强大的LLM模型，它提供适当的API调用，经过在多个大型机器学习中心数据集上的训练

# Gorilla: Large Language Model Connected with Massive APIs [[Project Website](https://shishirpatil.github.io/gorilla/)] <img src="https://github.com/ShishirPatil/gorilla/blob/gh-pages/assets/img/logo.png" width=50% height=50%> **:fire: Gorilla OpenFunctions** is a drop-in alternative for function calling! [Release Blog](https://gorilla.cs.berkeley.edu/blogs/4_open_functions.html) **🟢 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** [](https://colab.research.google.com/drive/1DEBPsccVLF_aUnmD0FwPeHFrtdC0QIUP?usp=sharing) :computer: Use [Gorilla in your CLI](https://github.com/gorilla-llm/gorilla-cli) with `pip install gorilla-cli` **:newspaper_roll: Checkout our paper!** [](https://arxiv.org/abs/2305.15334) **:wave: Join our Discord!** [](https://discord.gg/SwTyuTAxX3) `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 - :fire: [11/16] Excited to release [Gorilla OpenFunctions](https://gorilla.cs.berkeley.edu/blogs/4_open_functions.html) - 💻 [06/29] Released [gorilla-cli](https://github.com/gorilla-llm/gorilla-cli), LLMs for your CLI! - 🟢 [06/06] Released Commercially usable, Apache 2.0 licensed Gorilla models - :rocket: [05/30] Provided the [CLI interface](inference/README.md) to chat with Gorilla! - :rocket: [05/28] Released Torch Hub and TensorFlow Hub Models! - :rocket: [05/27] Released the first Gorilla model! [](https://colab.research.google.com/drive/1DEBPsccVLF_aUnmD0FwPeHFrtdC0QIUP?usp=sharing) or [:hugs:](https://huggingface.co/gorilla-llm/gorilla-7b-hf-delta-v0)! - :fire: [05/27] We released the APIZoo contribution guide for community API contributions! - :fire: [05/25] We release the APIBench dataset and the evaluation code of Gorilla! ## Gorilla Gradio **Try Gorilla LLM models in [HF Spaces](https://huggingface.co/spaces/gorilla-llm/gorilla-demo/) or [](https://colab.research.google.com/drive/1ktnVWPJOgqTC9hLW8lJPVZszuIddMy7y?usp=sharing)**  ## Get Started Inference: Run Gorilla locally [`inference/README.md`](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](https://github.com/ShishirPatil/gorilla/tree/main/eval). ## Repository Organization Our repository organization is shown below. - The `data` folder contains all the evaluation APIs `(APIBench)` and the community contributed APIs. - The `eval` folder contains all our evaluation code as well as the Gorilla outputs. - The `inference` folder contains all the inference code for running Gorilla locally. - <span style="color:hr">[Coming Soon!]</span> The `train` folder contains all the training code associated with Gorilla finetuning. 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](https://github.com/ShishirPatil/gorilla/tree/main/eval) 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 ├── 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 ├── train (Coming Soon!) ``` ## 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](https://github.com/ShishirPatil/gorilla/tree/main/data/README.md). THIS WILL ALWAYS REMAIN OPEN SOURCE. ## 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. 2. 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