update meta-info

app.py

@@ -27,18 +27,15 @@ So, can individual researchers and small labs still train state-of-the-art? Yes
 All it takes is for a bunch of us to come together. In fact, we're doing it right now and <b>you're invited to join!</b>
 """, vspace_before=12)
 
-content_text("<br>")
+st.markdown("<br>", unsafe_allow_html=True)
 
 draw_current_progress()
 
 content_text(f"""
 The model we're training is called DALLE: a transformer "language model" that generates images from text description.
 We're training this model on <a href=https://laion.ai/laion-400-open-dataset/>LAION</a> - the world's largest openly available
-image-text-pair dataset with 400 million samples.
-<b>TODO</b> You see a short description of training dataset, model architecture and training configuration.
-In includes all necessary citations and, most importantly, a down-to-earth explanation of what exactly is dalle.
-It properly refers the communities that provided data, the source codebase and provides necessary links.
-""")
+image-text-pair dataset with 400 million samples. Our model is based on
+<a href=https://github.com/lucidrains/DALLE-pytorch>dalle-pytorch</a> with additional features for memory efficiency.""")
 
 content_title("How do I join?")
 

st_helpers.py

@@ -37,5 +37,5 @@ CITATIONS = {}
 
 
 def cite(tag):
-    CITATIONS[tag] = len(CITATIONS) + 1
+    CITATIONS.setdefault(tag, len(CITATIONS) + 1)
     return f" [{CITATIONS[tag]}]"

static/meta.html

@@ -1,5 +1,5 @@
 <meta http-equiv="Content-Type" content="text/html; charset=utf-8"/>
-<title>learning@home</title>
+<title>Training Transformers Together</title>
 <meta name="description" content="A NeurIPS'21 demonstration that explains how to train large models together with multiple collaborators.">
 <link rel="mask-icon" href="https://learning-at-home.github.io/logo_small.png">
 <link rel="alternate icon" class="js-site-favicon" type="image/png" href="https://learning-at-home.github.io/logo.png">
@@ -7,7 +7,7 @@
 <meta property="og:url" content="https://training-transformers-together.github.io">
 <meta property="og:site_name" content="learning@home">
 <meta property="og:title" content="Train vast neural networks together">
-<meta property="og:description" content="A library to train large neural networks across the internet. Imagine training one huge transformer on thousands of computers from universities, companies, and volunteers.">
+<meta property="og:description" content="A NeurIPS'21 demonstration that explains how to train large models together with multiple collaborators.">
 <meta property="og:image" content="https://learning-at-home.github.io/logo_small.png">
 <meta property="og:image:type" content="image/png">
 <meta property="og:image:width" content="96">
@@ -16,6 +16,6 @@
 <meta property="twitter:creator" content="Yandex, Huggingface, Hivemind team & contributors">
 <meta property="twitter:card" content="summary_large_image">
 <meta property="twitter:title" content="learning@home">
-<meta property="twitter:description" content="Hivemind is a library to train large neural networks across the internet. Imagine training one huge transformer on thousands of computers from universities, companies, and volunteers.">
+<meta property="twitter:description" content="A NeurIPS'21 demonstration that explains how to train large models together with multiple collaborators.">
 <meta property="twitter:image:src" content="https://learning-at-home.github.io/logo_horizontal.png">
 <meta name="viewport" content="width=device-width, initial-scale=1">