Text generation web UI

A Gradio web UI for Large Language Models.

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

Features

• 3 interface modes: default (two columns), notebook, and chat
• Multiple model backends: transformers, llama.cpp, ExLlama, ExLlamaV2, AutoGPTQ, GPTQ-for-LLaMa, CTransformers, AutoAWQ
• Dropdown menu for quickly switching between different models
• LoRA: load and unload LoRAs on the fly, train a new LoRA using QLoRA
• Precise instruction templates for chat mode, including Llama-2-chat, Alpaca, Vicuna, WizardLM, StableLM, and many others
• 4-bit, 8-bit, and CPU inference through the transformers library
• Use llama.cpp models with transformers samplers (llamacpp_HF loader)
• Multimodal pipelines, including LLaVA and MiniGPT-4
• Extensions framework
• Custom chat characters
• Very efficient text streaming
• Markdown output with LaTeX rendering, to use for instance with GALACTICA
• OpenAI-compatible API server

Documentation

To learn how to use the various features, check out the Documentation:

https://github.com/oobabooga/text-generation-webui/wiki

Installation

One-click installers

1. Clone or download the repository.
2. Run the start_linux.sh, start_windows.bat, start_macos.sh, or start_wsl.bat script depending on your OS.
3. Select your GPU vendor when asked.
4. Have fun!

How it works

The script creates a folder called installer_files where it sets up a Conda environment using Miniconda. The installation is self-contained: if you want to reinstall, just delete installer_files and run the start script again.

To launch the webui in the future after it is already installed, run the same start script.

Getting updates

Run update_linux.sh, update_windows.bat, update_macos.sh, or update_wsl.bat.

Running commands

If you ever need to install something manually in the installer_files environment, you can launch an interactive shell using the cmd script: cmd_linux.sh, cmd_windows.bat, cmd_macos.sh, or cmd_wsl.bat.

Defining command-line flags

To define persistent command-line flags like --listen or --api, edit the CMD_FLAGS.txt file with a text editor and add them there. Flags can also be provided directly to the start scripts, for instance, ./start-linux.sh --listen.

Other info

• There is no need to run any of those scripts as admin/root.
• For additional instructions about AMD setup, WSL setup, and nvcc installation, consult the documentation.
• The installer has been tested mostly on NVIDIA GPUs. If you can find a way to improve it for your AMD/Intel Arc/Mac Metal GPU, you are highly encouraged to submit a PR to this repository. The main file to be edited is one_click.py.
• For automated installation, you can use the GPU_CHOICE, USE_CUDA118, LAUNCH_AFTER_INSTALL, and INSTALL_EXTENSIONS environment variables. For instance: GPU_CHOICE=A USE_CUDA118=FALSE LAUNCH_AFTER_INSTALL=FALSE INSTALL_EXTENSIONS=FALSE ./start_linux.sh.

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 (source):

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

1. Create a new conda environment

conda create -n textgen python=3.11
conda activate textgen

2. Install Pytorch

System	GPU	Command
Linux/WSL	NVIDIA	pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
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/rocm5.6
MacOS + MPS	Any	pip3 install torch torchvision torchaudio
Windows	NVIDIA	pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
Windows	CPU only	pip3 install torch torchvision torchaudio

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

For NVIDIA, you may also need to manually install the CUDA runtime libraries:

conda install -y -c "nvidia/label/cuda-12.1.0" cuda-runtime

3. Install the web UI

git clone https://github.com/oobabooga/text-generation-webui
cd text-generation-webui
pip install -r <requirements file according to table below>

Requirements file to use:

GPU	CPU	requirements file to use
NVIDIA	has AVX2	requirements.txt
NVIDIA	no AVX2	requirements_noavx2.txt
AMD	has AVX2	requirements_amd.txt
AMD	no AVX2	requirements_amd_noavx2.txt
CPU only	has AVX2	requirements_cpu_only.txt
CPU only	no AVX2	requirements_cpu_only_noavx2.txt
Apple	Intel	requirements_apple_intel.txt
Apple	Apple Silicon	requirements_apple_silicon.txt

AMD GPU on Windows

1. Use requirements_cpu_only.txt or requirements_cpu_only_noavx2.txt in the command above.

2. Manually install llama-cpp-python using the appropriate command for your hardware: Installation from PyPI.

   • Use the LLAMA_HIPBLAS=on toggle.
   • Note the Windows remarks.

3. Manually install AutoGPTQ: Installation.

   • Perform the from-source installation - there are no prebuilt ROCm packages for Windows.

4. Manually install ExLlama by simply cloning it into the repositories folder (it will be automatically compiled at runtime after that):

cd text-generation-webui
git clone https://github.com/turboderp/exllama repositories/exllama

Older NVIDIA GPUs

1. For Kepler GPUs and older, you will need to install CUDA 11.8 instead of 12:

pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
conda install -y -c "nvidia/label/cuda-11.8.0" cuda-runtime

2. bitsandbytes >= 0.39 may not work. 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

Manual install

The requirments*.txt above contain various precompiled wheels. If you wish to compile things manually, or if you need to because no suitable wheels are available for your hardware, you can use requirements_nowheels.txt and then install your desired loaders manually.

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 these commands:

conda activate textgen
cd text-generation-webui
pip install -r <requirements file that you've used> --upgrade

Downloading models

Models should be placed in the text-generation-webui/models folder. They are usually downloaded from Hugging Face.

• Transformers or GPTQ models are made of several files and must be placed in a subfolder. Example:

text-generation-webui
├── models
│   ├── lmsys_vicuna-33b-v1.3
│   │   ├── config.json
│   │   ├── generation_config.json
│   │   ├── pytorch_model-00001-of-00007.bin
│   │   ├── pytorch_model-00002-of-00007.bin
│   │   ├── pytorch_model-00003-of-00007.bin
│   │   ├── pytorch_model-00004-of-00007.bin
│   │   ├── pytorch_model-00005-of-00007.bin
│   │   ├── pytorch_model-00006-of-00007.bin
│   │   ├── pytorch_model-00007-of-00007.bin
│   │   ├── pytorch_model.bin.index.json
│   │   ├── special_tokens_map.json
│   │   ├── tokenizer_config.json
│   │   └── tokenizer.model

• GGUF models are a single file and should be placed directly into models. Example:

text-generation-webui
├── models
│   ├── llama-2-13b-chat.Q4_K_M.gguf

In both cases, you can use the "Model" tab of the UI to download the model from Hugging Face automatically. It is also possible to download via the command-line with python download-model.py organization/model (use --help to see all the options).

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
--multi-user	Multi-user mode. Chat histories are not saved or automatically loaded. WARNING: this is likely not safe for sharing publicly.
--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.
--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.
--chat-buttons	Show buttons on the chat tab instead of a hover menu.

Model loader

Flag	Description
--loader LOADER	Choose the model loader manually, otherwise, it will get autodetected. Valid options: transformers, exllama_hf, exllamav2_hf, exllama, exllamav2, autogptq, gptq-for-llama, llama.cpp, llamacpp_hf, ctransformers, autoawq.

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 VRAM usage slightly, but it comes at a performance cost.
--xformers	Use xformer's memory efficient attention. This is really old and probably doesn't do anything.
--sdp-attention	Use PyTorch 2.0's SDP attention. Same as above.
--trust-remote-code	Set trust_remote_code=True while loading the model. Necessary for some models.
--use_fast	Set use_fast=True while loading the tokenizer.
--use_flash_attention_2	Set use_flash_attention_2=True while loading the model.

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).
--use_double_quant	use_double_quant for 4-bit.
--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.

llama.cpp

Flag	Description
--n_ctx N_CTX	Size of the prompt context.
--threads	Number of threads to use.
--threads-batch THREADS_BATCH	Number of threads to use for batches/prompt processing.
--no_mul_mat_q	Disable the mulmat kernels.
--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.
--n-gpu-layers N_GPU_LAYERS	Number of layers to offload to the GPU.
--tensor_split TENSOR_SPLIT	Split the model across multiple GPUs. Comma-separated list of proportions. Example: 18,17.
--llama_cpp_seed SEED	Seed for llama-cpp models. Default is 0 (random).
--numa	Activate NUMA task allocation for llama.cpp.
--cache-capacity CACHE_CAPACITY	Maximum cache capacity (llama-cpp-python). Examples: 2000MiB, 2GiB. When provided without units, bytes will be assumed.

ExLlama

Flag	Description
--gpu-split	Comma-separated list of VRAM (in GB) to use per GPU device for model layers. Example: 20,7,7.
--max_seq_len MAX_SEQ_LEN	Maximum sequence length.
--cfg-cache	ExLlama_HF: Create an additional cache for CFG negative prompts. Necessary to use CFG with that loader, but not necessary for CFG with base ExLlama.
--no_flash_attn	Force flash-attention to not be used.
--cache_8bit	Use 8-bit cache to save VRAM.

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.
--disable_exllama	Disable ExLlama kernel, which can improve inference speed on some systems.

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.

ctransformers

Flag	Description
--model_type MODEL_TYPE	Model type of pre-quantized model. Currently gpt2, gptj, gptneox, falcon, llama, mpt, starcoder (gptbigcode), dollyv2, and replit are supported.

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, ExLlama, ExLlamaV2, and transformers)

Flag	Description
--alpha_value ALPHA_VALUE	Positional embeddings alpha factor for NTK RoPE scaling. Use either this or compress_pos_emb, not both.
--rope_freq_base ROPE_FREQ_BASE	If greater than 0, will be used instead of alpha_value. Those two are related by rope_freq_base = 10000 * alpha_value ^ (64 / 63).
--compress_pos_emb COMPRESS_POS_EMB	Positional embeddings compression factor. Should be set to (context length) / (model's original context length). Equal to 1/rope_freq_scale.

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.
--listen-host LISTEN_HOST	The hostname 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 password in the format "username:password". Multiple credentials can also be supplied with "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 the same format as above.
--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.
--public-api-id PUBLIC_API_ID	Tunnel ID for named Cloudflare Tunnel. Use together with public-api option.
--api-port API_PORT	The listening port for the API.
--api-key API_KEY	API authentication key.

Multimodal

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

Google Colab notebook

https://colab.research.google.com/github/oobabooga/text-generation-webui/blob/main/Colab-TextGen-GPU.ipynb

Contributing

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

Community

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

Acknowledgment

In August 2023, Andreessen Horowitz (a16z) provided a generous grant to encourage and support my independent work on this project. I am extremely grateful for their trust and recognition, which will allow me to dedicate more time towards realizing the full potential of text-generation-webui.