text-generation-webui

A gradio webui for running large language models locally. Supports gpt-j-6B, gpt-neox-20b, opt, galactica, and many others.

Its goal is to become the AUTOMATIC1111/stable-diffusion-webui of text generation.

Features

• Switch between different models using a dropdown menu.
• Generate nice HTML output for GPT-4chan.
• Generate Markdown output for GALACTICA, including LaTeX support.
• Notebook mode that resembles OpenAI's playground.
• Chat mode for conversation and role playing.
• Load parameter presets from text files.
• Load large models in 8-bit mode.
• Split large models across your GPU(s) and CPU.
• CPU mode.
• Get responses via API.

Installation

1. You need to have the conda environment manager installed into your system. If you don't have it already, get miniconda here.

2. Then open a terminal window and create a conda environment:

conda create -n textgen
conda activate textgen

3. Install the appropriate pytorch. For NVIDIA GPUs, this should work:

conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia

For AMD GPUs, you need the ROCm version of pytorch. For running exclusively on the CPU, you just need the stock pytorch and this should probably work:

conda install pytorch torchvision torchaudio -c pytorch

4. Clone or download this repository, and then cd into its directory from your terminal window.

5. Install the required Python libraries:

pip install -r requirements.txt

After these steps, you should be able to start the webui, but first you need to download some model to load.

Downloading models

Models should be placed under models/model-name. For instance, models/gpt-j-6B for gpt-j-6B.

Hugging Face

Hugging Face is the main place to download models. These are some of my favorite:

• gpt-j-6B
• gpt-neox-20b
• OPT
• GALACTICA
• *-Erebus

The files that you need to download are the json, txt, and pytorch*.bin files. The remaining files are not necessary.

For your convenience, you can automatically download a model from HF using the script download-model.py. Its usage is very simple:

python download-model.py organization/model

For instance:

python download-model.py facebook/opt-1.3b

GPT-4chan

GPT-4chan has been shut down from Hugging Face, so you need to download it elsewhere. You have two options:

• Torrent: 16-bit / 32-bit
• Direct download: 16-bit / 32-bit

Then follow these steps to install:

1. Place the files under models/gpt4chan_model_float16 or models/gpt4chan_model
2. Place GPT-J-6B's config.json file in that same folder: config.json
3. Download GPT-J-6B under models/gpt-j-6B:

python download-model.py EleutherAI/gpt-j-6B

You don't really need all of GPT-J's files, just the tokenizer files, but you might as well download the whole thing. Those files will be automatically detected when you attempt to load gpt4chan.

Converting to pytorch (optional)

The script convert-to-torch.py allows you to convert models to .pt format, which is about 10x faster to load:

python convert-to-torch.py models/model-name

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.

Starting the webui

conda activate textgen
python server.py

Then browse to

http://localhost:7860/?__theme=dark

Optionally, you can use the following command-line flags:

-h, --help      show this help message and exit
--model MODEL   Name of the model to load by default.
--notebook      Launch the webui in notebook mode, where the output is written to the same text
                box as the input.
--chat          Launch the webui in chat mode.
--cpu           Use the CPU to generate text.
--auto-devices  Automatically split the model across the available GPU(s) and CPU.
--load-in-8bit  Load the model with 8-bit precision.
--no-listen     Make the webui unreachable from your local network.

Presets

Inference settings presets can be created under presets/ as text files. These files are detected automatically at startup.

System requirements

Check the wiki for some examples of VRAM and RAM usage in both GPU and CPU mode.

Contributing

Pull requests, suggestions and issue reports are welcome.

Other projects

Make sure to also check out the great work by KoboldAI. I have borrowed some of the presets listed on their wiki after performing a k-means clustering analysis to select the most relevant subsample.