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Fill summary of what was done
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alex-snd committed Oct 3, 2022
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7 changes: 0 additions & 7 deletions docs/objective/related_work.md

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title: "️✔️Summary"
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# ✔️Summary

<h3 align="justify">
Nowadays, Big Tech corporations are training state of the art multi-billion parameter models on thousands of GPUs,
spending millions of dollars on it, which the ordinary researcher cannot afford. 😢
<br><br>
What I want to show with this project is that even for a non-profit research task, you can find like-minded
volunteers and train a fairly large machine learning model.
</h3>

## Work accomplished
- <div align="justify"><font size="4">Designed and trained 50M parameters Transformer model to “read” meaningful text in columns that can be compiled for a
<a href="https://en.wikipedia.org/wiki/Running_key_cipher">Running Key Cipher</a>, widely known in field of Cryptography</font></div>
- <div align="justify"><font size="4">Implemented the code base and prepared the infrastructure for conveniently conducting an experiment on
distributed model training across the Internet with the help of volunteers</font></div>
- <div align="justify"><font size="4">Implemented beam search for inference and CLI interface, dashboard and API with a
celery worker under the hood for convenient interaction, and packed it all into docker to make it more reproducible</font></div>
- <div align="justify"><font size="4">Configured CI/CD pipelines to publish python package to PyPi, build docker images
and push them to Docker Hub and GitHub Packages, sync repository with HuggingFace Sphere and update project website</font></div>
2 changes: 1 addition & 1 deletion mkdocs.yml
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- Train: getting_started/train.md
- Objective:
- Task definition: objective/task_definition.md
- Related work: objective/related_work.md
- Summary: objective/summary.md
- Demo: demo.md
- Reference:
- CLI:
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