No more magic comments (huggingface#1554)

* no more magic comments

* Also replace h1 by actual markdown

* nit: remove extra space

* Fix remaining <h1>s

* handling complex h1 heading

* Update README.md

cc @mishig25

---------

Co-authored-by: Mishig Davaadorj <dmishig@gmail.com>

Author: julien-c

1b-sentence-embeddings.md

@@ -7,8 +7,6 @@ authors:
 
 # Train a Sentence Embedding Model with 1 Billion Training Pairs
 
-<!-- {blog_metadata} -->
-<!-- {authors} -->
 
 **Sentence embedding** is a method that maps sentences to vectors of real numbers. Ideally, these vectors would capture the semantic of a sentence and be highly generic. Such representations could then be used for many downstream applications such as clustering, text mining, or question answering.
 

3d-assets.md

@@ -7,8 +7,6 @@ authors:
 
 # Practical 3D Asset Generation: A Step-by-Step Guide
 
-<!-- {blog_metadata} -->
-<!-- {authors} -->
 
 ## Introduction
 

4bit-transformers-bitsandbytes.md

@@ -13,8 +13,6 @@ authors:
 
 # Making LLMs even more accessible with bitsandbytes, 4-bit quantization and QLoRA
 
-<!-- {blog_metadata} -->
-<!-- {authors} -->
 
 LLMs are known to be large, and running or training them in consumer hardware is a huge challenge for users and accessibility. 
 Our [LLM.int8 blogpost](https://huggingface.co/blog/hf-bitsandbytes-integration) showed how the techniques in the [LLM.int8 paper](https://arxiv.org/abs/2208.07339) were integrated in transformers using the `bitsandbytes` library.

Llama2-for-non-engineers.md

@@ -6,8 +6,6 @@ authors:
 - user: abhishek
 ---
 
-<!-- {blog_metadata} -->
-<!-- {authors} -->
 
 # Non-engineers guide: Train a LLaMA 2 chatbot
 

README.md

@@ -29,19 +29,15 @@ authors:
 
 # Train your first Decision Transformer
 
-<!-- {blog_metadata} -->
-<!-- {authors} -->
-
 Your content here [...]
 ```
 
-The blog_metadata and authors HTML comments are meant to mark where in the file will be inserted the following UI elements:
+When published, the Hub will insert the following UI elements right after the blogpost's main header (i.e. the line that starts with a single `#`, aka. the `<h1>`):
+
 - "Published on [date]"
 - "Update on GitHub" button
 - avatars of the authors that were listed in authors.
 
-⚠️ Please keep the blog_metadata and authors comments exactly equal to those strings otherwise they won't be replaced.
-
 5️⃣ Then, you can add your content. It's markdown system so if you wrote your text on notion just control shift v to copy/paste as markdown.
 
 6️⃣ Modify `_blog.yml` to add your blogpost.

accelerate-deepspeed.md

@@ -6,10 +6,8 @@ authors:
 - user: sgugger
 ---
 
-<h1>Accelerate Large Model Training using DeepSpeed</h1>
+# Accelerate Large Model Training using DeepSpeed
 
-<!-- {blog_metadata} -->
-<!-- {authors} -->
 
 In this post we will look at how we can leverage the **[Accelerate](https://github.com/huggingface/accelerate)** library for training large models which enables users to leverage the ZeRO features of **[DeeSpeed](https://www.deepspeed.ai)**.
 

accelerate-large-models.md

@@ -7,8 +7,6 @@ authors:
 
 # How 🤗 Accelerate runs very large models thanks to PyTorch
 
-<!-- {blog_metadata} -->
-<!-- {authors} -->
 
 ## Load and run large models
 

accelerate-library.md

@@ -7,8 +7,6 @@ authors:
 
 # Introducing 🤗 Accelerate
 
-<!-- {blog_metadata} -->
-<!-- {authors} -->
 
 ## 🤗 Accelerate
 

accelerate-transformers-with-inferentia2.md

@@ -8,8 +8,6 @@ authors:
 
 # Accelerating Hugging Face Transformers with AWS Inferentia2
 
-<!-- {blog_metadata} -->
-<!-- {authors} -->
 
 <script async defer src="https://unpkg.com/medium-zoom-element@0/dist/medium-zoom-element.min.js"></script>
 

accelerated-inference.md

@@ -3,9 +3,8 @@ title: "How we sped up transformer inference 100x for 🤗 API customers"
 thumbnail: /blog/assets/09_accelerated_inference/thumbnail.png
 ---
 
-<h1>How we sped up transformer inference 100x for 🤗 API customers</h1>
+# How we sped up transformer inference 100x for 🤗 API customers
 
-<!-- {blog_metadata} -->
 
 🤗 Transformers has become the default library for data scientists all around the world to explore state of the art NLP models and build new NLP features. With over 5,000 pre-trained and fine-tuned models available, in over 250 languages, it is a rich playground, easily accessible whichever framework you are working in.