Recently Meta announce the availability of its Llama 2 pretrained models, trained on 2 trillion tokens, and have double the context length than Llama 1. Its fine-tuned models have been trained on over 1 million human annotations. If you are interested in learning how to fine tune Meta’s Llama 2 open source large language model to run on a single GPU. You’ll be pleased to know that the Deep Learning AI YouTube channel has created a 60 minute tutorial providing more insight into how this can be accomplished and is presented by Piero Molino and Travis Addair.
Fine-tuning large language models (LLMs) like Meta’s Llama 2 to run on a single GPU can be a daunting task. However, a recent tutorial by the Deep Learning AI YouTube channel, presented by Piero Molino and Travis Addair, offers valuable insights into this process. This 60-minute tutorial is a treasure trove of information for machine learning engineers looking to harness the power of LLMs for their projects.
How to fine tune Llama 2
One of the first hurdles that engineers often face when fine-tuning an LLM is the “host out of memory” error. This issue becomes even more challenging when dealing with the 7B parameter Llama-2 model, which demands a higher memory capacity. However, Molino and Addair, both from the open-source Ludwig project, provide practical solutions to this problem.
Other articles you may find of interest on the subject of Meta’s Llama 2 :
The in the video above the presenters explains that an optimized LLM training framework, such as Ludwig.ai, can significantly reduce the host memory overhead. This reduction is achievable even when training on multiple GPUs, making the process more efficient and manageable.
The tutorial is not just a theoretical discussion; it is a hands-on workshop that delves into the unique challenges of fine-tuning LLMs. It provides a demonstration of how these challenges can be tackled using open-source tools. The topics covered in the workshop include:
- Fine-tuning LLMs like Llama-2-7b on a single GPU
- The use of techniques like parameter-efficient tuning and quantization
- Training a 7b param model on a single T4 GPU (QLoRA)
- Deploying tuned models like Llama-2 to production
- Continued training with RLHF
- Using RAG for question answering with trained LLMs
The presenters of the tutorial, Piero Molino and Travis Addair, bring a wealth of experience to the table. Molino, co-founder and CEO of Predibase, was a founding member of Uber AI Labs. He has worked on several deployed ML systems, including an NLP model for Customer Support and the Uber Eats Recommender System. He later served as a Staff Research Scientist at Stanford University, focusing on Machine Learning systems. Molino is also the author of Ludwig.ai, an open-source declarative deep learning framework with 8900 stars on GitHub.
Travis Addair, co-founder and CTO of Predibase, has made significant contributions to the field of AI. He serves as the lead maintainer for the Horovod distributed deep learning framework within the Linux Foundation and is a co-maintainer of the Ludwig declarative deep learning framework. Previously, he led Uber’s deep learning training team as part of the Michelangelo machine learning platform.
This tutorial is a comprehensive guide for ML engineers looking to unlock the capabilities of LLMs like Llama 2. It provides practical solutions to common challenges and offers a roadmap for successfully deploying these models in production. The expertise of Molino and Addair, combined with their hands-on approach, makes this tutorial an invaluable resource for anyone interested in the field of AI and machine learning. Other articles you may find of interest on the subject of The large language model Llama 2.
Latest Geeky Gadgets Deals
Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.