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

• Multiple backends for text generation in a single UI and API, including Transformers, llama.cpp (through llama-cpp-python), ExLlamaV2, AutoGPTQ, and TensorRT-LLM. AutoAWQ, HQQ, and AQLM are also supported through the Transformers loader.
• OpenAI-compatible API server with Chat and Completions endpoints – see the examples.
• Automatic prompt formatting for each model using the Jinja2 template in its metadata.
• Three chat modes: instruct, chat-instruct, and chat, allowing for both instruction-following and casual conversations with characters. chat-instruct mode automatically applies the model's template to the chat prompt, ensuring high-quality outputs without manual setup.
• "Past chats" menu to quickly switch between conversations and start new ones.
• Free-form generation in the Default/Notebook tabs without being limited to chat turns. Send formatted chat conversations from the Chat tab to these tabs.
• Multiple sampling parameters and generation options for sophisticated text generation control.
• Easy switching between different models through the UI without restarting, using the "Model" tab.
• Simple LoRA fine-tuning tool to customize models with your data.
• All in one folder. The requirements are installed in a self-contained installer_files folder that doesn't interfere with the system's environment.
• Extensions support, including numerous built-in and user-contributed extensions. See the wiki and the extensions directory for details.

How to install

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. Once the installation ends, browse to http://localhost:7860.
5. Have fun!

To restart the web UI in the future, run the start_ script again.

This script creates an installer_files folder where it sets up the project's requirements. If you need to reinstall the requirements, just delete that folder and start the web UI again.

The script accepts command-line flags, such as ./start_linux.sh --help. Alternatively, you can edit the CMD_FLAGS.txt file with a text editor and add your flags there, such as --api in case you need to use the API.

To get updates in the future, 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==2.2.2 torchvision==0.17.2 torchaudio==2.2.2 --index-url https://download.pytorch.org/whl/cu121
Linux/WSL	CPU only	pip3 install torch==2.2.2 torchvision==0.17.2 torchaudio==2.2.2 --index-url https://download.pytorch.org/whl/cpu
Linux	AMD	pip3 install torch==2.2.2 torchvision==0.17.2 torchaudio==2.2.2 --index-url https://download.pytorch.org/whl/rocm5.6
MacOS + MPS	Any	pip3 install torch==2.2.2 torchvision==0.17.2 torchaudio==2.2.2
Windows	NVIDIA	pip3 install torch==2.2.2 torchvision==0.17.2 torchaudio==2.2.2 --index-url https://download.pytorch.org/whl/cu121
Windows	CPU only	pip3 install torch==2.2.2 torchvision==0.17.2 torchaudio==2.2.2

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

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

conda install -y -c "nvidia/label/cuda-12.1.1" cuda-runtime

If you need nvcc to compile some library manually, replace the command above with

conda install -y -c "nvidia/label/cuda-12.1.1" 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

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.

Older NVIDIA GPUs

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

pip3 install torch==2.2.2 torchvision==0.17.2 torchaudio==2.2.2 --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 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] [--chat-buttons] [--idle-timeout IDLE_TIMEOUT] [--loader LOADER] [--cpu] [--auto-devices]
                 [--gpu-memory GPU_MEMORY [GPU_MEMORY ...]] [--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] [--load-in-4bit] [--use_double_quant] [--compute_dtype COMPUTE_DTYPE] [--quant_type QUANT_TYPE]
                 [--flash-attn] [--tensorcores] [--n_ctx N_CTX] [--threads THREADS] [--threads-batch THREADS_BATCH] [--no_mul_mat_q] [--n_batch N_BATCH] [--no-mmap] [--mlock]
                 [--n-gpu-layers N_GPU_LAYERS] [--tensor_split TENSOR_SPLIT] [--numa] [--logits_all] [--no_offload_kqv] [--cache-capacity CACHE_CAPACITY] [--row_split] [--streaming-llm]
                 [--attention-sink-size ATTENTION_SINK_SIZE] [--tokenizer-dir TOKENIZER_DIR] [--gpu-split GPU_SPLIT] [--autosplit] [--max_seq_len MAX_SEQ_LEN] [--cfg-cache] [--no_flash_attn]
                 [--no_xformers] [--no_sdpa] [--cache_8bit] [--cache_4bit] [--num_experts_per_token NUM_EXPERTS_PER_TOKEN] [--triton] [--no_inject_fused_mlp] [--no_use_cuda_fp16] [--desc_act]
                 [--disable_exllama] [--disable_exllamav2] [--wbits WBITS] [--groupsize GROUPSIZE] [--hqq-backend HQQ_BACKEND] [--cpp-runner] [--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] [--api] [--public-api] [--public-api-id PUBLIC_API_ID] [--api-port API_PORT] [--api-key API_KEY] [--admin-key ADMIN_KEY] [--nowebui]
                 [--multimodal-pipeline MULTIMODAL_PIPELINE] [--model_type MODEL_TYPE] [--pre_layer PRE_LAYER [PRE_LAYER ...]] [--checkpoint CHECKPOINT] [--monkey-patch] [--no_inject_fused_attention]

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.
  --chat-buttons                                 Show buttons on the chat tab instead of a hover menu.
  --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, llamacpp_HF, ExLlamav2_HF, ExLlamav2,
                                                 AutoGPTQ.

Transformers/Accelerate:
  --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.
  --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.

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.
  --tensorcores                                  NVIDIA only: use llama-cpp-python compiled with tensor cores support. This may increase performance on newer cards.
  --n_ctx N_CTX                                  Size of the prompt context.
  --threads 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 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: 60,40.
  --numa                                         Activate NUMA task allocation for llama.cpp.
  --logits_all                                   Needs to be set for perplexity evaluation to work. Otherwise, ignore it, as it makes prompt processing slower.
  --no_offload_kqv                               Do not offload the K, Q, V to the GPU. This saves VRAM but reduces the performance.
  --cache-capacity CACHE_CAPACITY                Maximum cache capacity (llama-cpp-python). Examples: 2000MiB, 2GiB. When provided without units, bytes will be assumed.
  --row_split                                    Split the model by rows across GPUs. This may improve multi-gpu performance.
  --streaming-llm                                Activate StreamingLLM to avoid re-evaluating the entire prompt when old messages are removed.
  --attention-sink-size ATTENTION_SINK_SIZE      StreamingLLM: number of sink tokens. Only used if the trimmed prompt does not share a prefix with the old prompt.
  --tokenizer-dir TOKENIZER_DIR                  Load the tokenizer from this folder. Meant to be used with llamacpp_HF through the command-line.

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.
  --max_seq_len MAX_SEQ_LEN                      Maximum sequence length.
  --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.
  --cache_8bit                                   Use 8-bit cache to save VRAM.
  --cache_4bit                                   Use Q4 cache to save VRAM.
  --num_experts_per_token NUM_EXPERTS_PER_TOKEN  Number of experts to use for generation. Applies to MoE models like Mixtral.

AutoGPTQ:
  --triton                                       Use triton.
  --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 do not 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.
  --disable_exllamav2                            Disable ExLlamav2 kernel.
  --wbits WBITS                                  Load a pre-quantized model with specified precision in bits. 2, 3, 4 and 8 are supported.
  --groupsize GROUPSIZE                          Group size.

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.

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

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.
  --nowebui                                      Do not launch the Gradio UI. Useful for launching the API in standalone mode.

Multimodal:
  --multimodal-pipeline MULTIMODAL_PIPELINE      The multimodal pipeline to use. Examples: llava-7b, llava-13b.

Documentation

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

Downloading models

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

• 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

• The remaining model types (like 16-bit transformers models and 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

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

• 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.