LoRA Land: How We Trained 25 Fine-Tuned Mistral-7b Models that Outperform GPT-4

LoRA Land is a collection of 25+ fine-tuned Mistral-7b models that outperform GPT-4 in task-specific applications and provides a blueprint for teams looking to quickly and cost-effectively deploy AI systems. Furthermore, these fine-tuned models were all trained for less than $8 each on average and are all being served with the Mistral-7b base model on a single GPU with LoRAX.

In this on-demand webinar, Staff Software Engineer Justin Zhao and ML Engineer Timothy Wang lead an in-depth discussion and demonstration covering the:
• Emergence of fine-tuned task-specific models
• Rationale for selecting Mistral-7b as a base model
• Efficient fine-tuning of 25+ models using Parameter-Efficient Fine-Tuning (PEFT) methods like Low Rank Adaptation (LoRA)
• Evaluation of the fine-tuned models vs. established benchmarks

You'll learn how we built LoRA Land as well as best practices for how you can apply best practices from our project to your own AI initiatives.

Ready to get started?
• Visit LoRA Land to prompt our 25 open-source adapters: https://predibase.com/lora-land
• Efficiently fine-tune and serve your own LLMs with $25 in free Predibase: https://predibase.com/free-trial