AMD AI SPRINT: HACKATHON & WORKSHOP SERIES- Bengaluru, INDIA
Please Read before Registering:
This hackathon is not beginner-friendly. We are specifically looking for developers with experience and a proven track record in the following areas:
Proficiency in Python and expertise with ML libraries such as PyTorch, Hugging Face Transformers, or LangChain.
Prior experience (minimum 1 year) with training, fine-tuning, or prompting LLMs (e.g., Qwen, LLaMA, Mistral, or GPT-based models).
Familiarity with agentic workflows, prompt engineering, or RAG (Retrieval-Augmented Generation)pipelines.
Ability to work with Linux-based remote GPU instances or cloud environments.
IMPORTANT NOTE:
Please do not contact the event hosts for approval updates. All application approvals will be communicated only via Luma.
We are currently reviewing a high volume of applications. Thank you for your patience!
AMD is NOT responsible for sponsoring travel or accommodation. If your application is approved, it will be your responsibility to make your own travel and stay arrangements.
Please do NOT contact IISc, Bengaluru about event approval.
Why Attend?
Join India’s leading AI engineers, researchers, and enthusiasts in Mumbai for a 2-day hands-on workshops and hackathons powered by AMD’s MI300X GPUs. Whether you're a student, AI expert, or just starting your journey in machine learning, this is your chance to:
Build AI agents using models like Qwen and Llama through a browser-based workflow – no prior experience needed
Fine-tune pre-trained models with tools like torchtune and Unsloth to specialize them for real-world tasks
Work directly on AMD’s cutting-edge MI300X GPUs with guidance from AMD experts
Learn from engineers at AMD, Hugging Face, Meta & more
Win exciting prizes in the hackathons
Seats are limited – register early to secure your spot in Bengaluru!
Agenda:
Day 1:
9:30 AM – Doors Open
10:00 AM – Opening Talk by AMD on "What’s New with AMD’s Developer Initiatives?"
10:15 AM – Hackathon/Competition Introduction
10:30 AM – Workshop 1: Developing MCP Agents with AMD GPUs.
11:15 AM - 11:30 AM Break– Workshop 2: Finetuning AI models using Torchtune and other libraries.
12:15 PM – Lunch + Networking + Hackathon/ Competition Team Formation if not done already
1:00 PM – Hackathon/Competition Kickoff+ Overview on Rules, Resources & Support
6:00 PM – Dinner & Networking
Day 2
8:00 AM – Doors Open + Breakfast Networking
8:45 AM – Partner Talk: Hugging Face- {Details Coming soon}
9:00 AM – 2:00 PM – Hackathon/Competition Continue
12:00 PM – 1:00 PM – Lunch
2:00 PM - Submission Deadline
2:00 PM – 4:00 PM - Evaluation
3:00 PM - Research Talk: Prof. Prathosh AP, IISc Bangalore
4:00 PM – 5:00 PM – Optional Top Team Presentations (5 min each)
5:30 PM – 8:00 PM – Awards + Happy Hour
Workshop Details:
Building AI Agents on AMD GPUs
This hands-on workshop teaches you how to set up AI models and build intelligent agents (like chatbots or task-automation tools) using AMD MI300X GPUs. You’ll learn from AMD experts on how open-source models like Qwen and Llama through a browser-based workflow. No prior experience needed!
Fine-Tuning AI Models
This second beginner-level workshop focuses on fine-tuning pre-trained AI models to make them smarter or more specific to a task (like better in answering questions or generating images). You’ll use AMD GPUs, torchtune, and other open-source tools like Unsloth, with guidance from AMD engineers.
Hackathon / Competition Details
A total of 5 Lakh in Prize money.
Track 1: AMD AI Premier League (AAIPL)
A head-to-head AI competition where teams of up to 3 developers build two intelligent language model based agents:
Q-agent: Generates valid, challenging multiple choice questions belonging to a given domain.
A-agent: Attempts to answer questions posed by the opposing team’s Q-agent.
Goal: Create a question generator that can pose the most difficult yet correct questions while ensuring your answerer can accurately answer as many of them.
Format:
Matches are played between pairs of teams where one team's Q-agent generates a set of questions to which A-agent of the opposing team responds and vice-versa.
Winning teams advance to the next stage.
Resources Provided:
1 MI300 GPU for 24 hours
Sample code for prompt tuning, reinforcement learning, and fine-tuning
Deliverables:
Final working solution
PowerPoint summarizing techniques used
Track 2: Agentic AI Scheduling Assistant
Problem: Scheduling meetings across time zones is inefficient and requires multiple back-and-forth messages. Existing tools lack intelligent conflict resolution.
Goal: Build an AI-powered scheduling assistant using Agentic AI that automates meeting coordination by
Scanning participant calendars to find optimal meeting slots
Negotiating conflicts with other participant agents
Sending polite reschedule requests
Instantly confirming meetings or proposing alternative times
Expectation: A seamless, intelligent assistant that minimizes manual effort in scheduling.
Resources Provided:
1 MI300 GPU for 24 hours
Example application and dummy calendar events