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Text generation web UI

A gradio web UI for running Large Language Models like LLaMA, llama.cpp, GPT-J, Pythia, OPT, and GALACTICA.

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

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Features

Installation

One-click installers

Just all the files in the source code below as zip, extract it, and double click on "install". The web UI and all its dependencies will be installed in the same folder.

  • To download a model, double click on "download-model"
  • To start the web UI, double click on "start-webui"

Source codes: https://github.com/danmincu/one-click-installers-m40

Note

Thanks to @jllllll and @ClayShoaf, the Windows 1-click installer now sets up 8-bit and 4-bit requirements out of the box. No additional installation steps are necessary.

Note

There is no need to run the installer as admin.

Install the Tesla M40 specific version on Ubuntu 22.04

Prerequisites:

  • Ubuntu 22.04 minimal server
  • git (sudo apt-get install git)
  • install Nvidia drivers with cuda-11.7 NOTE: this version is important for the Tesla M40 to work
  • reboot after GPU drivers are installed
# Updated the drivers with the following commands

wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-ubuntu2204.pin
sudo mv cuda-ubuntu2204.pin /etc/apt/preferences.d/cuda-repository-pin-600
sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/3bf863cc.pub
sudo add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/ /"
sudo apt-get update
sudo apt-get -y install cuda-11.7

To install the Web UI run the following script in your home directory linux-install.sh

Note: this git forked GPTQ-for-LLaMa is the secret sauce to have the M40 working.

Manual installation using Conda

Recommended if you have some experience with the command-line.

On Windows, I additionally recommend carrying out the installation on WSL instead of the base system: WSL installation guide.

0. Install Conda

https://docs.conda.io/en/latest/miniconda.html

On Linux or WSL, it can be automatically installed with these two commands:

curl -sL "https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh" > "Miniconda3.sh"
bash Miniconda3.sh

Source: https://educe-ubc.github.io/conda.html

1. Create a new conda environment

conda create -n textgen python=3.10.9
conda activate textgen

2. Install Pytorch

System GPU Command
Linux/WSL NVIDIA pip3 install torch torchvision torchaudio
Linux AMD pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm5.4.2
MacOS + MPS (untested) Any pip3 install torch torchvision torchaudio

The up to date commands can be found here: https://pytorch.org/get-started/locally/.

2.1 Special instructions

3. Install the web UI

git clone https://github.com/oobabooga/text-generation-webui
cd text-generation-webui
pip install -r requirements.txt

Note

For bitsandbytes and --load-in-8bit to work on Linux/WSL, this dirty fix is currently necessary: oobabooga#400 (comment)

Alternative: manual Windows installation

As an alternative to the recommended WSL method, you can install the web UI natively on Windows using this guide. It will be a lot harder and the performance may be slower: Windows installation guide.

Alternative: Docker

cp .env.example .env
docker compose up --build

Make sure to edit .env.example and set the appropriate CUDA version for your GPU.

You need to have docker compose v2.17 or higher installed in your system. For installation instructions, see Docker compose installation.

Contributed by @loeken in #633

Updating the requirements

From time to time, the requirements.txt changes. To update, use this command:

conda activate textgen
cd text-generation-webui
pip install -r requirements.txt --upgrade

Downloading models

Models should be placed inside the models folder.

Hugging Face is the main place to download models. These are some examples:

You can automatically download a model from HF using the script download-model.py:

python download-model.py organization/model

For example:

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

If you want to download a model manually, note that all you need are the json, txt, and pytorch*.bin (or model*.safetensors) files. The remaining files are not necessary.

GPT-4chan

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

The 32-bit version is only relevant if you intend to run the model in CPU mode. Otherwise, you should use the 16-bit version.

After downloading the model, follow these steps:

  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's tokenizer files (they will be automatically detected when you attempt to load GPT-4chan):
python download-model.py EleutherAI/gpt-j-6B --text-only

Starting the web UI

conda activate textgen
cd text-generation-webui
python server.py

Then browse to

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

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

Basic settings

Flag Description
-h, --help Show this help message and exit.
--notebook Launch the web UI in notebook mode, where the output is written to the same text box as the input.
--chat Launch the web UI in chat mode.
--model MODEL Name of the model to load by default.
--lora LORA Name of the LoRA to apply to the model by default.
--model-dir MODEL_DIR Path to directory with all the models.
--lora-dir LORA_DIR Path to directory with all the loras.
--no-stream Don't stream the text output in real time.
--settings SETTINGS_FILE Load the default interface settings from this json file. See settings-template.json for an example. If you create a file called settings.json, this file will be loaded by default without the need to use the --settings flag.
--extensions EXTENSIONS [EXTENSIONS ...] The list of extensions to load. If you want to load more than one extension, write the names separated by spaces.
--verbose Print the prompts to the terminal.

Accelerate/transformers

Flag Description
--cpu Use the CPU to generate text.
--auto-devices Automatically split the model across the available GPU(s) and CPU.
--gpu-memory GPU_MEMORY [GPU_MEMORY ...] Maxmimum GPU memory in GiB to be allocated per GPU. Example: --gpu-memory 10 for a single GPU, --gpu-memory 10 5 for two GPUs. You can also set values in MiB like --gpu-memory 3500MiB.
--cpu-memory CPU_MEMORY Maximum CPU memory in GiB to allocate for offloaded weights. Same as above.
--disk If the model is too large for your GPU(s) and CPU combined, send the remaining layers to the disk.
--disk-cache-dir DISK_CACHE_DIR Directory to save the disk cache to. Defaults to cache/.
--load-in-8bit Load the model with 8-bit precision.
--bf16 Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU.
--no-cache Set use_cache to False while generating text. This reduces the VRAM usage a bit with a performance cost.

llama.cpp

Flag Description
--threads Number of threads to use in llama.cpp.

GPTQ

Flag Description
--wbits WBITS GPTQ: Load a pre-quantized model with specified precision in bits. 2, 3, 4 and 8 are supported.
--model_type MODEL_TYPE GPTQ: Model type of pre-quantized model. Currently LLaMA, OPT, and GPT-J are supported.
--groupsize GROUPSIZE GPTQ: Group size.
--pre_layer PRE_LAYER GPTQ: The number of layers to allocate to the GPU. Setting this parameter enables CPU offloading for 4-bit models.

FlexGen

Flag Description
--flexgen Enable the use of FlexGen offloading.
--percent PERCENT [PERCENT ...] FlexGen: allocation percentages. Must be 6 numbers separated by spaces (default: 0, 100, 100, 0, 100, 0).
--compress-weight FlexGen: Whether to compress weight (default: False).
--pin-weight [PIN_WEIGHT] FlexGen: whether to pin weights (setting this to False reduces CPU memory by 20%).

DeepSpeed

Flag Description
--deepspeed Enable the use of DeepSpeed ZeRO-3 for inference via the Transformers integration.
--nvme-offload-dir NVME_OFFLOAD_DIR DeepSpeed: Directory to use for ZeRO-3 NVME offloading.
--local_rank LOCAL_RANK DeepSpeed: Optional argument for distributed setups.

RWKV

Flag Description
--rwkv-strategy RWKV_STRATEGY RWKV: The strategy to use while loading the model. Examples: "cpu fp32", "cuda fp16", "cuda fp16i8".
--rwkv-cuda-on RWKV: Compile the CUDA kernel for better performance.

Gradio

Flag Description
--listen Make the web UI reachable from your local network.
--listen-port LISTEN_PORT The listening port that the server will use.
--share Create a public URL. This is useful for running the web UI on Google Colab or similar.
--auto-launch Open the web UI in the default browser upon launch.
--gradio-auth-path GRADIO_AUTH_PATH Set the gradio authentication file path. The file should contain one or more user:password pairs in this format: "u1:p1,u2:p2,u3:p3"

Out of memory errors? Check the low VRAM guide.

Presets

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

By default, 10 presets by NovelAI and KoboldAI are included. These were selected out of a sample of 43 presets after applying a K-Means clustering algorithm and selecting the elements closest to the average of each cluster.

Visualization

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.

Before reporting a bug, make sure that you have:

  1. Created a conda environment and installed the dependencies exactly as in the Installation section above.
  2. Searched to see if an issue already exists for the issue you encountered.

Credits