Fixed typo and formatting

app/src/content/article.mdx

@@ -90,7 +90,7 @@ These principles were not decided in a vacuum. The library _evolved_ towards the
 <li class="tenet">
 <a id="source-of-truth"></a>
 <strong>Source of Truth</strong>
-<p>We aim be the [source of truth for all model definitions](https://huggingface.co/blog/transformers-model-definition). This is not a tenet, but something that guides our decisions. Model implementations should be reliable, reproducible, and faithful to the original performances.</p>
+<p>We aim to be the [source of truth for all model definitions](https://huggingface.co/blog/transformers-model-definition). This is not a tenet, but something that guides our decisions. Model implementations should be reliable, reproducible, and faithful to the original performances.</p>
 <em>This overarching guideline ensures quality and reproducibility across all models in the library.</em>
 </li>
 
@@ -704,7 +704,7 @@ Keep VLM embedding mix in the modeling file (semantics), standardize safe helper
 
 ### On image processing and processors
 
-Deciding to become a `torch`-first library meant relieving a tremendous amount of support for `jax ` and `TensorFlow`, and it also meant that we could be more lenient about the amount of torch-dependent utilities that we were able to accept. One of these is the _fast processing_ of images. Where inputs were once minimally assumed to be ndarrays, enforcing native `torch` and `torchvision` inputs allowed us to massively improve processing speed for each model.
+Deciding to become a `torch`-first library meant relieving a tremendous amount of support for `jax` and `TensorFlow`, and it also meant that we could be more lenient about the amount of torch-dependent utilities that we were able to accept. One of these is the _fast processing_ of images. Where inputs were once minimally assumed to be ndarrays, enforcing native `torch` and `torchvision` inputs allowed us to massively improve processing speed for each model.
 
 The gains in performance are immense, up to 20x speedup for most models when using compiled torchvision ops. Furthermore, let us run the whole pipeline solely on GPU.