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.

Features

• 3 interface modes: default (two columns), notebook, and chat
• Multiple model backends: transformers, llama.cpp, ExLlama, AutoGPTQ, GPTQ-for-LLaMa, ctransformers
• 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
• Extensions framework
• Custom chat characters
• Very efficient text streaming
• Markdown output with LaTeX rendering, to use for instance with GALACTICA
• API, including endpoints for websocket streaming (see the 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	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.

• The source codes and more information can be found here: https://github.com/oobabooga/one-click-installers
• There is no need to run the installers as admin.
• Huge thanks to @jllllll, @ClayShoaf, and @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 (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/.

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 on AMD, Metal, and some specific CPUs

Precompiled wheels are included for CPU-only and NVIDIA GPUs (cuBLAS). For AMD, Metal, and some specific CPUs, you need to uninstall those wheels and compile llama-cpp-python yourself.

To uninstall:

pip uninstall -y llama-cpp-python llama-cpp-python-cuda

To compile: https://github.com/abetlen/llama-cpp-python#installation-with-openblas--cublas--clblast--metal

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:

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 the "Model" tab of the UI, those models can be automatically downloaded from Hugging Face. You can also download them via the command-line with python download-model.py organization/model.

• GGML models are a single file and should be placed directly into models. Example:

text-generation-webui
├── models
│   ├── llama-13b.ggmlv3.q4_K_M.bin

Those models must be downloaded manually, as they are not currently supported by the automated downloader.

GPT-4chan

Instructions

GPT-4chan has been shut down from Hugging Face, so you need to download it elsewhere. You have two options:

• Torrent: 16-bit / 32-bit
• Direct download: 16-bit / 32-bit

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

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.
--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.

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

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.

GGML (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.

llama.cpp

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).
--n_gqa N_GQA	grouped-query attention. Must be 8 for llama-2 70b.
--rms_norm_eps RMS_NORM_EPS	5e-6 is a good value for llama-2 models.
--cpu	Use the CPU version of llama-cpp-python instead of the GPU-accelerated version.

ctransformers

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.

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.
--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.

ExLlama

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.

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.

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.

RoPE (for llama.cpp, ExLlama, 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.
--compress_pos_emb COMPRESS_POS_EMB	Positional embeddings compression factor. Should typically be set to max_seq_len / 2048.

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"
--ssl-keyfile SSL_KEYFILE	The path to the SSL certificate key file.
--ssl-certfile SSL_CERTFILE	The path to the SSL certificate cert file.

API

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.

Multimodal

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

Presets

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.

Contributing

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

Community

• Subreddit: https://www.reddit.com/r/oobabooga/
• Discord: https://discord.gg/jwZCF2dPQN