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@@ -8,22 +8,22 @@
     <script src="https://d3js.org/d3.v7.min.js"></script>
     <meta name="viewport" content="width=device-width, initial-scale=1">
     <meta charset="utf8">
-    <title>Transformers Feature Showcase</title>
     <link rel="stylesheet" href="style.css">
     <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/prism/1.29.0/themes/prism.min.css">
 </head>
 <body>
     <d-front-matter>
         <script id='distill-front-matter' type="text/json">{
-    "title": "Transformers Feature Showcase",
-    "description": "An interactive demonstration of transformers library features and design philosophy.",
     "published": "Aug 21, 2025",
     "authors": [{"author": "Pablo Montalvo", "authorURL": "https://huggingface.co/Molbap"}]
   }</script>
     </d-front-matter>
     <d-title>
-        <h1>Transformers Feature Showcase</h1>
-        <p>An interactive demonstration of transformers library features and design philosophy.</p>
     </d-title>
     <d-byline></d-byline>
     <d-article>
@@ -48,9 +48,13 @@
             </nav>
         </d-contents>
         <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. The name of the library itself is mostly majority driven as many models are not even transformers architectures, like Mamba/RWKV.  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>. Inference and training are supported. The library supports ML courses, cookbooks, and 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.</p>
-<p>The ML wave has not stopped, there’s more and more models being added. <code>Transformers</code> is widely used, and we read the feedback that users post. 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 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.
 This will also showcase new features you might have missed so you’ll be up-to-date.</p>
@@ -537,7 +541,7 @@ machinery is the <code>attention mask</code>, cause of confusion. Thankfully, we
                                 // Extract tenet text for tooltips
                                 const tenetTooltips = {
-                                    'source-of-truth': 'We should be a source of truth for all model definitions. Model implementations should be reliable, reproducible, and faithful to the original performances.',
                                     'one-model-one-file': 'All inference (and most of training, loss is separate, not a part of model) logic visible, top‑to‑bottom.',
                                     'code-is-product': 'Optimize for reading, diffing, and tweaking, our users are power users. Variables can be explicit, full words, even several words, readability is primordial.',
                                     'standardize-dont-abstract': 'If it\'s model behavior, keep it in the file; abstractions only for generic infra.',

     <script src="https://d3js.org/d3.v7.min.js"></script>
     <meta name="viewport" content="width=device-width, initial-scale=1">
     <meta charset="utf8">
+    <title>Scaling insanity: maintaining hundreds of model definitions</title>
     <link rel="stylesheet" href="style.css">
     <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/prism/1.29.0/themes/prism.min.css">
 </head>
 <body>
     <d-front-matter>
         <script id='distill-front-matter' type="text/json">{
+    "title": "Scaling insanity: maintaining hundreds of model definitions",
+    "description": "A peek into software engineering for the transformers library",
     "published": "Aug 21, 2025",
     "authors": [{"author": "Pablo Montalvo", "authorURL": "https://huggingface.co/Molbap"}]
   }</script>
     </d-front-matter>
     <d-title>
+        <h1>Scaling insanity: maintaining hundreds of model definitions</h1>
+        <p>A peek into software engineering for the transformers library</p>
     </d-title>
     <d-byline></d-byline>
     <d-article>
             </nav>
         </d-contents>
         <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>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.
 This will also showcase new features you might have missed so you’ll be up-to-date.</p>
                                 // Extract tenet text for tooltips
                                 const tenetTooltips = {
+                                    'source-of-truth': 'We aim to be a source of truth for all model definitions. Model implementations should be reliable, reproducible, and faithful to the original performances.',
                                     'one-model-one-file': 'All inference (and most of training, loss is separate, not a part of model) logic visible, top‑to‑bottom.',
                                     'code-is-product': 'Optimize for reading, diffing, and tweaking, our users are power users. Variables can be explicit, full words, even several words, readability is primordial.',
                                     'standardize-dont-abstract': 'If it\'s model behavior, keep it in the file; abstractions only for generic infra.',