Image Sources: https://openai.com/, https://www.meta.com/, https://github.com/salesforce/CodeGen, https://www.google.com/, https://www.anthropic.com/, and https://huggingface.co/

Welcome to our Efficient LLM Solutions project page!

Students: Zhongzhi Yu, Chaojian Li, Yonggan Fu, Haoran You, Yongan (Luke) Zhang, and Yang (Katie) Zhao

The advent of large language models (LLMs) has catalyzed a transformative shift in our interactions with technology. The multifaceted applications of LLMs hold the promise of reshaping numerous facets of our digital experiences.

However, this promise comes with its own set of challenges. The considerable computational, memory, and energy overheads associated with LLMs, along with their substantial resource requirements, act as barriers to the full potential of LLM-driven applications.

Driven by a commitment to innovation, our objective is to craft efficient LLM solutions that bridge these gaps. Melding comprehensive research with real-world testing, we aim to streamline LLM applications, making them more efficient and widely accessible.

Central to our mission is the goal of pushing the boundaries of what LLMs can achieve. Welcome to join our ambitious journey, as we work towards a future where the capabilities of large language models are realized to their utmost potential.

Corresponding Publications: