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| """ | |
| This specific file was bodged together by ham-handed hedgehogs. If something looks wrong, it's because it is. | |
| If you're not a hedgehog, you shouldn't reuse this code. Use this instead: https://docs.streamlit.io/library/get-started | |
| """ | |
| import streamlit as st | |
| from st_helpers import make_header, content_text, content_title, cite, make_footer, make_tabs | |
| from charts import draw_current_progress | |
| st.set_page_config(page_title="Training Transformers Together", layout="centered") | |
| st.markdown("## Full demo content will be posted here on December 7th!") | |
| make_header() | |
| content_text(f""" | |
| There was a time when you could comfortably train SoTA vision and language models at home on your workstation. | |
| The first ConvNet to beat ImageNet took in 5-6 days on two gamer-grade GPUs{cite("alexnet")}. Today's top-1 imagenet model | |
| took 20,000 TPU-v3 days{cite("coatnet")}. And things are even worse in the NLP world: training GPT-3 on a top-tier server | |
| with 8 A100 would still take decades{cite("gpt-3")}.""") | |
| content_text(f""" | |
| So, can individual researchers and small labs still train state-of-the-art? Yes we can! | |
| 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) | |
| 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 target="_blank" rel="noopener noreferrer" href=https://laion.ai/laion-400-open-dataset/>LAION</a> - the world's largest openly available | |
| image-text-pair dataset with 400 million samples. Our model is based on | |
| <a target="_blank" rel="noopener noreferrer" href=https://github.com/lucidrains/DALLE-pytorch>dalle-pytorch</a> | |
| with several tweaks for memory-efficient training.""") | |
| content_title("How do I join?") | |
| content_text(""" | |
| That's easy. First, make sure you're logged in at Hugging Face. If you don't have an account, create one <b>TODO</b>.<br> | |
| <ul style="text-align: left; list-style-position: inside; margin-top: 12px; margin-left: -32px;"> | |
| <li style="margin-top: 4px;"> | |
| Join our organization on Hugging Face here: <b>TODO</b>. </li> | |
| <li style="margin-top: 4px;"> | |
| The simplest way to start is with colab <b>TODO</b>;</li> | |
| <li style="margin-top: 4px;"> | |
| You can find other starter kits, evaluation and inference notebooks <b>TODO IN OUR ORGANIZATION</b>;</li> | |
| <li style="margin-top: 4px;"> | |
| If you have any issues, <b>TODO DISCORD BADGE</b> </li> | |
| </ul> | |
| Please note that we currently limit the number of colab participants to <b>TODO</b> to make sure we do not interfere | |
| with other users. If there are too many active peers, take a look at alternative starter kits here <b>TODO</b> | |
| """) | |
| content_title("But how does it work?") | |
| content_text("<b> TODO </b> General Story That Weaves Together Three Tabs Below . Lorem ipsum dolor sit amet, " | |
| "consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim" | |
| " ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. " | |
| "Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. " | |
| "Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.") | |
| make_tabs() | |
| content_text("<b> TODO UPDATE") | |
| make_footer() | |