oobabooga / text-generation-webui

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

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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.

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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.

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


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

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 GPTQ models, the additional installation steps below are necessary:

GPTQ models (4 bit mode)

Note about 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: 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. 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:

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 has been shut down from Hugging Face, so you need to download it elsewhere. You have two options:

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


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 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.


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 (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.

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.


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).


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.


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%).


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.


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.


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"


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


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

Out of memory errors? Check the low VRAM guide.


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

By default, 10 presets based on NovelAI and KoboldAI presets 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.



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



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.



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

License:GNU Affero General Public License v3.0


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