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feat: update for 2024 #12
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@@ -129,4 +129,8 @@ dmypy.json | |
.pyre/ | ||
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.DS_Store | ||
.vscode/ | ||
.vscode/ | ||
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vendor/ | ||
_site/ | ||
.bundle |
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--- | ||
title: Frequently Asked Questions | ||
menu_title: FAQ | ||
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## Can I participate in multiple projects? | ||
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Yes! You can participate in as many projects as you like. However, we recommend that you focus on one project at a time to ensure that you can make a meaningful contribution. | ||
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## Do I need to use LLMs in my project? | ||
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Yes. The goal of the hackathon is to explore the capabilities of LLMs. However, you are free to use any LLM you like, and we interpret the term "LLM" broadly. For example, you could also use state-space models like MAMBA. | ||
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## How do I submit my project? | ||
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You will submit your project by | ||
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1. Posting a video on social media (Twitter, LinkedIn, or YouTube) with the hashtag #LLMMatHack. | ||
2. You will post a link to your post in the Slack channel #project-submissions. | ||
3. You will include a link to a Google Docs with a brief description of your project and a link to your code/data as well as a listing of all team members (with affiliations, emails, and ORCIDs). | ||
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## Hackathon paper | ||
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We [again](https://pubs.rsc.org/en/content/articlelanding/2023/dd/d3dd00113j) aim to publish a paper about the hackathon, and we invite all participants to contribute. | ||
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To be included as an author, you must meet the following guidelines: | ||
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- You must have _actively_ participated in the hackathon. This must be evident from commit history, public discussions, or other forms of engagement. | ||
- Your submission must be reusable. That is, it must be possible for others to build upon your work. For coding projects, this means: | ||
- Your code must be well-documented (e.g., with docstrings, comments, and a README file). | ||
- Your code must be available on a public repository (e.g., GitHub). | ||
- Your code must be licensed under an open-source license (e.g., MIT, BSD, or Apache). | ||
- Your dependencies must be clearly listed (e.g., in a `requirements.txt` file). | ||
- Your code must include a narrated example/tutorial (e.g., in a Jupyter notebook). | ||
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For benchmark submissions, this means: | ||
- Your dataset must be well-documented (e.g., with a README file). | ||
- Your dataset must be available on a public repository (e.g., Zenodo). | ||
- Your dataset must be licensed under an open-source license (e.g., CC-BY). | ||
- Your dataset must be accompanied by a data dictionary, schema, or similar. | ||
- Your dataset must be accompanied by a narrated discussion on the scope and limitations of the dataset. | ||
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If you are not sure how to meet these requirements, please ask for help. |
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How about just #LLMHack
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Also, we should note the video should be 2 minutes or less.