Gorilla: Large Language Model Connected with APIs

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🦍 Gorilla: Large Language Model Connected with Massive APIs

Shishir G. Patil*, Tianjun Zhang*, Xin Wang, Joseph E. Gonzalez

UC Berkeley

sgp@berkeley.edu, tianjunz@berkeley.edu

Blogs GitHub HuggingFace Discord

Gorilla Paper RAFT Paper GoEX Paper

Systems and Algorithms for Integrating LLMs with Applications, Tools, and Services

Gorilla Used at

Gorilla OpenFunctions

Teach LLM tool use

🎁In OpenFunctions-v2, we natively train the model to support parallel functions (generate multiple functions at a time) and multiple functions (select one or more functions). Java/REST/Python APIs are also supported for the first time with extended data types📷

Read More: Blog

How well to other function-calling models perform: Berkeley Function Calling Leaderboard

Play with the model online: Gorilla OpenFunctions-v2 Web Demo

Check out the project: GitHub Code

Model (6.91B) on HuggingFace 🤗: gorilla-llm/gorilla-openfunctions-v2

BFCL

Benchmarking LLMs on function calling capabilities

🏆 Berkeley Function-Calling Leaderboard (BFCL) 📊 aims to provide a thorough study of the function-calling capability of different LLMs. It consists of 2k 📝 question-function-answer pairs with multiple languages (🐍 Python, ☕ Java, 🟨 JavaScript, 🌐 REST API), diverse application domains, and complex use cases (multiple and parallel function calls). We also investigate function relevance detection 🕵️‍♂️, to determine how the model will react when the provided function is not suitable to answer the user's question.

Read More: Blog

Live Leaderboard: Website

BFCL Evaluation Dataset: HuggingFace Dataset 🤗

Gradio Demo: HuggingFace Space 🤗

Reproducibility: Github Code

RAFT

Better way to do RAG

RAFT: Retriever-Aware FineTuning for domain-specific RAG 🚀 Drawing parallels between LLMs and students in open-book (RAG) 📔 and closed-book exams (SFT) 🧠, we present a better recipe for fine-tuning a base LLM for RAG-focused challenges. Discover how RAFT prepares LLMs to excel with a specific document set, mirroring students' prep for finals! 🎓

Read More: Blog

Paper: https://arxiv.org/abs/2404.06921

Try it out: Web Demo

Reproducibility: GitHub Code

News

🚒 GoEx: A Runtime for executing LLM generated actions like code & API calls GoEx presents “undo” and “damage confinement” abstractions for mitigating the risk of unintended actions taken in LLM-powered systems. Release blog, Paper.

🎉 Berkeley Function Calling Leaderboard! How do models stack up for function calling? 🎯 Read more in our Release Blog.

🏆 Gorilla OpenFunctions v2 Sets new SoTA for open-source LLMs 💪 On-par with GPT-4 🙌 Supports more languages 👌

🥇 Gorilla OpenFunctions! Drop in replacement! Examples

🚀 Try Gorilla in 60s! No sign-ups, no installs, just colab!

🤩 With Apache 2.0 licensed LLM models, you can use Gorilla commercially without any obligations!

📣 We are excited to hear your feedback and we welcome API contributions as we build this open-source project. Join us on Discord or feel free to email us!

Gorilla for your CLI and Spotlight Search

Gorilla powered CLI

Get started with pip install gorilla-cli

Gorilla Powered Spotlight Search

Gorilla-Spotlight Signup

Vision

Rather have the user at the center, Gorilla enables users to interact with a wide range of services through LLMs. Gorilla is an open-source, state-of-the-art LLM that invokes API calls to interact with services!

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Citation

@article{patil2024gorilla, title={Gorilla: Large Language Model Connected with Massive APIs}, author={Patil, Shishir G. and Zhang, Tianjun and Wang, Xin and Gonzalez, Joseph E.}, booktitle = {Advances in Neural Information Processing Systems}, year={2024},

Gorilla: Large Language Model Connected with APIs

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