**Vote on the preset arena!**

* Arena: https://oobabooga.github.io/arena/index.html
* [Preliminary results](https://oobabooga.github.io/arena/preliminary-results.html)
* [Presets](https://oobabooga.github.io/arena/presets.html)
* +Info: https://www.reddit.com/r/LocalLLaMA/comments/14adfw2/preset_arena_17205_comparisons_between_241/

# 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](https://github.com/AUTOMATIC1111/stable-diffusion-webui) of text generation.

|![Image1](https://github.com/oobabooga/screenshots/raw/main/qa.png) | ![Image2](https://github.com/oobabooga/screenshots/raw/main/cai3.png) |
|:---:|:---:|
|![Image3](https://github.com/oobabooga/screenshots/raw/main/gpt4chan.png) | ![Image4](https://github.com/oobabooga/screenshots/raw/main/galactica.png) |

## Features

* 3 interface modes: default, notebook, and chat
* Multiple model backends: tranformers, llama.cpp, AutoGPTQ, GPTQ-for-LLaMa, RWKV, FlexGen
* Dropdown menu for quickly switching between different models
* LoRA: load and unload LoRAs on the fly, load multiple LoRAs at the same time, train a new LoRA
* Precise instruction templates for chat mode, including Alpaca, Vicuna, Open Assistant, Dolly, Koala, ChatGLM, MOSS, RWKV-Raven, Galactica, StableLM, WizardLM, Baize, Ziya, Chinese-Vicuna, MPT, INCITE, Wizard Mega, KoAlpaca, Vigogne, Bactrian, h2o, and OpenBuddy
* [Multimodal pipelines, including LLaVA and MiniGPT-4](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/multimodal)
* 8-bit and 4-bit inference through bitsandbytes
* CPU mode for transformers models
* [DeepSpeed ZeRO-3 inference](docs/DeepSpeed.md)
* [Extensions](docs/Extensions.md)
* [Custom chat characters](docs/Chat-mode.md)
* Very efficient text streaming
* Markdown output with LaTeX rendering, to use for instance with [GALACTICA](https://github.com/paperswithcode/galai)
* Nice HTML output for GPT-4chan
* API, including endpoints for websocket streaming ([see the examples](https://github.com/oobabooga/text-generation-webui/blob/main/api-examples))

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

## Installation

### One-click installers

| Windows | Linux | macOS | WSL |
|--------|--------|--------|--------|
| [oobabooga-windows.zip](https://github.com/oobabooga/text-generation-webui/releases/download/installers/oobabooga_windows.zip) | [oobabooga-linux.zip](https://github.com/oobabooga/text-generation-webui/releases/download/installers/oobabooga_linux.zip) |[oobabooga-macos.zip](https://github.com/oobabooga/text-generation-webui/releases/download/installers/oobabooga_macos.zip) | [oobabooga-wsl.zip](https://github.com/oobabooga/text-generation-webui/releases/download/installers/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.

* The source codes are here: https://github.com/oobabooga/one-click-installers
* There is no need to run the installers as admin.
* AMD doesn't work on Windows.
* Huge thanks to [@jllllll](https://github.com/jllllll), [@ClayShoaf](https://github.com/ClayShoaf), and [@xNul](https://github.com/xNul) for their contributions to these installers.

### 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:

```
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` |
| Windows | NVIDIA | `pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117` |

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

#### 2.1 Special instructions

* MacOS users: https://github.com/oobabooga/text-generation-webui/pull/393
* AMD users: https://rentry.org/eq3hg

#### 3. Install the web UI

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

#### llama.cpp with GPU acceleration

Requires the additional compilation step described here: [GPU acceleration](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp-models.md#gpu-acceleration).

#### 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: 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](https://github.com/oobabooga/text-generation-webui/blob/main/docs/Docker.md) 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 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](https://huggingface.co/models?pipeline_tag=text-generation&sort=downloads) is the main place to download models. These are some examples:

* [Pythia](https://huggingface.co/models?sort=downloads&search=eleutherai%2Fpythia+deduped)
* [OPT](https://huggingface.co/models?search=facebook/opt)
* [GALACTICA](https://huggingface.co/models?search=facebook/galactica)
* [GPT-J 6B](https://huggingface.co/EleutherAI/gpt-j-6B/tree/main)

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

To download a protected model, set env vars `HF_USER` and `HF_PASS` to your Hugging Face username and password (or [User Access Token](https://huggingface.co/settings/tokens)). The model's terms must first be accepted on the HF website.

#### 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

<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.
</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:

#### 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. |

#### Model loader

| Flag                                       | Description |
|--------------------------------------------|-------------|
| `--loader LOADER`                          | Choose the model loader manually, otherwise, it will get autodetected. Valid options: autogptq, gptq-for-llama, transformers, llamacpp, rwkv, flexgen |

#### Accelerate/transformers

| 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. |

#### llama.cpp

| 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). |

#### AutoGPTQ

| 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. |
| `--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. |

#### GPTQ-for-LLaMa

| 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. |

#### FlexGen

| Flag             | Description |
|------------------|-------------|
| `--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%). |

#### DeepSpeed

| 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. |

#### RWKV

| 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. |

#### Gradio

| 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" |

#### API

| Flag                                  | Description |
|---------------------------------------|-------------|
| `--api`                               | Enable the API extension. |
| `--public-api`                        | Create a public URL for the API using Cloudfare. |
| `--api-blocking-port BLOCKING_PORT`   | The listening port for the blocking API. |
| `--api-streaming-port STREAMING_PORT` | The listening port for the streaming API. |

#### Multimodal

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

Out of memory errors? [Check the low VRAM guide](docs/Low-VRAM-guide.md).

## Presets

Inference settings presets can be created under `presets/` as yaml 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: [tSNE visualization](https://user-images.githubusercontent.com/112222186/228956352-1addbdb9-2456-465a-b51d-089f462cd385.png).

## Contributing

* Pull requests, suggestions, and issue reports are welcome. 
* Make sure to carefully [search](https://github.com/oobabooga/text-generation-webui/issues) existing issues before starting a new one.
* If you have some experience with git, testing an open pull request and leaving a comment on whether it works as expected or not is immensely helpful.
* A simple way to contribute, even if you are not a programmer, is to leave a 👍 on an issue or pull request that you find relevant.

## Credits

- Gradio dropdown menu refresh button, code for reloading the interface: https://github.com/AUTOMATIC1111/stable-diffusion-webui
- NovelAI and KoboldAI presets: https://github.com/KoboldAI/KoboldAI-Client/wiki/Settings-Presets
- Code for early stopping in chat mode, code for some of the sliders: https://github.com/PygmalionAI/gradio-ui/