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--- |
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title: My Chat |
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app_file: server.py |
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sdk: gradio |
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sdk_version: 3.50.2 |
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emoji: 🚀 |
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colorFrom: red |
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colorTo: green |
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pinned: true |
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--- |
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# Text generation web UI |
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A Gradio web UI for Large Language Models. |
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Its goal is to become the [AUTOMATIC1111/stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui) of text generation. |
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|:---:|:---:| |
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## Features |
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* 3 interface modes: default (two columns), notebook, and chat |
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* Multiple model backends: [transformers](https://github.com/huggingface/transformers), [llama.cpp](https://github.com/ggerganov/llama.cpp), [ExLlama](https://github.com/turboderp/exllama), [ExLlamaV2](https://github.com/turboderp/exllamav2), [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ), [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa), [CTransformers](https://github.com/marella/ctransformers), [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) |
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* Dropdown menu for quickly switching between different models |
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* LoRA: load and unload LoRAs on the fly, train a new LoRA using QLoRA |
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* Precise instruction templates for chat mode, including Llama-2-chat, Alpaca, Vicuna, WizardLM, StableLM, and many others |
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* 4-bit, 8-bit, and CPU inference through the transformers library |
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* Use llama.cpp models with transformers samplers (`llamacpp_HF` loader) |
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* [Multimodal pipelines, including LLaVA and MiniGPT-4](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/multimodal) |
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* [Extensions framework](https://github.com/oobabooga/text-generation-webui/wiki/07-%E2%80%90-Extensions) |
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* [Custom chat characters](https://github.com/oobabooga/text-generation-webui/wiki/03-%E2%80%90-Parameters-Tab#character) |
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* Markdown output with LaTeX rendering, to use for instance with [GALACTICA](https://github.com/paperswithcode/galai) |
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* OpenAI-compatible API server with Chat and Completions endpoints -- see the [examples](https://github.com/oobabooga/text-generation-webui/wiki/12-%E2%80%90-OpenAI-API#examples) |
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## Documentation |
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To learn how to use the various features, check out the Documentation: |
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https://github.com/oobabooga/text-generation-webui/wiki |
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## Installation |
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### One-click installers |
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1) Clone or [download](https://github.com/oobabooga/text-generation-webui/archive/refs/heads/main.zip) the repository. |
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2) Run the `start_linux.sh`, `start_windows.bat`, `start_macos.sh`, or `start_wsl.bat` script depending on your OS. |
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3) Select your GPU vendor when asked. |
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4) Have fun! |
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#### How it works |
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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. |
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To launch the webui in the future after it is already installed, run the same `start` script. |
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#### Getting updates |
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Run `update_linux.sh`, `update_windows.bat`, `update_macos.sh`, or `update_wsl.bat`. |
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#### Running commands |
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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`. |
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#### Defining command-line flags |
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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`. |
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#### Other info |
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* There is no need to run any of those scripts as admin/root. |
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* For additional instructions about AMD setup, WSL setup, and nvcc installation, consult [the documentation](https://github.com/oobabooga/text-generation-webui/wiki). |
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* 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`. |
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* 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`. |
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### Manual installation using Conda |
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Recommended if you have some experience with the command-line. |
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#### 0. Install Conda |
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https://docs.conda.io/en/latest/miniconda.html |
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On Linux or WSL, it can be automatically installed with these two commands ([source](https://educe-ubc.github.io/conda.html)): |
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``` |
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curl -sL "https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh" > "Miniconda3.sh" |
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bash Miniconda3.sh |
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``` |
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#### 1. Create a new conda environment |
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``` |
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conda create -n textgen python=3.11 |
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conda activate textgen |
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``` |
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#### 2. Install Pytorch |
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| System | GPU | Command | |
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|--------|---------|---------| |
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| Linux/WSL | NVIDIA | `pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121` | |
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| Linux/WSL | CPU only | `pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu` | |
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| Linux | AMD | `pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm5.6` | |
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| MacOS + MPS | Any | `pip3 install torch torchvision torchaudio` | |
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| Windows | NVIDIA | `pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121` | |
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| Windows | CPU only | `pip3 install torch torchvision torchaudio` | |
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The up-to-date commands can be found here: https://pytorch.org/get-started/locally/. |
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For NVIDIA, you may also need to manually install the CUDA runtime libraries: |
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``` |
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conda install -y -c "nvidia/label/cuda-12.1.0" cuda-runtime |
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``` |
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#### 3. Install the web UI |
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``` |
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git clone https://github.com/oobabooga/text-generation-webui |
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cd text-generation-webui |
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pip install -r <requirements file according to table below> |
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``` |
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Requirements file to use: |
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| GPU | CPU | requirements file to use | |
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|--------|---------|---------| |
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| NVIDIA | has AVX2 | `requirements.txt` | |
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| NVIDIA | no AVX2 | `requirements_noavx2.txt` | |
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| AMD | has AVX2 | `requirements_amd.txt` | |
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| AMD | no AVX2 | `requirements_amd_noavx2.txt` | |
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| CPU only | has AVX2 | `requirements_cpu_only.txt` | |
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| CPU only | no AVX2 | `requirements_cpu_only_noavx2.txt` | |
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| Apple | Intel | `requirements_apple_intel.txt` | |
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| Apple | Apple Silicon | `requirements_apple_silicon.txt` | |
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##### AMD GPU on Windows |
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1) Use `requirements_cpu_only.txt` or `requirements_cpu_only_noavx2.txt` in the command above. |
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2) Manually install llama-cpp-python using the appropriate command for your hardware: [Installation from PyPI](https://github.com/abetlen/llama-cpp-python#installation-with-hardware-acceleration). |
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* Use the `LLAMA_HIPBLAS=on` toggle. |
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* Note the [Windows remarks](https://github.com/abetlen/llama-cpp-python#windows-remarks). |
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3) Manually install AutoGPTQ: [Installation](https://github.com/PanQiWei/AutoGPTQ#install-from-source). |
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* Perform the from-source installation - there are no prebuilt ROCm packages for Windows. |
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4) Manually install [ExLlama](https://github.com/turboderp/exllama) by simply cloning it into the `repositories` folder (it will be automatically compiled at runtime after that): |
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```sh |
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cd text-generation-webui |
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git clone https://github.com/turboderp/exllama repositories/exllama |
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``` |
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##### Older NVIDIA GPUs |
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1) For Kepler GPUs and older, you will need to install CUDA 11.8 instead of 12: |
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``` |
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pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118 |
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conda install -y -c "nvidia/label/cuda-11.8.0" cuda-runtime |
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``` |
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2) bitsandbytes >= 0.39 may not work. In that case, to use `--load-in-8bit`, you may have to downgrade like this: |
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* Linux: `pip install bitsandbytes==0.38.1` |
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* Windows: `pip install https://github.com/jllllll/bitsandbytes-windows-webui/raw/main/bitsandbytes-0.38.1-py3-none-any.whl` |
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##### Manual install |
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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. |
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### Alternative: Docker |
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``` |
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ln -s docker/{Dockerfile,docker-compose.yml,.dockerignore} . |
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cp docker/.env.example .env |
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# Edit .env and set TORCH_CUDA_ARCH_LIST based on your GPU model |
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docker compose up --build |
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``` |
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* You need to have docker compose v2.17 or higher installed. See [this guide](https://github.com/oobabooga/text-generation-webui/wiki/09-%E2%80%90-Docker) for instructions. |
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* For additional docker files, check out [this repository](https://github.com/Atinoda/text-generation-webui-docker). |
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### Updating the requirements |
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From time to time, the `requirements*.txt` changes. To update, use these commands: |
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``` |
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conda activate textgen |
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cd text-generation-webui |
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pip install -r <requirements file that you've used> --upgrade |
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``` |
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## Downloading models |
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Models should be placed in the `text-generation-webui/models` folder. They are usually downloaded from [Hugging Face](https://huggingface.co/models?pipeline_tag=text-generation&sort=downloads). |
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* Transformers or GPTQ models are made of several files and must be placed in a subfolder. Example: |
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``` |
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text-generation-webui |
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├── models |
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│ ├── lmsys_vicuna-33b-v1.3 |
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│ │ ├── config.json |
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│ │ ├── generation_config.json |
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│ │ ├── pytorch_model-00001-of-00007.bin |
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│ │ ├── pytorch_model-00002-of-00007.bin |
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│ │ ├── pytorch_model-00003-of-00007.bin |
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│ │ ├── pytorch_model-00004-of-00007.bin |
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│ │ ├── pytorch_model-00005-of-00007.bin |
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│ │ ├── pytorch_model-00006-of-00007.bin |
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│ │ ├── pytorch_model-00007-of-00007.bin |
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│ │ ├── pytorch_model.bin.index.json |
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│ │ ├── special_tokens_map.json |
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│ │ ├── tokenizer_config.json |
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│ │ └── tokenizer.model |
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``` |
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* GGUF models are a single file and should be placed directly into `models`. Example: |
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``` |
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text-generation-webui |
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├── models |
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│ ├── llama-2-13b-chat.Q4_K_M.gguf |
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``` |
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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). |
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#### GPT-4chan |
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<details> |
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<summary> |
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Instructions |
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</summary> |
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[GPT-4chan](https://huggingface.co/ykilcher/gpt-4chan) has been shut down from Hugging Face, so you need to download it elsewhere. You have two options: |
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* Torrent: [16-bit](https://archive.org/details/gpt4chan_model_float16) / [32-bit](https://archive.org/details/gpt4chan_model) |
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* Direct download: [16-bit](https://theswissbay.ch/pdf/_notpdf_/gpt4chan_model_float16/) / [32-bit](https://theswissbay.ch/pdf/_notpdf_/gpt4chan_model/) |
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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. |
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After downloading the model, follow these steps: |
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1. Place the files under `models/gpt4chan_model_float16` or `models/gpt4chan_model`. |
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2. Place GPT-J 6B's config.json file in that same folder: [config.json](https://huggingface.co/EleutherAI/gpt-j-6B/raw/main/config.json). |
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3. Download GPT-J 6B's tokenizer files (they will be automatically detected when you attempt to load GPT-4chan): |
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``` |
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python download-model.py EleutherAI/gpt-j-6B --text-only |
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``` |
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When you load this model in default or notebook modes, the "HTML" tab will show the generated text in 4chan format: |
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</details> |
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## Starting the web UI |
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conda activate textgen |
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cd text-generation-webui |
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python server.py |
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Then browse to |
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`http://localhost:7860/?__theme=dark` |
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Optionally, you can use the following command-line flags: |
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#### Basic settings |
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| Flag | Description | |
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| `-h`, `--help` | show this help message and exit | |
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| `--multi-user` | Multi-user mode. Chat histories are not saved or automatically loaded. WARNING: this is likely not safe for sharing publicly. | |
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| `--character CHARACTER` | The name of the character to load in chat mode by default. | |
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| `--model MODEL` | Name of the model to load by default. | |
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| `--lora LORA [LORA ...]` | The list of LoRAs to load. If you want to load more than one LoRA, write the names separated by spaces. | |
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| `--model-dir MODEL_DIR` | Path to directory with all the models. | |
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| `--lora-dir LORA_DIR` | Path to directory with all the loras. | |
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| `--model-menu` | Show a model menu in the terminal when the web UI is first launched. | |
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| `--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. | |
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| `--extensions EXTENSIONS [EXTENSIONS ...]` | The list of extensions to load. If you want to load more than one extension, write the names separated by spaces. | |
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| `--verbose` | Print the prompts to the terminal. | |
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| `--chat-buttons` | Show buttons on the chat tab instead of a hover menu. | |
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#### Model loader |
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| Flag | Description | |
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|--------------------------------------------|-------------| |
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| `--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. | |
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#### Accelerate/transformers |
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| Flag | Description | |
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|---------------------------------------------|-------------| |
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| `--cpu` | Use the CPU to generate text. Warning: Training on CPU is extremely slow. | |
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| `--auto-devices` | Automatically split the model across the available GPU(s) and CPU. | |
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| `--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. | |
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| `--cpu-memory CPU_MEMORY` | Maximum CPU memory in GiB to allocate for offloaded weights. Same as above. | |
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| `--disk` | If the model is too large for your GPU(s) and CPU combined, send the remaining layers to the disk. | |
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| `--disk-cache-dir DISK_CACHE_DIR` | Directory to save the disk cache to. Defaults to "cache". | |
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| `--load-in-8bit` | Load the model with 8-bit precision (using bitsandbytes). | |
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| `--bf16` | Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU. | |
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| `--no-cache` | Set `use_cache` to `False` while generating text. This reduces VRAM usage slightly, but it comes at a performance cost. | |
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| `--xformers` | Use xformer's memory efficient attention. This is really old and probably doesn't do anything. | |
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| `--sdp-attention` | Use PyTorch 2.0's SDP attention. Same as above. | |
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| `--trust-remote-code` | Set `trust_remote_code=True` while loading the model. Necessary for some models. | |
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| `--use_fast` | Set `use_fast=True` while loading the tokenizer. | |
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| `--use_flash_attention_2` | Set use_flash_attention_2=True while loading the model. | |
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#### Accelerate 4-bit |
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⚠️ Requires minimum compute of 7.0 on Windows at the moment. |
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| Flag | Description | |
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|---------------------------------------------|-------------| |
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| `--load-in-4bit` | Load the model with 4-bit precision (using bitsandbytes). | |
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| `--use_double_quant` | use_double_quant for 4-bit. | |
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| `--compute_dtype COMPUTE_DTYPE` | compute dtype for 4-bit. Valid options: bfloat16, float16, float32. | |
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| `--quant_type QUANT_TYPE` | quant_type for 4-bit. Valid options: nf4, fp4. | |
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#### llama.cpp |
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| Flag | Description | |
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|-------------|-------------| |
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| `--n_ctx N_CTX` | Size of the prompt context. | |
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| `--threads` | Number of threads to use. | |
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| `--threads-batch THREADS_BATCH` | Number of threads to use for batches/prompt processing. | |
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| `--no_mul_mat_q` | Disable the mulmat kernels. | |
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| `--n_batch` | Maximum number of prompt tokens to batch together when calling llama_eval. | |
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| `--no-mmap` | Prevent mmap from being used. | |
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| `--mlock` | Force the system to keep the model in RAM. | |
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| `--n-gpu-layers N_GPU_LAYERS` | Number of layers to offload to the GPU. | |
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| `--tensor_split TENSOR_SPLIT` | Split the model across multiple GPUs. Comma-separated list of proportions. Example: 18,17. | |
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| `--llama_cpp_seed SEED` | Seed for llama-cpp models. Default is 0 (random). | |
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| `--numa` | Activate NUMA task allocation for llama.cpp. | |
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| `--logits_all`| Needs to be set for perplexity evaluation to work. Otherwise, ignore it, as it makes prompt processing slower. | |
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| `--cache-capacity CACHE_CAPACITY` | Maximum cache capacity (llama-cpp-python). Examples: 2000MiB, 2GiB. When provided without units, bytes will be assumed. | |
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#### ExLlama |
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| Flag | Description | |
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|------------------|-------------| |
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|`--gpu-split` | Comma-separated list of VRAM (in GB) to use per GPU device for model layers. Example: 20,7,7. | |
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|`--max_seq_len MAX_SEQ_LEN` | Maximum sequence length. | |
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|`--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. | |
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|`--no_flash_attn` | Force flash-attention to not be used. | |
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|`--cache_8bit` | Use 8-bit cache to save VRAM. | |
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#### AutoGPTQ |
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| Flag | Description | |
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|------------------|-------------| |
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| `--triton` | Use triton. | |
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| `--no_inject_fused_attention` | Disable the use of fused attention, which will use less VRAM at the cost of slower inference. | |
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| `--no_inject_fused_mlp` | Triton mode only: disable the use of fused MLP, which will use less VRAM at the cost of slower inference. | |
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| `--no_use_cuda_fp16` | This can make models faster on some systems. | |
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| `--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. | |
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| `--disable_exllama` | Disable ExLlama kernel, which can improve inference speed on some systems. | |
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#### GPTQ-for-LLaMa |
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| Flag | Description | |
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|---------------------------|-------------| |
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| `--wbits WBITS` | Load a pre-quantized model with specified precision in bits. 2, 3, 4 and 8 are supported. | |
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| `--model_type MODEL_TYPE` | Model type of pre-quantized model. Currently LLaMA, OPT, and GPT-J are supported. | |
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| `--groupsize GROUPSIZE` | Group size. | |
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| `--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`. | |
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| `--checkpoint CHECKPOINT` | The path to the quantized checkpoint file. If not specified, it will be automatically detected. | |
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| `--monkey-patch` | Apply the monkey patch for using LoRAs with quantized models. | |
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#### ctransformers |
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| Flag | Description | |
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|-------------|-------------| |
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| `--model_type MODEL_TYPE` | Model type of pre-quantized model. Currently gpt2, gptj, gptneox, falcon, llama, mpt, starcoder (gptbigcode), dollyv2, and replit are supported. | |
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#### DeepSpeed |
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| Flag | Description | |
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|---------------------------------------|-------------| |
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| `--deepspeed` | Enable the use of DeepSpeed ZeRO-3 for inference via the Transformers integration. | |
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| `--nvme-offload-dir NVME_OFFLOAD_DIR` | DeepSpeed: Directory to use for ZeRO-3 NVME offloading. | |
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| `--local_rank LOCAL_RANK` | DeepSpeed: Optional argument for distributed setups. | |
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#### RWKV |
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| Flag | Description | |
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|---------------------------------|-------------| |
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| `--rwkv-strategy RWKV_STRATEGY` | RWKV: The strategy to use while loading the model. Examples: "cpu fp32", "cuda fp16", "cuda fp16i8". | |
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| `--rwkv-cuda-on` | RWKV: Compile the CUDA kernel for better performance. | |
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#### RoPE (for llama.cpp, ExLlama, ExLlamaV2, and transformers) |
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|
| 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)`. | |
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| `--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`. | |
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#### Gradio |
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|
|
| Flag | Description | |
|
|---------------------------------------|-------------| |
|
| `--listen` | Make the web UI reachable from your local network. | |
|
| `--listen-port LISTEN_PORT` | The listening port that the server will use. | |
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| `--listen-host LISTEN_HOST` | The hostname that the server will use. | |
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| `--share` | Create a public URL. This is useful for running the web UI on Google Colab or similar. | |
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| `--auto-launch` | Open the web UI in the default browser upon launch. | |
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| `--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". | |
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| `--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. | |
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| `--ssl-keyfile SSL_KEYFILE` | The path to the SSL certificate key file. | |
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| `--ssl-certfile SSL_CERTFILE` | The path to the SSL certificate cert file. | |
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#### API |
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|
|
| 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. | |
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| `--api-port API_PORT` | The listening port for the API. | |
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| `--api-key API_KEY` | API authentication key. | |
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#### Multimodal |
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|
|
| Flag | Description | |
|
|---------------------------------------|-------------| |
|
| `--multimodal-pipeline PIPELINE` | The multimodal pipeline to use. Examples: `llava-7b`, `llava-13b`. | |
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## Google Colab notebook |
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https://colab.research.google.com/github/oobabooga/text-generation-webui/blob/main/Colab-TextGen-GPU.ipynb |
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## Contributing |
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If you would like to contribute to the project, check out the [Contributing guidelines](https://github.com/oobabooga/text-generation-webui/wiki/Contributing-guidelines). |
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## Community |
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* Subreddit: https://www.reddit.com/r/oobabooga/ |
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* Discord: https://discord.gg/jwZCF2dPQN |
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## Acknowledgment |
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|
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In August 2023, [Andreessen Horowitz](https://a16z.com/) (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. |
