Format ray-rag post + do not publish for now (will pub tomorrow)

cc @amogkam

_blog.yml

@@ -77,16 +77,16 @@
   thumbnail: ./assets/10_tf-serving/thumbnail.png
   date: January 26, 2021
 
-- local: ray-rag
-  title: "Retrieval Augmented Generation with Huggingface Transformers and Ray"
-  thumbnail: ./assets/12_ray_rag/ray_arch_updated.png
-  author: amogkam
-  guest: true
-  date: February 1, 2021
-
 - local: pytorch-xla
   title: "Hugging Face on PyTorch / XLA TPUs"
   thumbnail: ./assets/13_pytorch_xla/pytorch_xla_thumbnail.png
   author: jysohn23
   guest: true
   date: February 9, 2021
+
+# - local: ray-rag
+#   title: "Retrieval Augmented Generation with Huggingface Transformers and Ray"
+#   thumbnail: ./assets/12_ray_rag/ray_arch_updated.png
+#   author: amogkam
+#   guest: true
+#   date: February 10, 2021

porting-fsmt.md

@@ -18,7 +18,7 @@ thumbnail: /blog/assets/07_porting_fsmt/thumbnail.png
         <div class="bfc">
             <code>stas</code>
             <span class="fullname">Stas Bekman</span>
-            <span class="mono-label" style="color: var(--text-main);">guest</span>
+            <span class="bg-gray-100 rounded px-1 text-gray-600 text-sm font-mono">guest</span>
         </div>
     </a>
 </div>

ray-rag.md

@@ -1,6 +1,29 @@
-## Retrieval Augmented Generation with Huggingface Transformers and Ray
+---
+title: "Retrieval Augmented Generation with Huggingface Transformers and Ray"
+thumbnail: /blog/assets/12_ray_rag/ray_arch_updated.png
+---
 
-_[Huggingface Transformers](https://huggingface.co/) recently added the [Retrieval Augmented Generation (RAG)](https://twitter.com/huggingface/status/1310597560906780680) model, a new NLP architecture that leverages external documents (like Wikipedia) to augment its knowledge and achieve state of the art results on knowledge-intensive tasks. In this blog post, we introduce the integration of [Ray](https://docs.ray.io/en/master/), a library for building scalable applications, into the RAG contextual document retrieval mechanism. This speeds up retrieval calls by 2x and improves the scalability of RAG distributed [fine-tuning](https://github.com/huggingface/transformers/tree/master/examples/research_projects/rag)._
+# Retrieval Augmented Generation with Huggingface Transformers and Ray
+
+<div class="blog-metadata">
+    <small>Published Feb 10, 2021.</small>
+    <a target="_blank" class="btn no-underline text-sm mb-5 font-sans" href="https://github.com/huggingface/blog/blob/master/ray-rag.md">
+        Update on GitHub
+    </a>
+</div>
+
+<div class="author-card">
+    <a href="/ray-project">
+        <img class="avatar avatar-user" src="https://aeiljuispo.cloudimg.io/v7/https://s3.amazonaws.com/moonup/production/uploads/1603836776822-5f989a3b777efc07d7f1e290.png?w=200&h=200&f=face" title="Gravatar">
+        <div class="bfc">
+            <code>ray-project</code>
+            <span class="fullname">Ray Project (Anyscale)</span>
+            <span class="bg-gray-100 rounded px-1 text-gray-600 text-sm font-mono">guest</span>
+        </div>
+    </a>
+</div>
+
+[Huggingface Transformers](https://huggingface.co/) recently added the [Retrieval Augmented Generation (RAG)](https://twitter.com/huggingface/status/1310597560906780680) model, a new NLP architecture that leverages external documents (like Wikipedia) to augment its knowledge and achieve state of the art results on knowledge-intensive tasks. In this blog post, we introduce the integration of [Ray](https://docs.ray.io/en/master/), a library for building scalable applications, into the RAG contextual document retrieval mechanism. This speeds up retrieval calls by 2x and improves the scalability of RAG distributed [fine-tuning](https://github.com/huggingface/transformers/tree/master/examples/research_projects/rag).
 
 
 
@@ -101,7 +124,7 @@ _A performance comparison of different retrieval implementations. For each docum
 To try it out, first install the necessary requirements
 
 
-```
+```bash
 pip install ray
 pip install transformers
 pip install -r transformers/examples/research_projects/rag/requirements.txt
@@ -111,7 +134,7 @@ pip install -r transformers/examples/research_projects/rag/requirements.txt
 Then, you can specify your data paths and other configurations and run [finetune-rag-ray.sh](https://github.com/huggingface/transformers/blob/master/examples/research_projects/rag/finetune_rag_ray.sh)!
 
 
-```
+```bash
 # Sample script to finetune RAG using Ray for distributed retrieval.
 
 # Add parent directory to python path to access lightning_base.py

ray-tune.md

@@ -20,7 +20,7 @@ thumbnail: /blog/assets/06_ray_tune/ray-hf.jpg
         <div class="bfc">
             <code>ray-project</code>
             <span class="fullname">Ray Project (Anyscale)</span>
-            <span class="mono-label" style="color: var(--text-main);">guest</span>
+            <span class="bg-gray-100 rounded px-1 text-gray-600 text-sm font-mono">guest</span>
         </div>
     </a>
 </div>

zero-deepspeed-fairscale.md

@@ -18,7 +18,7 @@ thumbnail: /blog/assets/11_zero_deepspeed_fairscale/zero-partitioning.png
         <div class="bfc">
             <code>stas</code>
             <span class="fullname">Stas Bekman</span>
-            <span class="mono-label" style="color: var(--text-main);">guest</span>
+            <span class="bg-gray-100 rounded px-1 text-gray-600 text-sm font-mono">guest</span>
         </div>
     </a>
 </div>