https://simplecast.com 🤘(un)supervised learning pod Presented by a non-tech native, (un)supervised learning lowers the barrier to entry for understanding and adoption of opensource, local and bleeding edge tech. Through clear communication, peer-to-peer knowledge sharing and an “explain it like I’m five” mentality. www.unsupervisedlearning.co Renee Shaw en Thu, 1 Feb 2024 00:17:12 +0000 Thu, 1 Feb 2024 00:17:23 +0000 https://www.unsupervisedlearning.co/podcast 🤘(un)supervised learning pod https://image.simplecastcdn.com/images/bd742950-1333-440f-826e-4d7ab3265292/91c0922c-d79d-479c-9e8e-58472e39ba47/3000x3000/17e4278d3a8b0f8eca76123f390733fc.jpg?aid=rss_feed https://www.unsupervisedlearning.co/podcast episodic Presented by a non-tech native, (un)supervised learning lowers the barrier to entry for understanding and adoption of opensource, local and bleeding edge tech. Through clear communication, peer-to-peer knowledge sharing and an “explain it like I’m five” mentality. www.unsupervisedlearning.co Renee no https://feeds.simplecast.com/Hovoy7_E Renee renee@unsupervisedlearning.co be200b24-dc1d-4481-8540-77bbd0ede6fc Episode 1- Efficient LLM training with Unsloth.ai Co-Founder 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

 

Want to support or get in touch? renee@unsupervisedlearning.co 
https://ko-fi.com/unsupervisedlearning

]]> Thu, 1 Feb 2024 00:17:12 +0000 renee@unsupervisedlearning.co (Renee Shaw, Daniel Han) https://www.unsupervisedlearning.co/podcast 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

 

Want to support or get in touch? renee@unsupervisedlearning.co 
https://ko-fi.com/unsupervisedlearning

]]> Episode 1- Efficient LLM training with Unsloth.ai Co-Founder Renee Shaw, Daniel Han 00:19:51 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. 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. open ai, artifical intelligence, local ai, llm, unsloth, opensource software, ai startup no full 1 substack:post:140792027 An intro to (un)supervised learning Hello World!



This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.unsupervisedlearning.co

Want to support or get in touch? renee@unsupervisedlearning.co 
https://ko-fi.com/unsupervisedlearning

]]> Thu, 18 Jan 2024 05:19:02 +0000 renee@unsupervisedlearning.co (Renee) https://www.unsupervisedlearning.co/podcast Hello World!



This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.unsupervisedlearning.co

Want to support or get in touch? renee@unsupervisedlearning.co 
https://ko-fi.com/unsupervisedlearning

]]> An intro to (un)supervised learning Renee 00:00:40 Hello World! This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.unsupervisedlearning.co Hello World! This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.unsupervisedlearning.co no full 0