Ep 1- Efficient LLM finetuning with Co-Founder Unsloth

Episode 1!!! 🎉

Today we chat about AI Training with (un)Supervised Learning and Daniel from Unsloth.ai

The good stuff- Unsloth

https://www.unsloth.ai

https://ko-fi.com/unsloth

https://github.com/unslothai

In this episode of Unsupervised Learning, host Renee interviews Daniel, the co-founder of Unsloth, an AI training system that fine-tunes language models 30 times faster. They discuss Daniel's beginnings at Nvidia, his passion for making AI accessible and efficient, and his ultimate vision of creating a personal ChatGPT for everyone that operates on local machines. Daniel explains the concept of Retrieval Augmented Generation (RAG) as a knowledge injection system and elaborates on the current uses and future plans for Unsloth. The episode also touches on the issues with representing maths in language models and the misconceptions people have about working with large language models.

Have something to say? feedback, love notes or recommend a mate to join the pod @ renee@unsupervisedlearning.co

00:00 Introduction to the Podcast

00:26 Understanding Unsloth: The AI Training System

00:58 Daniel's Journey from NVIDIA to Unsloth

02:15 The Power of OpenAI's Triton Language

02:38 The Magic Behind Unsloth's Fine-Tuning Process

03:42 Community Engagement and Use Cases of Unsloth

05:03 Working with Family in the AI Space

05:35 The Role of Autonomous Agents in AI Development

06:57 Challenges of Using Language Models for Math

09:03 Unsloth's Vision for Democratizing AI

09:56 Misconceptions and Best Practices in Working with LLMs

14:21 Understanding Retrieval Augmented Generation (RAG)

17:29 Staying Updated in the AI Space

18:26 Supporting Unsloth's Open Source Initiative

19:29 Conclusion: The Future of AI with Unsloth