Training and Deploying Open-Source LLMs with Dr. Jon Krohn
Description
In this episode, we speak with Dr. Jon Krohn about the life cycle of open-source LLMs. Jon is a co-founder and chief data scientist at the machine learning company Nebula. He is the author of the book Deep Learning Illustrated, which was an instant #1 bestseller and was translated into seven languages. He is also the host of the fabulous SuperDataScience podcast, the data science industry’s most listened-to podcast. An incredible instructor and speaker, Jon’s workshops at ODSC conferences and other events are always one of our most popular.
Topics:
1. Guest Introduction
2. Definition of an open source LLMs and what it means to be truly open source
3. The importance of LLM weights and neural networks architecture for training
4. Transformer architecture
5. Apple expanding their AI team
6. What do I need to train or fine-tune an LLM
7. Key libraries for fine-tunning an LLM
8. The LoRA (Low-Rank Adaptation) technique for efficiently fine-tuning large language models
9. Testing and evaluating LLMs prior to deploying in production
10. Retrieval Augmented Generation (RAG)
11. Deploying LLM to production
12. How to keep inference costs down
13. How can people follow Jon’s content (see show notes also)
Show Notes: More about Jon:
Tools and Resources:
Michael Nielsen's eBook on Neural Networks and Deep Learning PyTorch Lightning is the deep learning framework Hugging Face Transformers LibraryVicuna: An Open-Source Chatbot LoRA: Low-Rank Adaptation of Large Language ModelsSDS 674: Parameter-Efficient Fine-Tuning of LLMs using LoRA (Low-Rank Adaptation)Unsloth for finetuning Llama 3, Mistral & GemmaPhoenix: an open-source AI Observability & Evaluation toolODSC Podcast with Amber Roberts on Phoenix and troubleshooting LLMsWeights & Biases
This episode was sponsored by:
Ai+ Training https://aiplus.training/
Home to hundreds of hours of on-demand, self-paced AI training, ODSC interviews, free webinars, and certifications in in-demand skills like LLMs and Prompt Engineering
And created in partnership with ODSC https://odsc.com/
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