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@ -10,264 +10,37 @@ Its goal is to become the [AUTOMATIC1111/stable-diffusion-webui](https://github.
## Features
* 3 interface modes: default (two columns), notebook, and chat
* Multiple model backends: [Transformers](https://github.com/huggingface/transformers), [llama.cpp](https://github.com/ggerganov/llama.cpp) (through [llama-cpp-python](https://github.com/abetlen/llama-cpp-python)), [ExLlama](https://github.com/turboderp/exllama), [ExLlamaV2](https://github.com/turboderp/exllamav2), [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ), [AutoAWQ](https://github.com/casper-hansen/AutoAWQ), [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa), [CTransformers](https://github.com/marella/ctransformers), [QuIP#](https://github.com/Cornell-RelaxML/quip-sharp)
* Dropdown menu for quickly switching between different models
* LoRA: load and unload LoRAs on the fly, train a new LoRA using QLoRA
* Precise instruction templates for chat mode, including Llama-2-chat, Alpaca, Vicuna, WizardLM, StableLM, and many others
* 4-bit, 8-bit, and CPU inference through the transformers library
* Use llama.cpp models with transformers samplers (`llamacpp_HF` loader)
* [Multimodal pipelines, including LLaVA and MiniGPT-4](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/multimodal)
* [Extensions framework](https://github.com/oobabooga/text-generation-webui/wiki/07-%E2%80%90-Extensions)
* [Custom chat characters](https://github.com/oobabooga/text-generation-webui/wiki/03-%E2%80%90-Parameters-Tab#character)
* Markdown output with LaTeX rendering, to use for instance with [GALACTICA](https://github.com/paperswithcode/galai)
* 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)
* 3 interface modes: default (two columns), notebook, and chat.
* Multiple model backends: [Transformers](https://github.com/huggingface/transformers), [llama.cpp](https://github.com/ggerganov/llama.cpp) (through [llama-cpp-python](https://github.com/abetlen/llama-cpp-python)), [ExLlama](https://github.com/turboderp/exllama), [ExLlamaV2](https://github.com/turboderp/exllamav2), [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ), [AutoAWQ](https://github.com/casper-hansen/AutoAWQ), [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa), [CTransformers](https://github.com/marella/ctransformers), [QuIP#](https://github.com/Cornell-RelaxML/quip-sharp).
* Dropdown menu for quickly switching between different models.
* Large number of extensions (built-in and user-contributed), including Coqui TTS for voice outputs, Whisper STT for voice inputs, translation, [multimodal pipelines](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/multimodal), vector databases, Stable Diffusion integration, and a lot more. See [the wiki](https://github.com/oobabooga/text-generation-webui/wiki/07-%E2%80%90-Extensions) and [the extensions directory](https://github.com/oobabooga/text-generation-webui-extensions) for details.
* Chat with [custom characters](https://github.com/oobabooga/text-generation-webui/wiki/03-%E2%80%90-Parameters-Tab#character).
* Precise templates for instruction-following models, including Llama-2-chat, Alpaca, Vicuna, Mistral, and many others.
* Easy UI for training LoRAs, as well as loading/unloading them on the fly.
* HF transformers integration: load models in 4-bit or 8-bit quantization through bitsandbytes, use llama.cpp with transformers samplers (`llamacpp_HF` loader), CPU inference in 32-bit precision using PyTorch.
* 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).
## Documentation
To learn how to use the various features, check out the Documentation:
https://github.com/oobabooga/text-generation-webui/wiki
## Installation
### One-click installers
## How to install
1) Clone or [download](https://github.com/oobabooga/text-generation-webui/archive/refs/heads/main.zip) 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) Have fun!
4) Once the installation ends, browse to `http://localhost:7860/?__theme=dark`.
5) Have fun!
#### How it works
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.
To launch the webui in the future after it is already installed, run the same `start` script.
#### Getting updates
Run `update_linux.sh`, `update_windows.bat`, `update_macos.sh`, or `update_wsl.bat`.
#### Running commands
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`.
#### Defining command-line flags
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`.
#### Other info
* There is no need to run any of those scripts as admin/root.
* For additional instructions about AMD setup and WSL setup, consult [the documentation](https://github.com/oobabooga/text-generation-webui/wiki).
* 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`.
* 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`.
### 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](https://educe-ubc.github.io/conda.html)):
```
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 torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121` |
| 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/rocm5.6` |
| MacOS + MPS | Any | `pip3 install torch torchvision torchaudio` |
| Windows | NVIDIA | `pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121` |
| Windows | CPU only | `pip3 install torch torchvision torchaudio` |
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` |
##### 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](https://github.com/abetlen/llama-cpp-python#installation-with-hardware-acceleration).
* Use the `LLAMA_HIPBLAS=on` toggle.
* Note the [Windows remarks](https://github.com/abetlen/llama-cpp-python#windows-remarks).
3) Manually install AutoGPTQ: [Installation](https://github.com/PanQiWei/AutoGPTQ#install-from-source).
* Perform the from-source installation - there are no prebuilt ROCm packages for Windows.
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):
```sh
cd text-generation-webui
git clone https://github.com/turboderp/exllama repositories/exllama
```
##### Older NVIDIA GPUs
1) For Kepler GPUs and older, you will need to install CUDA 11.8 instead of 12:
```
pip3 install torch torchvision torchaudio --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 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.
### Alternative: Docker
```
ln -s docker/{nvidia/Dockerfile,docker-compose.yml,.dockerignore} .
cp docker/.env.example .env
# 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
docker compose up --build
```
* 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.
* For additional docker files, check out [this repository](https://github.com/Atinoda/text-generation-webui-docker).
### 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 file that you've used> --upgrade
```
## Downloading models
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).
* Transformers or 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
```
* 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
```
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
To launch the web UI again in the future, run the same `start_` script that you used to install it.
<details>
<summary>
Instructions
Setup details
</summary>
[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:
* Torrent: [16-bit](https://archive.org/details/gpt4chan_model_float16) / [32-bit](https://archive.org/details/gpt4chan_model)
* Direct download: [16-bit](https://theswissbay.ch/pdf/_notpdf_/gpt4chan_model_float16/) / [32-bit](https://theswissbay.ch/pdf/_notpdf_/gpt4chan_model/)
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](https://huggingface.co/EleutherAI/gpt-j-6B/raw/main/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:
![Image3](https://github.com/oobabooga/screenshots/raw/main/gpt4chan.png)
</details>
## Starting the web UI
conda activate textgen
cd text-generation-webui
python server.py
Then browse to
`http://localhost:7860/?__theme=dark`
Optionally, you can use the following command-line flags:
Command-line flags can be passed to that script. Alternatively, you can place your flags in the `CMD_FLAGS.txt` file.
<details>
<summary>
Command-line flags list
</summary>
#### Basic settings
| Flag | Description |
@ -431,6 +204,246 @@ Optionally, you can use the following command-line flags:
|---------------------------------------|-------------|
| `--multimodal-pipeline PIPELINE` | The multimodal pipeline to use. Examples: `llava-7b`, `llava-13b`. |
</details>
### One-click-installer
#### How it works
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.
To launch the webui in the future after it is already installed, run the same `start` script.
#### Getting updates
Run `update_linux.sh`, `update_windows.bat`, `update_macos.sh`, or `update_wsl.bat`.
#### Running commands
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`.
#### Defining command-line flags
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`.
#### Other info
* There is no need to run any of those scripts as admin/root.
* For additional instructions about AMD setup and WSL setup, consult [the documentation](https://github.com/oobabooga/text-generation-webui/wiki).
* 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`.
* 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`.
### 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](https://educe-ubc.github.io/conda.html)):
```
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 torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121` |
| 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/rocm5.6` |
| MacOS + MPS | Any | `pip3 install torch torchvision torchaudio` |
| Windows | NVIDIA | `pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121` |
| Windows | CPU only | `pip3 install torch torchvision torchaudio` |
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](https://github.com/abetlen/llama-cpp-python#installation-with-hardware-acceleration).
* Use the `LLAMA_HIPBLAS=on` toggle.
* Note the [Windows remarks](https://github.com/abetlen/llama-cpp-python#windows-remarks).
3) Manually install AutoGPTQ: [Installation](https://github.com/PanQiWei/AutoGPTQ#install-from-source).
* Perform the from-source installation - there are no prebuilt ROCm packages for Windows.
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):
```sh
cd text-generation-webui
git clone https://github.com/turboderp/exllama repositories/exllama
```
##### Older NVIDIA GPUs
1) For Kepler GPUs and older, you will need to install CUDA 11.8 instead of 12:
```
pip3 install torch torchvision torchaudio --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 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.
### Alternative: Docker
```
ln -s docker/{nvidia/Dockerfile,docker-compose.yml,.dockerignore} .
cp docker/.env.example .env
# 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
docker compose up --build
```
* 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.
* For additional docker files, check out [this repository](https://github.com/Atinoda/text-generation-webui-docker).
### 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 file that you've used> --upgrade
```
</details>
## Documentation
https://github.com/oobabooga/text-generation-webui/wiki
## Downloading models
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).
* 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
```
* Other models (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 via the command-line with `python download-model.py organization/model` (use `--help` to see all the options).
#### GPT-4chan
<details>
<summary>
Instructions
</summary>
[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:
* Torrent: [16-bit](https://archive.org/details/gpt4chan_model_float16) / [32-bit](https://archive.org/details/gpt4chan_model)
* Direct download: [16-bit](https://theswissbay.ch/pdf/_notpdf_/gpt4chan_model_float16/) / [32-bit](https://theswissbay.ch/pdf/_notpdf_/gpt4chan_model/)
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](https://huggingface.co/EleutherAI/gpt-j-6B/raw/main/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:
![Image3](https://github.com/oobabooga/screenshots/raw/main/gpt4chan.png)
</details>
## Google Colab notebook
https://colab.research.google.com/github/oobabooga/text-generation-webui/blob/main/Colab-TextGen-GPU.ipynb