A Gradio web UI for Large Language Models.
Its goal is to become the AUTOMATIC1111/stable-diffusion-webui of text generation.
llamacpp_HF
loader), CPU inference in 32-bit precision using PyTorch.
start_linux.sh
,
start_windows.bat
,
start_macos.sh
, or
start_wsl.bat
script depending on your OS.
http://localhost:7860/?__theme=dark
.
To restart the web UI in the future, just run the
start_
script again. This script creates an
installer_files
folder where it sets up the project's requirements. In case you need to reinstall the requirements, you can simply delete that folder and start the web UI again.
The script accepts command-line flags. Alternatively, you can edit the
CMD_FLAGS.txt
file with a text editor and add your flags there.
To get updates in the future, run
update_wizard_linux.sh
,
update_wizard_windows.bat
,
update_wizard_macos.sh
, or
update_wizard_wsl.bat
.
The script uses Miniconda to set up a Conda environment in the
installer_files
folder.
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
.
start_
,
update_wizard_
, or
cmd_
) as admin/root.
extensions_reqs
script for your OS. At the end, this script will install the main requirements for the project to make sure that they take precedence in case of version conflicts.
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=TRUE ./start_linux.sh
.
Recommended if you have some experience with the command-line.
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
conda create -n textgen python=3.11
conda activate textgen
System
Command
Linux/WSL
NVIDIA
pip3 install torch==2.2.1 torchvision==0.17.1 torchaudio==2.2.1 --index-url https://download.pytorch.org/whl/cu121
Linux/WSL
CPU only
pip3 install torch==2.2.1 torchvision==0.17.1 torchaudio==2.2.1 --index-url https://download.pytorch.org/whl/cpu
Linux
pip3 install torch==2.2.1 torchvision==0.17.1 torchaudio==2.2.1 --index-url https://download.pytorch.org/whl/rocm5.6
MacOS + MPS
pip3 install torch==2.2.1 torchvision==0.17.1 torchaudio==2.2.1
Windows
NVIDIA
pip3 install torch==2.2.1 torchvision==0.17.1 torchaudio==2.2.1 --index-url https://download.pytorch.org/whl/cu121
Windows
CPU only
pip3 install torch==2.2.1 torchvision==0.17.1 torchaudio==2.2.1
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
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:
requirements file to use
Manually install llama-cpp-python using the appropriate command for your hardware: Installation from PyPI.
Use the LLAMA_HIPBLAS=on toggle.
Note the Windows remarks.
Manually install AutoGPTQ: Installation.
Perform the from-source installation - there are no prebuilt ROCm packages for Windows.
For Kepler GPUs and older, you will need to install CUDA 11.8 instead of 12:
pip3 install torch==2.2.1 torchvision==0.17.1 torchaudio==2.2.1 --index-url https://download.pytorch.org/whl/cu118
conda install -y -c "nvidia/label/cuda-11.8.0" cuda-runtime
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
The requirements*.txt above contain various wheels precompiled through GitHub Actions. 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.
For NVIDIA GPU:
ln -s docker/{nvidia/Dockerfile,nvidia/docker-compose.yml,.dockerignore} .
For AMD GPU:
ln -s docker/{amd/Dockerfile,intel/docker-compose.yml,.dockerignore} .
For Intel GPU:
ln -s docker/{intel/Dockerfile,amd/docker-compose.yml,.dockerignore} .
For CPU only
ln -s docker/{cpu/Dockerfile,cpu/docker-compose.yml,.dockerignore} .
cp docker/.env.example .env
#Create logs/cache dir :
mkdir -p logs cache
# 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
# Edit CMD_FLAGS.txt and add in it the options you want to execute (like --listen --cpu)
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.
From time to time, the requirements*.txt change. To update, use these commands:
conda activate textgen
cd text-generation-webui
pip install -r <requirements file that you have used> --upgrade
--multi-user
Multi-user mode. Chat histories are not saved or automatically loaded. WARNING: this is likely not safe for sharing publicly.
--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 the chat tab instead of a hover menu.
Description
--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 VRAM usage slightly, but it comes at a performance cost.
--trust-remote-code
Set trust_remote_code=True while loading the model. Necessary for some models.
--no_use_fast
Set use_fast=False while loading the tokenizer (it's True by default). Use this if you have any problems related to use_fast.
--use_flash_attention_2
Set use_flash_attention_2=True while loading the model.
⚠️ Requires minimum compute of 7.0 on Windows at the moment.
Description
--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.
Description
--tensor_split TENSOR_SPLIT
Split the model across multiple GPUs. Comma-separated list of proportions. Example: 18,17.
--numa
Activate NUMA task allocation for llama.cpp.
--logits_all
Needs to be set for perplexity evaluation to work. Otherwise, ignore it, as it makes prompt processing slower.
--no_offload_kqv
Do not offload the K, Q, V to the GPU. This saves VRAM but reduces the performance.
--cache-capacity CACHE_CAPACITY
Maximum cache capacity (llama-cpp-python). Examples: 2000MiB, 2GiB. When provided without units, bytes will be assumed.
--row_split
Split the model by rows across GPUs. This may improve multi-gpu performance.
--streaming-llm
Activate StreamingLLM to avoid re-evaluating the entire prompt when old messages are removed.
--attention-sink-size ATTENTION_SINK_SIZE
StreamingLLM: number of sink tokens. Only used if the trimmed prompt doesn't share a prefix with the old prompt.
Description
--gpu-split
Comma-separated list of VRAM (in GB) to use per GPU device for model layers. Example: 20,7,7.
--max_seq_len MAX_SEQ_LEN
Maximum sequence length.
--cfg-cache
ExLlamav2_HF: Create an additional cache for CFG negative prompts. Necessary to use CFG with that loader.
--no_flash_attn
Force flash-attention to not be used.
--cache_8bit
Use 8-bit cache to save VRAM.
--cache_4bit
Use Q4 cache to save VRAM.
--num_experts_per_token NUM_EXPERTS_PER_TOKEN
Number of experts to use for generation. Applies to MoE models like Mixtral.
Description
--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.
--disable_exllamav2
Disable ExLlamav2 kernel.
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.
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.
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.
Description
--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 password in the format "username:password". Multiple credentials can also be supplied with "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 the same format as above.
--ssl-keyfile SSL_KEYFILE
The path to the SSL certificate key file.
--ssl-certfile SSL_CERTFILE
The path to the SSL certificate cert file.
Description
--public-api-id PUBLIC_API_ID
Tunnel ID for named Cloudflare Tunnel. Use together with public-api option.
--api-port API_PORT
The listening port for the API.
--api-key API_KEY
API authentication key.
--admin-key ADMIN_KEY
API authentication key for admin tasks like loading and unloading models. If not set, will be the same as --api-key.
--nowebui
Do not launch the Gradio UI. Useful for launching the API in standalone mode.
Description
https://github.com/oobabooga/text-generation-webui/wiki
Models should be placed in the folder text-generation-webui/models. They are usually downloaded from Hugging Face.
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
The remaining model types (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 it via the command-line with
python download-model.py organization/model
Run python download-model.py --help to see all the options.
If you would like to contribute to the project, check out the Contributing guidelines.
Subreddit: https://www.reddit.com/r/oobabooga/
Discord: https://discord.gg/jwZCF2dPQN
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.