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112 lines
4.3 KiB
Markdown
112 lines
4.3 KiB
Markdown
# text-generation-webui
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A gradio webui for running large language models locally. Supports gpt-j-6B, gpt-neox-20b, opt, galactica, and many others.
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Its goal is to become the [AUTOMATIC1111/stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui) of text generation.
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## Features
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* Switch between different models using a dropdown menu.
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* Generate nice HTML output for GPT-4chan.
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* Generate Markdown output for [GALACTICA](https://github.com/paperswithcode/galai), including LaTeX support.
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* Notebook mode that resembles OpenAI's playground.
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* Chat mode for conversation and role playing.
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* Load 13b/20b models in 8-bit mode.
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* Load parameter presets from text files.
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* CPU mode.
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## Installation
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Create a conda environment:
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conda create -n textgen
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conda activate textgen
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Install the appropriate pytorch for your GPU. For NVIDIA GPUs, this should work:
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conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia
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Install the requirements:
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pip install -r requirements.txt
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## Downloading models
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Models should be placed under `models/model-name`. For instance, `models/gpt-j-6B` for [gpt-j-6B](https://huggingface.co/EleutherAI/gpt-j-6B/tree/main).
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#### Hugging Face
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Hugging Face is the main place to download models. These are some of my favorite:
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* [gpt-j-6B](https://huggingface.co/EleutherAI/gpt-j-6B/tree/main)
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* [gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b/tree/main)
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* [OPT](https://huggingface.co/models?search=facebook/opt)
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* [GALACTICA](https://huggingface.co/models?search=facebook/galactica)
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* [\*-Erebus](https://huggingface.co/models?search=erebus)
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The files that you need to download are the json, txt, and pytorch\*.bin files. The remaining files are not necessary.
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For your convenience, you can automatically download a model from HF using the script `download-model.py`. Its usage is very simple:
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python download-model.py organization/model
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For instance:
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python download-model.py facebook/opt-1.3b
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#### GPT-4chan
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[GPT-4chan](https://huggingface.co/ykilcher/gpt-4chan) has been shut down from Hugging Face, so you need to download it elsewhere. You have two options:
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* Torrent: [16-bit](https://archive.org/details/gpt4chan_model_float16) / [32-bit](https://archive.org/details/gpt4chan_model)
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* Direct download: [16-bit](https://theswissbay.ch/pdf/_notpdf_/gpt4chan_model_float16/) / [32-bit](https://theswissbay.ch/pdf/_notpdf_/gpt4chan_model/)
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You also need to put GPT-J-6B's config.json file in the same folder: [config.json](https://huggingface.co/EleutherAI/gpt-j-6B/raw/main/config.json)
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#### Converting to pytorch (optional)
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The script `convert-to-torch.py` allows you to convert models to .pt format, which is about 10x faster to load:
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python convert-to-torch.py models/model-name
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The output model will be saved to `torch-dumps/model-name.pt`. When you load a new model, the webui first looks for this .pt file; if it is not found, it loads the model as usual from `models/model-name`.
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## Starting the webui
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conda activate textgen
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python server.py
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Then browse to
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`http://localhost:7860/?__theme=dark`
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Optionally, you can use the following command-line flags:
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```
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-h, --help show this help message and exit
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--model MODEL Name of the model to load by default.
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--notebook Launch the webui in notebook mode, where the output is written
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to the same text box as the input.
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--chat Launch the webui in chat mode.
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--cpu Use the CPU to generate text.
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--listen Makes the webui reachable from your local network.
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```
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## Presets
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Inference settings presets can be created under `presets/` as text files. These files are detected automatically at startup.
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## System requirements
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Check the [wiki](https://github.com/oobabooga/text-generation-webui/wiki/System-requirements) for some examples of VRAM and RAM usage in both GPU and CPU mode.
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## Contributing
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Pull requests, suggestions and issue reports are welcome.
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## Other projects
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Make sure to also check out the great work by [KoboldAI](https://github.com/KoboldAI/KoboldAI-Client). I have borrowed some of the presets listed on their [wiki](https://github.com/KoboldAI/KoboldAI-Client/wiki/Settings-Presets) after performing a k-means clustering analysis to select the most relevant subsample.
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