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Local builds


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.

Image1 Image2
Image1 Image2


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


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.

Manual installation using Conda

Recommended if you have some experience with the command-line.

0. Install Conda


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=3.10.9
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/.

3. Install the web UI

git clone https://github.com/oobabooga/text-generation-webui
cd text-generation-webui
pip install -r requirements.txt

AMD, Metal, Intel Arc, and CPUs without AVX2

  1. Replace the last command above with
pip install -r requirements_nocuda.txt
  1. Manually install llama-cpp-python using the appropriate command for your hardware: Installation from PyPI.

  2. Do the same for CTransformers: Installation.

  3. AMD: Manually install AutoGPTQ: Installation.

  4. AMD: 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

bitsandbytes on older NVIDIA GPUs

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

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.txt --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:
├── 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:
├── 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 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

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


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 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.
--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 chat tab instead of hover menu.

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, ctransformers


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.

GGUF (for llama.cpp and ctransformers)

Flag Description
--threads Number of threads to use.
--n_batch Maximum number of prompt tokens to batch together when calling llama_eval.
--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.


Flag Description
--no-mmap Prevent mmap from being used.
--mlock Force the system to keep the model in RAM.
--mul_mat_q Activate new mulmat kernels.
--cache-capacity CACHE_CAPACITY Maximum cache capacity. Examples: 2000MiB, 2GiB. When provided without units, bytes will be assumed.
--tensor_split TENSOR_SPLIT Split the model across multiple GPUs, comma-separated list of proportions, e.g. 18,17
--llama_cpp_seed SEED Seed for llama-cpp models. Default 0 (random).
--cpu Use the CPU version of llama-cpp-python instead of the GPU-accelerated version.
--cfg-cache llamacpp_HF: Create an additional cache for CFG negative prompts.


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.


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.


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.
--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.


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.


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.

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.


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.


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-blocking-port BLOCKING_PORT The listening port for the blocking API.
--api-streaming-port STREAMING_PORT The listening port for the streaming API.


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


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.


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



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.