Meta Llama Hackathon

Applications Open for the Largest Meta Llama Hackathon in Asia Pacific

Want to leave your mark on AI and generative tech? The largest Meta Llama Hackathon in Asia Pacific gives you an incredible opportunity to flex your technical muscle among the best of innovators, developers, and tech enthusiasts in the region, vying for a prize pool of up to $100,000.

Table of Content:

Overview:

Recent breakthroughs in AI, especially generative AI, unleash new dimensions of economic and social opportunity. Such technological advancements help people, creators, and organizations express their ideas in unprecedented ways and reach an audience across the globe.

Building further on the legacy of open-source contributions, in July 2023, Meta announced Meta Llama 2, and in April 2024, it announced the next generation called Meta Llama 3. These models come up with state-of-the-art performance and second-to-none reasoning, hence finding great value for developers worldwide.

Since its release, Llama 2 and Llama 3 have received more than 180 million download requests. We are committed to continuing to support the use of these models to create economic and social impact. If you are using Llama or plan to build on top of it, we want to hear back!

Important Dates:

Proposal Submission: August 01, 2024
Semi-finalists Informed: August 16, 2024
Pitching Event in Islamabad: August 20, 2024
Regional Hackathon Finals in Singapore: October 02, 2024

Selection Process:

  1. Submission of the proposal
  2. Shortlisted participants will be notified and invited for an in-person pitch in Islamabad
  3. Winner will travel to the Regional Hackathon Finals in Singapore; Meta will fully cover travel and accommodation expenses.
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Eligibility Criteria:

  • Individuals/teams directly building on top of Llama or other Large Language Models but willing to pivot to Llama.
  • Existing products already using Large Language Models to drive a social and/or economic impact.
  • Technical resources and expertise to implement the proposed solution on Llama.
  • Affiliation with a valid business, non-profit organization, or academic/research institute. Note that this only applies to staff members, not students.
  • Organizations must be operational for at least 1 year.
  • Persons affiliated with any government entity are ineligible.

Judging Criteria:

  1. Technical Feasibility: Clearly show how and the feasibility of using Llama in the proposed solution.
  2. Impact: Describe the vision for economic/social benefit and impact on the broader problem space.
  3. Responsibility: Information security, privacy, and responsible development practices

Judges:

  • Muhammad Rashid Mukhtar: Industry-academia bridge builder and TEDx event leader
  • Dr. Muhammad Usman: Expert in Generative AI with 15+ years of experience in academia and industry
  • Zeeshan Qedwaee: Business professional driving growth through strategic technology adoption
  • Ammar Jaffri: Leader of national and international digital initiatives
  • Barira Hanif: Specialist in Sustainable Innovation and Corporate Finance
  • Dr. Adnan-ul-Hasan: Expert in deep learning-based text recognition algorithms

Partner:

NICAT

Minister of IT and Telecommunication

Ignite

Apply Now:

Deadline: August 01, 2024, 11:59 PM (PKT)
Make sure you have read and understood the eligibility criteria before applying.

Apply by clicking Here

Do not miss the opportunity to share your innovative solution with the globe and compete globally! Finalize your proposal now and be a part of these life-changing Meta Llama Hackathon.

FAQs:

Eligibility

How to apply

Applications are open till August 01, 2024. Finalists will be invited to present in Islamabad. The winner progresses to the finals in Singapore, where expenses are covered by Meta.

Language

Applications must be in English.

Data and model weights sharing

There is no requirement to share any trained model weights or data.

Additional supporting documents

Provide links to external resources explaining in detail how this was done through the Llama or other Large Language Models.

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