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

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-

FAQ(s)

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

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

1. Can we use Gorilla with Langchain, Toolformer, AutoGPT 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 without requiring any additional coding. It’s designed to work as part of a wider ecosystem and can be flexibly integrated with 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.

AutoGPT, on the other hand, concentrates on the art of prompting GPT series models. It’s worth noting that Gorilla, as a fully fine-tuned model, consistently shows remarkable accuracy, and lowers hallucination, outperforming GPT-4 in making specific API calls.

Now, when it comes to ToolFormer, Toolformer zeroes in on a select set of tools, providing specialized functionalities. Gorilla, in contrast, has the capacity to manage thousands of API calls, offering a broader coverage over a more extensive range of tools.

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.

1. How to train your own Gorilla models?

We will release the training code as soon as we can get GPUs to test and finalize the pipeline. Given the demand for our hosted end-points, we have dedicated all of our GPUs to serve the models. If you would like to help with resources get in touch!

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},
}