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Gorilla: Large Language Model Connected with Massive APIs [Project Website]

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🚒 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? :dart: Releasing the Berkeley Function Calling Leaderboard. Read more in our Release Blog.

:trophy: Gorilla OpenFunctions v2 Sets a new SoTA for open-source LLMs :muscle: On-par with GPT-4 :raised_hands: Supports more languages :ok_hand: Blog.

:fire: Gorilla OpenFunctions is a drop-in alternative for function calling! Release Blog

🟢 Gorilla is Apache 2.0 With Gorilla being fine-tuned on MPT and Falcon, you can use Gorilla commercially with no obligations! :golf:

:rocket: Try Gorilla in 60s Colab

:computer: Use Gorilla in your CLI with pip install gorilla-cli

:fax: Check out our blogs for all things tools-use/function-calling!

:newspaper_roll: Check out our paper! arXiv

:wave: Join our Discord! Discord

Gorilla enables LLMs to use tools by invoking APIs. Given a natural language query, Gorilla comes up with the semantically- and syntactically- correct API to invoke. With Gorilla, we are the first to demonstrate how to use LLMs to invoke 1,600+ (and growing) API calls accurately while reducing hallucination. We also release APIBench, the largest collection of APIs, curated and easy to be trained on! Join us, as we try to expand the largest API store and teach LLMs how to write them! Hop on our Discord, or open a PR, or email us if you would like to have your API incorporated as well.

News

Gorilla Gradio

Try Gorilla LLM models in HF Spaces or Gradio Colab gorilla_webUI_2

Get Started

Inference: Run Gorilla locally inference/README.md

Evaluation: We have included prompts and responses for the APIBench with and without retrievers along with the Abstract Syntax Tree (AST) matching evaluation script at evaluation.

Repository Organization

Our repository organization is shown below.

For our dataset collections, all the 1640 API documentation is in data/api. We also include the APIBench dataset created by self-instruct in data/apibench. For evaluation, we convert this into a LLM-friendly chat format, and the questions are in eval/eval-data/questions, and the corresponding responses are in eval/eval-data/responses. We have also included the evaluation scripts are in eval/eval-scripts. This would be entirely sufficient to train Gorilla yourself, and reproduce our results. Please see evaluation for the details on how to use our evaluation pipeline.

Additionally, we have released all the model weights. gorilla-7b-hf-v0 lets you invoke over 925 Hugging Face APIs. Similarly, gorilla-7b-tf-v0 and gorilla-7b-th-v0 have 626 (exhaustive) Tensorflow v2, and 94 (exhaustive) Torch Hub APIs. gorilla-mpt-7b-hf-v0 and gorilla-falcon-7b-hf-v0 are Apache 2.0 licensed models (commercially usable) fine-tuned on MPT-7B and Falcon-7B respectively. We will release a model with all three combined with generic chat capability and community contributed APIs as soon as we can scale our serving infrastructure. You can run Gorilla locally from instructions in the inference/ sub-directory, or we also provide a hosted Gorilla chat completion API (see Colab)! If you have any suggestions or if you run into any issues, please feel free to reach out to us either through Discord or email or raise a Github issue.

gorilla
|-- berkeley-function-call-leaderboard (data and scripts to eval model's function-calling ability)
├── data
│   ├── api (TF/HF/TH APIs used in generating apibench)
│   │   ├── {api_name}_api.jsonl
│   ├── apibench (Evaluating LLM models) v-1.0
│   │   ├── {api_name}_train.jsonl, {api_name}_eval.jsonl
|   |── apizoo (Contributed by the community - evolving)
│   |   ├── username1.json
│   │   ├── username2.json
│   │   ├── ...
├── eval
│   ├── README.md
│   ├── get_llm_responses.py
│   ├── eval-scripts
│   │   ├── ast_eval_{api_name}.py
│   ├── eval-data
│   │   ├── questions
│   │   │   ├── API name
│   │   │   │   ├── questions_{api_name}_{eval_metric}.jsonl
│   │   ├── responses
│   │   │   ├── API name
│   │   │   │   ├── responses_{api_name}_Gorilla_FT_{eval_metric}.jsonl
│   │   │   │   ├── responses_{api_name}_Gorilla_RT_{eval_metric}.jsonl
├── inference
│   ├── README.md
│   ├── serve
│   │   ├── gorilla_cli.py
│   │   ├── conv_template.py
├── openfunctions
|   ├── openfunctions-v1 (data and scripts for openfunctions-v0 and v1)
|   ├── utils (parsing script for openfunctions-v2)
|   ├── inference_* (openfunctions-v2 hosted/local inference code)

Contributing Your API

We aim to build an open-source, one-stop-shop for all APIs, LLMs can interact with! Any suggestions and contributions are welcome! Please see the details on how to contribute. THIS WILL ALWAYS REMAIN OPEN SOURCE.

FAQ(s)

  1. I would like to use Gorilla commercially. Is there going to be an Apache 2.0 licensed version?

Yes! We now have models that you can use commercially without any obligations.

  1. Can we use Gorilla with other tools like Langchain etc?

Absolutely! You've highlighted a great aspect of our tools. Gorilla is an end-to-end model, specifically tailored to serve correct API calls (tools) without requiring any additional coding. It's designed to work as part of a wider ecosystem and can be flexibly integrated within agentic frameworks and other tools.

Langchain, is a versatile developer tool. Its "agents" can efficiently swap in any LLM, Gorilla included, making it a highly adaptable solution for various needs.

The beauty of these tools truly shines when they collaborate, complementing each other's strengths and capabilities to create an even more powerful and comprehensive solution. This is where your contribution can make a difference. We enthusiastically welcome any inputs to further refine and enhance these tools.

Check out our blog on How to Use Gorilla: A Step-by-Step Walkthrough to see all the different ways you can integrate Gorilla in your projects.

Project Roadmap

In the immediate future, we plan to release the following:

Propose a new task you would like to work on :star_struck:

Citation

If you use Gorilla or APIBench, please cite our paper:

@article{patil2023gorilla,
  title={Gorilla: Large Language Model Connected with Massive APIs},
  author={Shishir G. Patil and Tianjun Zhang and Xin Wang and Joseph E. Gonzalez},
  year={2023},
  journal={arXiv preprint arXiv:2305.15334},
}