haotian-liu/text-generation-webui
haotian-liu/text-generation-webui sits at 4 stars on GitHub, written primarily in Python. A gradio web UI for running Large Language Models like LLaMA, llama.cpp, GPT-J, OPT, and GALACTICA.
Snapshot summary built from the project's own GitHub metadata — there's no written TopGit review yet. The page will update automatically when a full review is published.
TopGit writes full reviews for the most-starred, most-requested repositories. This page is a snapshot until then — see the READ ME tab for the original README in full.
Snapshot
Top contributors
Show top contributors
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, ExLlamaV2, AutoGPTQ, GPTQ-for-LLaMa, CTransformers, AutoAWQ
- 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_HFloader) - 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
- Clone or download the repository.
- Run the
start_linux.sh,start_windows.bat,start_macos.sh, orstart_wsl.batscript depending on your OS. - Select your GPU vendor when asked.
- 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, WSL setup, and nvcc installation, consult this page.
- 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,LAUNCH_AFTER_INSTALL, andINSTALL_EXTENSIONSenvironment variables. For instance:GPU_CHOICE=A 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):
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
conda activate textgen
2. Install Pytorch
| System | GPU | Command |
|---|---|---|
| Linux/WSL | NVIDIA | pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118 |
| 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/cu118 |
| 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 may also need to manually install the CUDA runtime libraries:
conda install -y -c "nvidia/label/cuda-11.8.0" cuda-runtime
3. Install the web UI
git clone https://github.com/oobabooga/text-generation-webui
cd text-generation-webui
pip install -r requirements.txt
AMD, Metal, Intel Arc, and CPUs without AVX2
- Replace the last command above with
pip install -r requirements_nowheels.txt
-
Manually install llama-cpp-python using the appropriate command for your hardware: Installation from PyPI.
-
Do the same for CTransformers: Installation.
-
AMD: Manually install AutoGPTQ: Installation.
-
AMD: Manually install ExLlama by simply cloning it into the
repositoriesfolder (it will be automatically compiled at runtime after that):
cd text-generation-webui
git clone https://github.com/turboderp/exllama repositories/exllama
bitsandbytes on older NVIDIA GPUs
bitsandbytes >= 0.39 may not work. In that case, to use --load-in-8bit, you may have to downgrade like this:
- Linux:
pip install bitsandbytes==0.38.1 - Windows:
pip install https://github.com/jllllll/bitsandbytes-windows-webui/raw/main/bitsandbytes-0.38.1-py3-none-any.whl
Alternative: Docker
ln -s docker/{Dockerfile,docker-compose.yml,.dockerignore} .
cp docker/.env.example .env
# Edit .env and set TORCH_CUDA_ARCH_LIST based on your GPU model
docker compose up --build
- You need to have docker compose v2.17 or higher installed. See this guide for instructions.
- For additional docker files, check out this repository.
Updating the requirements
From time to time, the requirements.txt changes. To update, use these commands:
conda activate textgen
cd text-generation-webui
pip install -r requirements.txt --upgrade
Downloading models
Models should be placed in the text-generation-webui/models folder. They are usually downloaded from Hugging Face.
- Transformers or GPTQ models are made of several files and must be placed in a subfolder. Example:
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
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:
- Place the files under
models/gpt4chan_model_float16ormodels/gpt4chan_model. - Place GPT-J 6B's config.json file in that same folder: config.json.
- 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. |
--chat-buttons | Show buttons on chat tab instead of hover menu. |
Model loader
| Flag | Description |
|---|---|
--loader LOADER | Choose the model loader manually, otherwise, it will get autodetected. Valid options: transformers, autogptq, gptq-for-llama, exllama, exllama_hf, llamacpp, rwkv, ctransformers |
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. |
--use_fast | Set use_fast=True while loading a tokenizer. |
Accelerate 4-bit
⚠️ Requires minimum compute of 7.0 on Windows at the moment.
| Flag | Description |
|---|---|
--load-in-4bit | Load the model with 4-bit precision (using bitsandbytes). |
--compute_dtype COMPUTE_DTYPE | compute dtype for 4-bit. Valid options: bfloat16, float16, float32. |
--quant_type QUANT_TYPE | quant_type for 4-bit. Valid options: nf4, fp4. |
--use_double_quant | use_double_quant for 4-bit. |
GGUF (for llama.cpp and ctransformers)
| Flag | Description |
|---|---|
--threads | Number of threads to use. |
--threads-batch THREADS_BATCH | Number of threads to use for batches/prompt processing. |
--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 |
|---|---|
--mul_mat_q | Activate new mulmat kernels. |
--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). |
--cache-capacity CACHE_CAPACITY | Maximum cache capacity. Examples: 2000MiB, 2GiB. When provided without units, bytes will be assumed. |
--cfg-cache | llamacpp_HF: Create an additional cache for CFG negative prompts. |
--no-mmap | Prevent mmap from being used. |
--mlock | Force the system to keep the model in RAM. |
--numa | Activate NUMA task allocation for llama.cpp |
--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. |
--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. |
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, ExLlamaV2, and transformers)
| Flag | Description |
|---|---|
--alpha_value ALPHA_VALUE | Positional embeddings alpha factor for NTK RoPE scaling. Use either this or compress_pos_emb, not both. |
--rope_freq_base ROPE_FREQ_BASE | If greater than 0, will be used instead of alpha_value. Those two are related by rope_freq_base = 10000 * alpha_value ^ (64 / 63). |
--compress_pos_emb COMPRESS_POS_EMB | Positional embeddings compression factor. Should be set to (context length) / (model's original context length). Equal to 1/rope_freq_scale. |
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
Acknowledgment
In August 2023, Andreessen Horowitz (a16z) provided a generous grant to encourage and support my independent work on this project. I am extremely grateful for their trust and recognition, which will allow me to dedicate more time towards realizing the full potential of text-generation-webui.
Related repositories
Master programming by recreating your favorite technologies from scratch.
A curated meta-list of curated lists organized by technology domain. The repository acts as a directory pointing to hundreds of specialized awesome lists covering programming languages, platforms, frameworks, and tooling. All content is community-contributed under the CC0 public domain dedication.
Public APIs is a community-curated GitHub repository listing free, publicly accessible APIs across a wide range of categories, with auth type, HTTPS, and CORS noted for each entry. It's a browsable reference, not a library to install.
freeCodeCamp is a free, self-paced curriculum for learning to code, published as open source at freeCodeCamp/freeCodeCamp. It's a 501(c)(3) nonprofit funded by donor support, structured around six certifications in its Full-Stack Developer Curriculum, each gated by required projects instead of open-book quizzes. The repository also carries beta language certifications for developers, interview-prep resources, and the code that runs the live freecodecamp.org platform.
Quick answers
Does haotian-liu/text-generation-webui have any tags?
TopGit's last sync did not record any GitHub topics for haotian-liu/text-generation-webui. GitHub topics appear in the right sidebar of a repository page; that's the authoritative place to check.
How active is development on haotian-liu/text-generation-webui?
The most recent commit recorded on haotian-liu/text-generation-webui was 2.8 years ago, based on the GitHub push timestamp. The repository has 2 forks — one of the better signals of community interest.
How many stars does haotian-liu/text-generation-webui have?
haotian-liu/text-generation-webui has 4 GitHub stars — refresh the page for the live number, or check github.com/haotian-liu/text-generation-webui. TopGit mirrors GitHub's count but does not claim minute-by-minute accuracy.
What language is haotian-liu/text-generation-webui written in?
haotian-liu/text-generation-webui is written primarily in Python. GitHub's language field is based on the largest share of bytes in the default branch.
Where do I read more about haotian-liu/text-generation-webui?
This TopGit page is a snapshot — the READ ME tab shows the project's own README content (links stripped, images preserved). The GitHub repository at github.com/haotian-liu/text-generation-webui is the definitive source.
Read full README in the tab above.
Still deciding about text-generation-webui?
One click hands the question to an AI along with this page — see what it says about text-generation-webui.