--- datasets: - tiiuae/falcon-refinedweb language: - en inference: false ---
TheBlokeAI

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# Gorilla 7B GPTQ This repo contains a quantised 4-bit GPTQ model for [Gorilla 7B](https://huggingface.co/gorilla-llm/gorilla-7b-hf-delta-v0). It is the result of quantising to 4bit using [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa). **NOTE**: This is not a regular LLM. It is designed to allow LLMs to use tools by invoking APIs. "Gorilla enables LLMs to use tools by invoking APIs. Given a natural language query, Gorilla can write a 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. " ## Other repositories available * [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/gorilla-7B-GPTQ) * [4-bit, 5-bit, and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/gorilla-7B-GGML) * [Merged, unquantised fp16 model in HF format](https://huggingface.co/TheBloke/gorilla-7B-fp16) ## Prompt template ``` ###USER: find me an API to generate cute cat images ###ASSISTANT: ``` ## How to easily download and use this model in text-generation-webui Open the text-generation-webui UI as normal. 1. Click the **Model tab**. 2. Under **Download custom model or LoRA**, enter `TheBloke/gorilla-7B-GPTQ`. 3. Click **Download**. 4. Wait until it says it's finished downloading. 5. Click the **Refresh** icon next to **Model** in the top left. 6. In the **Model drop-down**: choose the model you just downloaded, `gorilla-7B-GPTQ`. 7. If you see an error in the bottom right, ignore it - it's temporary. 8. Fill out the `GPTQ parameters` on the right: `Bits = 4`, `Groupsize = 128`, `model_type = Llama` 9. Click **Save settings for this model** in the top right. 10. Click **Reload the Model** in the top right. 11. Once it says it's loaded, click the **Text Generation tab** and enter a prompt! ## Provided files **Gorilla-7B-GPTQ-4bit-128g.no-act-order.safetensors** This will work with all versions of GPTQ-for-LLaMa. It has maximum compatibility. It was created with groupsize 128 to ensure higher quality inference, without `--act-order` parameter to maximise compatibility. * `Gorilla-7B-GPTQ-4bit-128g.no-act-order.safetensors` * Works with all versions of GPTQ-for-LLaMa code, both Triton and CUDA branches * Works with AutoGPTQ * Works with text-generation-webui one-click-installers * Parameters: Groupsize = 128. No act-order. * Command used to create the GPTQ: ``` python llama.py /workspace/process/gorilla-7B/HF wikitext2 --wbits 4 --true-sequential --groupsize 128 --save_safetensors /workspace/process/gorilla-7B/gptq/Gorilla-7B-GPTQ-4bit-128g.no-act-order.safetensors ``` ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/Jq4vkcDakD) ## Thanks, and how to contribute. Thanks to the [chirper.ai](https://chirper.ai) team! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Patreon special mentions**: Aemon Algiz, Dmitriy Samsonov, Nathan LeClaire, Trenton Dambrowitz, Mano Prime, David Flickinger, vamX, Nikolai Manek, senxiiz, Khalefa Al-Ahmad, Illia Dulskyi, Jonathan Leane, Talal Aujan, V. Lukas, Joseph William Delisle, Pyrater, Oscar Rangel, Lone Striker, Luke Pendergrass, Eugene Pentland, Sebastain Graf, Johann-Peter Hartman. Thank you to all my generous patrons and donaters! # Original model card: Gorilla 7B # Gorilla: Large Language Model Connected with Massive APIs By Shishir G. Patil, Tianjun Zhang, Xin Wang, and Joseph E. Gonzalez ([Project Website](https://shishirpatil.github.io/gorilla/)) [![arXiv](https://img.shields.io/badge/arXiv-2305.15334-.svg?style=flat-square)](https://arxiv.org/abs/2305.15334) [![Discord](https://img.shields.io/discord/1111172801899012102?label=Discord&logo=discord&logoColor=green&style=flat-square)](https://discord.gg/3apqwwME) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1DEBPsccVLF_aUnmD0FwPeHFrtdC0QIUP?usp=sharing) `Gorilla` enables LLMs to use tools by invoking APIs. Given a natural language query, Gorilla can write a 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. ## Model Details Gorilla can be either trained via standard finetuning or using our novel retriever-aware training pipeline. We release `gorilla-7b-hf-delta-v0`, a 0-shot finetuned LLM that can reliably use Hugging Face APIs. It can be prompted through simply natural language (e.g., "I want to generate an image from text."). Checkour our website, github and paper for more information. ### Model Type Gorilla is an open-source API caller trained by fine-tuning LLaMA weights. It is an auto-regressive language model, based on the transformer architecture. ### Model Date 05/27/2023 ### Organization Gorilla LLM (UC Berkeley) --- license: apache-2.0 ---