DIY AI: Finetuning Open-Source LLMs Like a Pro!
Session Description
Abstract:
Open-source small language models (SLMs) like DeepSeek, Phi-3, and Mistral are game-changers in AI accessibility. But how do you get them to work specifically for your use case? Whether you're building a chatbot, a coding assistant, or a niche domain expert, finetuning these models can make a world of difference.
In this session, we’ll demystify the process of finetuning SLMs on consumer hardware and show how to optimize them for real-world applications.
We’ll discuss:
- Choosing the Right Model – Why smaller, open-source LLMs might be better than GPT-4 for your task.
- Data Curation & Preprocessing – Cleaning and structuring your dataset for better results.
- Fine-tuning Techniques – LoRA, QLoRA, and full fine-tuning explained in simple terms.
- Training on a Budget – Running your own fine-tuning experiments without a GPU cluster.
- Evaluating & Deploying – Making sure your model is accurate, efficient, and doesn’t hallucinate nonsense.
This talk is designed for AI enthusiasts, developers, and researchers who want to take control of open-source AI models without burning a hole in their cloud credits. Expect a fast-paced, engaging session with a live demo, practical insights, and a few AI-generated surprises!
Why This Talk?
- The open-source AI community is booming, and small models are the future.
- Many developers use LLMs, but few know how to fine-tune them properly.
- Bangalore’s tech community can benefit from an accessible, practical approach to AI customization.
Takeaways:
By the end of this talk, attendees will know:
✅ How to pick the right open-source model for their task
✅ The simplest ways to fine-tune a model with minimal compute
✅ How to evaluate and deploy their custom-tuned models effectively
Target Audience: College students and working professionals interested in AI, open-source, and machine learning.