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.

Try the Deep Reason extension

Features

• Supports multiple text generation backends in one UI/API, including Transformers, llama.cpp, ExLlamaV3, and ExLlamaV2. TensorRT-LLM is supported via its own Dockerfile, and the Transformers loader is compatible with libraries like AutoGPTQ, AutoAWQ, HQQ, and AQLM, but they must be installed manually.
• OpenAI-compatible API with Chat and Completions endpoints – see examples.
• Automatic prompt formatting using Jinja2 templates.
• Three chat modes: instruct, chat-instruct, and chat, with automatic prompt templates in chat-instruct.
• "Past chats" menu to quickly switch between conversations.
• Free-form text generation in the Default/Notebook tabs without being limited to chat turns. You can send formatted conversations from the Chat tab to these.
• Multiple sampling parameters and generation options for sophisticated text generation control.
• Switch between different models easily in the UI without restarting.
• Simple LoRA fine-tuning tool.
• Requirements installed in a self-contained installer_files directory that doesn't interfere with the system environment.
• Extension support, with numerous built-in and user-contributed extensions available. See the wiki and extensions directory for details.
• Completely private: no telemetry, no tracking, no remote connections.

How to install

Option 1: Portable builds

Compatible with GGUF (llama.cpp) models, just unzip and run, no installation. Available for Windows, Linux, and macOS.

Download from: https://github.com/oobabooga/text-generation-webui/releases

Option 2: One-click installer

1. Clone or download the repository.
2. Run the script that matches your OS: start_linux.sh, start_windows.bat, start_macos.sh, or start_wsl.bat.
3. Select your GPU vendor when asked.
4. Once the installation ends, browse to http://localhost:7860.
5. Have fun!

To restart the web UI later, just run the same start_ script. If you need to reinstall, delete the installer_files folder created during setup and run the script again.

You can use command-line flags, like ./start_linux.sh --help, or add them to CMD_FLAGS.txt (such as --api to enable API use). To update the project, run update_wizard_linux.sh, update_wizard_windows.bat, update_wizard_macos.sh, or update_wizard_wsl.bat.

Setup details and information about installing manually

One-click-installer

The script uses Miniconda to set up a Conda environment in the installer_files folder.

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.

• There is no need to run any of those scripts (start_, update_wizard_, or cmd_) as admin/root.
• To install the requirements for extensions, you can use the extensions_reqs script for your OS. At the end, this script will install the main requirements for the project to make sure that they take precedence in case of version conflicts.
• For additional instructions about AMD and WSL setup, consult the documentation.
• 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=TRUE ./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==2.6.0 torchvision==0.21.0 torchaudio==2.6.0 --index-url https://download.pytorch.org/whl/cu124
Linux/WSL	CPU only	pip3 install torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0 --index-url https://download.pytorch.org/whl/cpu
Linux	AMD	pip3 install torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0 --index-url https://download.pytorch.org/whl/rocm6.1
MacOS + MPS	Any	pip3 install torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0
Windows	NVIDIA	pip3 install torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0 --index-url https://download.pytorch.org/whl/cu124
Windows	CPU only	pip3 install torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0

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

If you need nvcc to compile some library manually, you will additionally need to install this:

conda install -y -c "nvidia/label/cuda-12.4.1" cuda

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

Start the web UI

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

Then browse to

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

Manual install

The requirements*.txt above contain various wheels precompiled through GitHub Actions. 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

For NVIDIA GPU:
ln -s docker/{nvidia/Dockerfile,nvidia/docker-compose.yml,.dockerignore} .
For AMD GPU: 
ln -s docker/{amd/Dockerfile,intel/docker-compose.yml,.dockerignore} .
For Intel GPU:
ln -s docker/{intel/Dockerfile,amd/docker-compose.yml,.dockerignore} .
For CPU only
ln -s docker/{cpu/Dockerfile,cpu/docker-compose.yml,.dockerignore} .
cp docker/.env.example .env
#Create logs/cache dir : 
mkdir -p logs cache
# Edit .env and set: 
#   TORCH_CUDA_ARCH_LIST based on your GPU model
#   APP_RUNTIME_GID      your host user's group id (run `id -g` in a terminal)
#   BUILD_EXTENIONS      optionally add comma separated list of extensions to build
# Edit CMD_FLAGS.txt and add in it the options you want to execute (like --listen --cpu)
# 
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 change. To update, use these commands:

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

List of command-line flags

usage: server.py [-h] [--multi-user] [--character CHARACTER] [--model MODEL] [--lora LORA [LORA ...]] [--model-dir MODEL_DIR] [--lora-dir LORA_DIR] [--model-menu] [--settings SETTINGS]
                 [--extensions EXTENSIONS [EXTENSIONS ...]] [--verbose] [--idle-timeout IDLE_TIMEOUT] [--loader LOADER] [--cpu] [--cpu-memory CPU_MEMORY] [--disk] [--disk-cache-dir DISK_CACHE_DIR]
                 [--load-in-8bit] [--bf16] [--no-cache] [--trust-remote-code] [--force-safetensors] [--no_use_fast] [--use_flash_attention_2] [--use_eager_attention] [--torch-compile] [--load-in-4bit]
                 [--use_double_quant] [--compute_dtype COMPUTE_DTYPE] [--quant_type QUANT_TYPE] [--flash-attn] [--threads THREADS] [--threads-batch THREADS_BATCH] [--batch-size BATCH_SIZE] [--no-mmap]
                 [--mlock] [--n-gpu-layers N_GPU_LAYERS] [--tensor-split TENSOR_SPLIT] [--numa] [--no-kv-offload] [--row-split] [--extra-flags EXTRA_FLAGS] [--streaming-llm] [--ctx-size CTX_SIZE]
                 [--model-draft MODEL_DRAFT] [--draft-max DRAFT_MAX] [--gpu-layers-draft GPU_LAYERS_DRAFT] [--device-draft DEVICE_DRAFT] [--ctx-size-draft CTX_SIZE_DRAFT] [--gpu-split GPU_SPLIT]
                 [--autosplit] [--cfg-cache] [--no_flash_attn] [--no_xformers] [--no_sdpa] [--num_experts_per_token NUM_EXPERTS_PER_TOKEN] [--enable_tp] [--hqq-backend HQQ_BACKEND] [--cpp-runner]
                 [--cache_type CACHE_TYPE] [--deepspeed] [--nvme-offload-dir NVME_OFFLOAD_DIR] [--local_rank LOCAL_RANK] [--alpha_value ALPHA_VALUE] [--rope_freq_base ROPE_FREQ_BASE]
                 [--compress_pos_emb COMPRESS_POS_EMB] [--listen] [--listen-port LISTEN_PORT] [--listen-host LISTEN_HOST] [--share] [--auto-launch] [--gradio-auth GRADIO_AUTH]
                 [--gradio-auth-path GRADIO_AUTH_PATH] [--ssl-keyfile SSL_KEYFILE] [--ssl-certfile SSL_CERTFILE] [--subpath SUBPATH] [--old-colors] [--api] [--public-api]
                 [--public-api-id PUBLIC_API_ID] [--api-port API_PORT] [--api-key API_KEY] [--admin-key ADMIN_KEY] [--api-enable-ipv6] [--api-disable-ipv4] [--nowebui]

Text generation web UI

options:
  -h, --help                                           show this help message and exit

Basic settings:
  --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                                  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.
  --idle-timeout IDLE_TIMEOUT                          Unload model after this many minutes of inactivity. It will be automatically reloaded when you try to use it again.

Model loader:
  --loader LOADER                                      Choose the model loader manually, otherwise, it will get autodetected. Valid options: Transformers, llama.cpp, ExLlamav3_HF, ExLlamav2_HF,
                                                       ExLlamav2, HQQ, TensorRT-LLM.

Transformers/Accelerate:
  --cpu                                                Use the CPU to generate text. Warning: Training on CPU is extremely slow.
  --cpu-memory CPU_MEMORY                              Maximum CPU memory in GiB. Use this for CPU offloading.
  --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 "user_data/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.
  --trust-remote-code                                  Set trust_remote_code=True while loading the model. Necessary for some models.
  --force-safetensors                                  Set use_safetensors=True while loading the model. This prevents arbitrary code execution.
  --no_use_fast                                        Set use_fast=False while loading the tokenizer (it's True by default). Use this if you have any problems related to use_fast.
  --use_flash_attention_2                              Set use_flash_attention_2=True while loading the model.
  --use_eager_attention                                Set attn_implementation= eager while loading the model.
  --torch-compile                                      Compile the model with torch.compile for improved performance.

bitsandbytes 4-bit:
  --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:
  --flash-attn                                         Use flash-attention.
  --threads THREADS                                    Number of threads to use.
  --threads-batch THREADS_BATCH                        Number of threads to use for batches/prompt processing.
  --batch-size BATCH_SIZE                              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: 60,40.
  --numa                                               Activate NUMA task allocation for llama.cpp.
  --no-kv-offload                                      Do not offload the K, Q, V to the GPU. This saves VRAM but reduces the performance.
  --row-split                                          Split the model by rows across GPUs. This may improve multi-gpu performance.
  --extra-flags EXTRA_FLAGS                            Extra flags to pass to llama-server. Format: "flag1=value1;flag2;flag3=value3". Example: "override-tensor=exps=CPU"
  --streaming-llm                                      Activate StreamingLLM to avoid re-evaluating the entire prompt when old messages are removed.

Context and cache management:
  --ctx-size CTX_SIZE, --n_ctx CTX_SIZE, --max_seq_len CTX_SIZE
                                                       Context size in tokens.

Speculative decoding:
  --model-draft MODEL_DRAFT                            Path to the draft model for speculative decoding.
  --draft-max DRAFT_MAX                                Number of tokens to draft for speculative decoding.
  --gpu-layers-draft GPU_LAYERS_DRAFT                  Number of layers to offload to the GPU for the draft model.
  --device-draft DEVICE_DRAFT                          Comma-separated list of devices to use for offloading the draft model. Example: CUDA0,CUDA1
  --ctx-size-draft CTX_SIZE_DRAFT                      Size of the prompt context for the draft model. If 0, uses the same as the main model.

ExLlamaV2:
  --gpu-split GPU_SPLIT                                Comma-separated list of VRAM (in GB) to use per GPU device for model layers. Example: 20,7,7.
  --autosplit                                          Autosplit the model tensors across the available GPUs. This causes --gpu-split to be ignored.
  --cfg-cache                                          ExLlamav2_HF: Create an additional cache for CFG negative prompts. Necessary to use CFG with that loader.
  --no_flash_attn                                      Force flash-attention to not be used.
  --no_xformers                                        Force xformers to not be used.
  --no_sdpa                                            Force Torch SDPA to not be used.
  --num_experts_per_token NUM_EXPERTS_PER_TOKEN        Number of experts to use for generation. Applies to MoE models like Mixtral.
  --enable_tp                                          Enable Tensor Parallelism (TP) in ExLlamaV2.

HQQ:
  --hqq-backend HQQ_BACKEND                            Backend for the HQQ loader. Valid options: PYTORCH, PYTORCH_COMPILE, ATEN.

TensorRT-LLM:
  --cpp-runner                                         Use the ModelRunnerCpp runner, which is faster than the default ModelRunner but doesn't support streaming yet.

Cache:
  --cache_type CACHE_TYPE                              KV cache type; valid options: llama.cpp - fp16, q8_0, q4_0; ExLlamaV2 - fp16, fp8, q8, q6, q4.

DeepSpeed:
  --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.

RoPE:
  --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:
  --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 GRADIO_AUTH                            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.
  --subpath SUBPATH                                    Customize the subpath for gradio, use with reverse proxy
  --old-colors                                         Use the legacy Gradio colors, before the December/2024 update.

API:
  --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.
  --admin-key ADMIN_KEY                                API authentication key for admin tasks like loading and unloading models. If not set, will be the same as --api-key.
  --api-enable-ipv6                                    Enable IPv6 for the API
  --api-disable-ipv4                                   Disable IPv4 for the API
  --nowebui                                            Do not launch the Gradio UI. Useful for launching the API in standalone mode.

Documentation

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

Downloading models

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

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

text-generation-webui
└── user_data
    └── models
        └── llama-2-13b-chat.Q4_K_M.gguf

• The remaining model types (like 16-bit Transformers models and EXL2 models) are made of several files and must be placed in a subfolder. Example:

text-generation-webui
└── user_data
    └── 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

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 it via the command-line with:

python download-model.py organization/model

Run python download-model.py --help to see all the options.

Google Colab notebook

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

Community

https://www.reddit.com/r/Oobabooga/

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.