even transformers architectures, like Mamba, Zamba, RWKV, and convolution-based models.
+Regardless, each of these is wrought by the research and engineering team that created them, then harmonized into a now famous interface, and callable with a simple <code>.from_pretrained</code> command.
+Inference works for all models, training is functional for most. The library is a foundation for many machine learning courses, cookbooks, and overall, several thousands other open-source libraries depend on it. All models are tested as part of a daily CI ensuring their preservation and reproducibility. Most importantly, it is <em>open-source</em> and has been written by the community for a large part.
+This isn’t really to brag but to set the stakes: what does it take to keep such a ship afloat, made of so many moving, unrelated parts?
+The ML wave has not stopped, there’s more and more models being added, at a steadily growing rate. <code>Transformers</code> is widely used, and we read the feedback that users post online. Whether it’s about a function that had 300+ keyword arguments, duplicated code and helpers, and mentions of <code>Copied from ... </code> everywhere, along with optimisation concerns. Text-only models are relatively tamed, but multimodal models remain to be harmonized.</p>
 <p>Here we will dissect what is the new design philosophy of transformers, as a continuation from the existing older <a href="https://huggingface.co/docs/transformers/en/philosophy">philosophy</a> page, and an accompanying <a href="https://huggingface.co/blog/transformers-design-philosophy">blog post from 2022</a> . Some time ago I dare not say how long, we discussed with transformers maintainers about the state of things. A lot of recent developments were satisfactory, but if we were only talking about these, self-congratulation would be the only goalpost. Reflecting on this philosophy now, as models pile up, is essential and will drive new developments.</p>
 <h3>What you will learn</h3>
 <p>Every reader, whether an OSS maintainer, power user, or casual fine-tuner, will walk away knowing how to reason about the <code>transformers</code> code base, how to use it better, how to meaningfully contribute to it.