I wanted to share a project I’ve been working on, LLM-VM. It’s a community-first, open-source tool designed to enhance the efficiency of fine-tuning and inference for large language models (LLMs) both locally and in cloud environments.

At its core, LLM-VM implements recursive synthesized distillation with automatic task discovery. This means it can iteratively refine training data and model parameters, aiming to optimize model performance with less computational overhead.

Our goal with LLM-VM is to provide a practical and accessible platform for researchers and developers. By facilitating more efficient model training and deployment, we hope to contribute to the broader machine learning community’s efforts in advancing language model capabilities.

I’d love to get your feedback, contributions, or any thoughts you might have. Let’s collaborate to push the boundaries of what we can achieve with LLMs!

Cheers!