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.

Features

• Dropdown menu for switching between models
• Notebook mode that resembles OpenAI's playground
• Chat mode for conversation and role-playing
• Instruct mode compatible with various formats, including Alpaca, Vicuna, Open Assistant, Dolly, Koala, ChatGLM, MOSS, RWKV-Raven, Galactica, StableLM, WizardLM, Baize, MPT, and INCITE
• Multimodal pipelines, including LLaVA and MiniGPT-4
• Markdown output for GALACTICA, including LaTeX rendering
• Nice HTML output for GPT-4chan
• Custom chat characters
• Advanced chat features (send images, get audio responses with TTS)
• Very efficient text streaming
• Parameter presets
• LLaMA model
• 4-bit GPTQ mode
• LoRA (loading and training)
• llama.cpp
• RWKV model
• 8-bit mode
• Layers splitting across GPU(s), CPU, and disk
• CPU mode
• FlexGen
• DeepSpeed ZeRO-3
• API with streaming and without streaming
• Extensions - see the user extensions list

Installation

One-click installers

Windows	Linux	macOS
oobabooga-windows.zip	oobabooga-linux.zip	oobabooga-macos.zip

Just download the zip above, extract it, and double-click on "start". The web UI and all its dependencies will be installed in the same folder.

• The source codes are here: https://github.com/oobabooga/one-click-installers
• There is no need to run the installers as admin.
• AMD doesn't work on Windows.
• Huge thanks to @jllllll, @ClayShoaf, and @xNul for their contributions to these installers.

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

• MacOS users: oobabooga#393
• AMD users: https://rentry.org/eq3hg

3. Install the web UI

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

4. Install GPTQ-for-LLaMa and the monkey patch

The base installation covers transformers models (AutoModelForCausalLM and AutoModelForSeq2SeqLM specifically) and llama.cpp (GGML) models.

To use 4-bit GPU models, the additional installation steps below are necessary:

GPTQ models (4 bit mode)

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

ln -s docker/{Dockerfile,docker-compose.yml,.dockerignore} .
cp docker/.env.example .env
# Edit .env and set TORCH_CUDA_ARCH_LIST based on your GPU model
docker compose up --build

You need to have docker compose v2.17 or higher installed in your system. To see how to install docker compose itself, see the guide in here.

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:

• Pythia
• OPT
• GALACTICA
• GPT-J 6B

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.

GGML models

You can drop these directly into the models/ folder, making sure that the file name contains ggml somewhere and ends in .bin.

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

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.
--character CHARACTER	The name of the character to load in chat mode by default.
--model MODEL	Name of the model to load by default.
--lora LORA [LORA ...]	The list of LoRAs to load. If you want to load more than one LoRA, write the names separated by spaces.
--model-dir MODEL_DIR	Path to directory with all the models.
--lora-dir LORA_DIR	Path to directory with all the loras.
--model-menu	Show a model menu in the terminal when the web UI is first launched.
--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. Warning: Training on CPU is extremely slow.
--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.
--xformers	Use xformer's memory efficient attention. This should increase your tokens/s.
--sdp-attention	Use torch 2.0's sdp attention.
--trust-remote-code	Set trust_remote_code=True while loading a model. Necessary for ChatGLM.

llama.cpp

Flag	Description
--threads	Number of threads to use.
--n_batch	Maximum number of prompt tokens to batch together when calling llama_eval.
--no-mmap	Prevent mmap from being used.
--mlock	Force the system to keep the model in RAM.

GPTQ

Flag	Description
--wbits WBITS	Load a pre-quantized model with specified precision in bits. 2, 3, 4 and 8 are supported.
--model_type MODEL_TYPE	Model type of pre-quantized model. Currently LLaMA, OPT, and GPT-J are supported.
--groupsize GROUPSIZE	Group size.
--pre_layer PRE_LAYER	The number of layers to allocate to the GPU. Setting this parameter enables CPU offloading for 4-bit models.
--checkpoint CHECKPOINT	The path to the quantized checkpoint file. If not specified, it will be automatically detected.
--monkey-patch	Apply the monkey patch for using LoRAs with quantized models.
--quant_attn	(triton) Enable quant attention.
--warmup_autotune	(triton) Enable warmup autotune.
--fused_mlp	(triton) Enable fused mlp.

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-host LISTEN_HOST	The hostname that the server will use.
--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"

API

Flag	Description
--api	Enable the API extension.
--public-api	Create a public URL for the API using Cloudfare.

Multimodal

Flag	Description
--multimodal-pipeline PIPELINE	The multimodal pipeline to use. Examples: llava-7b, llava-13b.

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

Documentation

Make sure to check out the documentation for an in-depth guide on how to use the web UI.

https://github.com/oobabooga/text-generation-webui/tree/main/docs

Contributing

Pull requests, suggestions, and issue reports are welcome.

You are also welcome to review open pull requests.

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

• Gradio dropdown menu refresh button, code for reloading the interface: https://github.com/AUTOMATIC1111/stable-diffusion-webui
• Verbose preset: Anonymous 4chan user.
• NovelAI and KoboldAI presets: https://github.com/KoboldAI/KoboldAI-Client/wiki/Settings-Presets
• Code for early stopping in chat mode, code for some of the sliders: https://github.com/PygmalionAI/gradio-ui/