Text generation web UI

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

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

Features

• 3 interface modes: default, notebook, and chat
• Multiple model backends: transformers, llama.cpp, ExLlama, AutoGPTQ, GPTQ-for-LLaMa
• Dropdown menu for quickly switching between different models
• LoRA: load and unload LoRAs on the fly, train a new LoRA
• Precise instruction templates for chat mode, including Llama 2, Alpaca, Vicuna, WizardLM, StableLM, and many others
• Multimodal pipelines, including LLaVA and MiniGPT-4
• 8-bit and 4-bit inference through bitsandbytes
• CPU mode for transformers models
• DeepSpeed ZeRO-3 inference
• Extensions
• Custom chat characters
• Very efficient text streaming
• Markdown output with LaTeX rendering, to use for instance with GALACTICA
• Nice HTML output for GPT-4chan
• API, including endpoints for websocket streaming (see the examples)

To learn how to use the various features, check out the Documentation: https://github.com/oobabooga/text-generation-webui/tree/main/docs

Installation

One-click installers

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

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/WSL	CPU only	pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
Linux	AMD	pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm5.4.2
MacOS + MPS	Any	pip3 install torch torchvision torchaudio
Windows	NVIDIA	pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117
Windows	CPU only	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: https://github.com/oobabooga/text-generation-webui/pull/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

bitsandbytes

bitsandbytes >= 0.39 may not work on older NVIDIA GPUs. In that case, to use --load-in-8bit, you may have to downgrade like this:

• Linux: pip install bitsandbytes==0.38.1
• Windows: pip install https://github.com/jllllll/bitsandbytes-windows-webui/raw/main/bitsandbytes-0.38.1-py3-none-any.whl

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. See this guide for instructions.
• For additional docker files, check out this repository.

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

To download a protected model, set env vars HF_USER and HF_PASS to your Hugging Face username and password (or User Access Token). The model's terms must first be accepted on the HF website.

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

Instructions

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

When you load this model in default or notebook modes, the "HTML" tab will show the generated text in 4chan format.

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.
--multi-user	Multi-user mode. Chat histories are not saved or automatically loaded. WARNING: this is highly experimental.
--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 yaml file. See settings-template.yaml for an example. If you create a file called settings.yaml, 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.

Model loader

Flag	Description
--loader LOADER	Choose the model loader manually, otherwise, it will get autodetected. Valid options: transformers, autogptq, gptq-for-llama, exllama, exllama_hf, llamacpp, rwkv

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 ...]	Maximum 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 (using bitsandbytes).
--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 and Falcon.

Accelerate 4-bit

⚠️ Requires minimum compute of 7.0 on Windows at the moment.

Flag	Description
--load-in-4bit	Load the model with 4-bit precision (using bitsandbytes).
--compute_dtype COMPUTE_DTYPE	compute dtype for 4-bit. Valid options: bfloat16, float16, float32.
--quant_type QUANT_TYPE	quant_type for 4-bit. Valid options: nf4, fp4.
--use_double_quant	use_double_quant for 4-bit.

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.
--cache-capacity CACHE_CAPACITY	Maximum cache capacity. Examples: 2000MiB, 2GiB. When provided without units, bytes will be assumed.
--n-gpu-layers N_GPU_LAYERS	Number of layers to offload to the GPU. Only works if llama-cpp-python was compiled with BLAS. Set this to 1000000000 to offload all layers to the GPU.
--n_ctx N_CTX	Size of the prompt context.
--llama_cpp_seed SEED	Seed for llama-cpp models. Default 0 (random).
--n_gqa N_GQA	grouped-query attention. Must be 8 for llama-2 70b.
--rms_norm_eps RMS_NORM_EPS	5e-6 is a good value for llama-2 models.
--cpu	Use the CPU version of llama-cpp-python instead of the GPU-accelerated version.

AutoGPTQ

Flag	Description
--triton	Use triton.
--no_inject_fused_attention	Disable the use of fused attention, which will use less VRAM at the cost of slower inference.
--no_inject_fused_mlp	Triton mode only: disable the use of fused MLP, which will use less VRAM at the cost of slower inference.
--no_use_cuda_fp16	This can make models faster on some systems.
--desc_act	For models that don't have a quantize_config.json, this parameter is used to define whether to set desc_act or not in BaseQuantizeConfig.

ExLlama

Flag	Description
--gpu-split	Comma-separated list of VRAM (in GB) to use per GPU device for model layers, e.g. 20,7,7
--max_seq_len MAX_SEQ_LEN	Maximum sequence length.

GPTQ-for-LLaMa

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 [PRE_LAYER ...]	The number of layers to allocate to the GPU. Setting this parameter enables CPU offloading for 4-bit models. For multi-gpu, write the numbers separated by spaces, eg --pre_layer 30 60.
--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.

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.

RoPE (for llama.cpp and ExLlama only)

Flag	Description
--compress_pos_emb COMPRESS_POS_EMB	Positional embeddings compression factor. Should typically be set to max_seq_len / 2048.
--alpha_value ALPHA_VALUE	Positional embeddings alpha factor for NTK RoPE scaling. Scaling is not identical to embedding compression. Use either this or compress_pos_emb, not both.

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 USER:PWD	set gradio authentication like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"
--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"
--ssl-keyfile SSL_KEYFILE	The path to the SSL certificate key file.
--ssl-certfile SSL_CERTFILE	The path to the SSL certificate cert file.

API

Flag	Description
--api	Enable the API extension.
--public-api	Create a public URL for the API using Cloudfare.
--api-blocking-port BLOCKING_PORT	The listening port for the blocking API.
--api-streaming-port STREAMING_PORT	The listening port for the streaming API.

Multimodal

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

Presets

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

The presets that are included by default are the result of a contest that received 7215 votes. More details can be found here.

Contributing

If you would like to contribute to the project, check out the Contributing guidelines.

Community

• Subreddit: https://www.reddit.com/r/oobaboogazz/
• Discord: https://discord.gg/jwZCF2dPQN