Continued Pretraining and Fine-Tuning with Unsloth
Continued pretraining, alongside Supervised Fine Tuning (SFT), is gaining in popularity alongside Small Language Models (SLMs) in the industry. Finding faster ways to fine-tune that maintain high accuracy and minimize hallucinations is a priority for AI Engineering teams everywhere.
One such method for faster fine-tuning is to use Unsloth, which is all about faster LLM training.
The claims we’ll test during the event from Unsloth for their free, open-source version are:
2x faster training
50% less memory usage
Additionally, continued pretraining (”AKA continued Finetuning” was released in June by the Unsloth team.)
Similar claims:
2x faster
50% less VRAM than Hugging Face + Flash Attention 2 QLoRA
The team also provided the following “insights” for continued pretraining:
You should finetune the input and output embeddings.
Unsloth offloads embeddings to disk to save VRAM.
Use different learning rates for the embeddings to stabilize training.
Use Rank stabilized LoRA.
During this event, we’ll leverage fine-tuning, to fully test out the concepts and code of Unsloth.
We’ll dig under the hood to find out what tricks they’re using to speed up the training and reduce memory usage, and we’ll review what we know about some of the more advanced techniques they’re using in their enterprise versions! This should give us some great insights into the tips and tricks that are being used today to speed things up!
We will leverage Unsloth through Google Colab directly so that we can use free GPUs during the event!
📚 You’ll learn:
How to leverage Unsloth for faster continued pretraining and supervised fine-tuning
What Unsloth is doing under the hood to speed up LLM training
To fit tools like Unsloth, which accelerate training and tuning, into your toolbelt
🤓 Who should attend the event:
Aspiring AI Engineers who want to understand the latest LLM training and fine-tuning tools
AI Engineering leaders interested in continued pretraining or fine-tuning of LLMs or SLMs
Speakers:
Dr. Greg Loughnane is the Co-Founder & CEO of AI Makerspace, where he is an instructor for their AI Engineering Bootcamp. Since 2021 he has built and led industry-leading Machine Learning education programs. Previously, he worked as an AI product manager, a university professor teaching AI, an AI consultant and startup advisor, and an ML researcher. He loves trail running and is based in Dayton, Ohio.
Chris “The Wiz” Alexiuk is the Co-Founder & CTO at AI Makerspace, where he is an instructor for their AI Engineering Bootcamp. During the day, he is also a Developer Advocate at NVIDIA. Previously, he was a Founding Machine Learning Engineer, Data Scientist, and ML curriculum developer and instructor. He’s a YouTube content creator YouTube who’s motto is “Build, build, build!” He loves Dungeons & Dragons and is based in Toronto, Canada.
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