Author of Gorilla Execution Engine (GoEx) here! The key idea behind GoEx:
1. "Post-facto validation" for assessing LLM actions after execution.
2. "Undo" button and “Damage confinement” abstractions to manage unintended actions & risks!
GoEx (code below) inherently supports Gmail/Slack/Dropbox/Spotify/Github, try it out to build your own LLM agents!
This is a three-way collaboration between Berkeley AI, Microsoft Azure, and Meta AI!
RAFT involves the concept of domain-specific RAG, which represents a more focused and growingly favored area compared to the broader concept of the general open-book exam. In such exams, the domain in which the LLM will be evaluated is known in advance and used for inference. The LLM is capable of addressing prompts by leveraging any and all information from this particular domain, on which it has been specifically fine-tuned.
Hi HN, I'm one of the authors from Gorilla Project. Gorilla now presents in an CLI interface and you can interact with your laptop in English! Feedbacks and suggestions are very welcome!!
Hey everyone, I am one of the authors of the Gorilla project. Super excited to see how the project grows! We have released LLaMA based, MPT based (Apache 2.0) and Falcon based (Apache 2.0) models so far. Something cooler is coming soon!
GoEx (code below) inherently supports Gmail/Slack/Dropbox/Spotify/Github, try it out to build your own LLM agents!
Blog: https://gorilla.cs.berkeley.edu/blogs/10_gorilla_exec_engine... Code: https://github.com/ShishirPatil/gorilla/tree/main/goex