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 of text generation.
 |  |
|---|---|
 |  |
| Windows | Linux | macOS |
|---|---|---|
| oobabooga-windows.zip | oobabooga-linux.zip | oobabooga-macos.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.
Recommended if you have some experience with the command line.
On Windows, I additionally recommend carrying out the installation on WSL instead of the base system: WSL installation guide.
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
conda create -n textgen python=3.10.9
conda activate textgen
| 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 |
The up-to-date commands can be found here: https://pytorch.org/get-started/locally/.
git clone https://github.com/oobabooga/text-generation-webui
cd text-generation-webui
pip install -r requirements.txt
The base installation covers transformers models (AutoModelForCausalLM and AutoModelForSeq2SeqLM specifically) and llama.cpp (GGML) models.
To use 4-bit GPU models, the additional installation steps below are necessary:
As an alternative to the recommended WSL method, you can install the web UI natively on Windows using this guide. It will be a lot harder and the performance may be slower: Windows installation guide.
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 in your system. To see how to install docker compose itself, see the guide in here.
Contributed by @loeken in #633
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
Models should be placed inside the models/ folder.
Hugging Face is the main place to download models. These are some examples:
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
If you want to download a model manually, note that all you need are the json, txt, and pytorch*.bin (or model*.safetensors) files. The remaining files are not necessary.
You can drop these directly into the models/ folder, making sure that the file name contains ggml somewhere and ends in .bin.
GPT-4chan has been shut down from Hugging Face, so you need to download it elsewhere. You have two options:
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:
models/gpt4chan_model_float16 or models/gpt4chan_model.python download-model.py EleutherAI/gpt-j-6B --text-only
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:
| 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 json file. See settings-template.json for an example. If you create a file called settings.json, 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. |
| 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 ...] | Maxmimum 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. |
--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. |
| 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. |
| 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 | The number of layers to allocate to the GPU. Setting this parameter enables CPU offloading for 4-bit models. |
--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. |
| Flag | Description |
|---|---|
--flexgen | Enable the use of FlexGen offloading. |
--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%). |
| 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. |
| 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. |
| 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-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" |
| Flag | Description |
|---|---|
--api | Enable the API extension. |
--public-api | Create a public URL for the API using Cloudfare. |
| Flag | Description |
|---|---|
--multimodal-pipeline PIPELINE | The multimodal pipeline to use. Examples: llava-7b, llava-13b. |
Out of memory errors? Check the low VRAM guide.
Inference settings presets can be created under presets/ as text files. These files are detected automatically at startup.
By default, 10 presets by NovelAI and KoboldAI 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.
Make sure to check out the documentation for an in-depth guide on how to use the web UI.
https://github.com/oobabooga/text-generation-webui/tree/main/docs
Pull requests, suggestions, and issue reports are welcome.
You are also welcome to review open pull requests.
Before reporting a bug, make sure that you have:
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 of text generation.
| |
|---|---|
| |
| Windows | Linux | macOS |
|---|---|---|
| oobabooga-windows.zip | oobabooga-linux.zip | oobabooga-macos.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.
Recommended if you have some experience with the command line.
On Windows, I additionally recommend carrying out the installation on WSL instead of the base system: WSL installation guide.
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
conda create -n textgen python=3.10.9
conda activate textgen
| 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 |
The up-to-date commands can be found here: https://pytorch.org/get-started/locally/.
git clone https://github.com/oobabooga/text-generation-webui
cd text-generation-webui
pip install -r requirements.txt
The base installation covers transformers models (AutoModelForCausalLM and AutoModelForSeq2SeqLM specifically) and llama.cpp (GGML) models.
To use 4-bit GPU models, the additional installation steps below are necessary:
As an alternative to the recommended WSL method, you can install the web UI natively on Windows using this guide. It will be a lot harder and the performance may be slower: Windows installation guide.
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 in your system. To see how to install docker compose itself, see the guide in here.
Contributed by @loeken in #633
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
Models should be placed inside the models/ folder.
Hugging Face is the main place to download models. These are some examples:
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
If you want to download a model manually, note that all you need are the json, txt, and pytorch*.bin (or model*.safetensors) files. The remaining files are not necessary.
You can drop these directly into the models/ folder, making sure that the file name contains ggml somewhere and ends in .bin.
GPT-4chan has been shut down from Hugging Face, so you need to download it elsewhere. You have two options:
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:
models/gpt4chan_model_float16 or models/gpt4chan_model.python download-model.py EleutherAI/gpt-j-6B --text-only
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:
| 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 json file. See settings-template.json for an example. If you create a file called settings.json, 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. |
| 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 ...] | Maxmimum 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. |
--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. |
| 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. |
| 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 | The number of layers to allocate to the GPU. Setting this parameter enables CPU offloading for 4-bit models. |
--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. |
| Flag | Description |
|---|---|
--flexgen | Enable the use of FlexGen offloading. |
--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%). |
| 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. |
| 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. |
| 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-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" |
| Flag | Description |
|---|---|
--api | Enable the API extension. |
--public-api | Create a public URL for the API using Cloudfare. |
| Flag | Description |
|---|---|
--multimodal-pipeline PIPELINE | The multimodal pipeline to use. Examples: llava-7b, llava-13b. |
Out of memory errors? Check the low VRAM guide.
Inference settings presets can be created under presets/ as text files. These files are detected automatically at startup.
By default, 10 presets by NovelAI and KoboldAI 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.
Make sure to check out the documentation for an in-depth guide on how to use the web UI.
https://github.com/oobabooga/text-generation-webui/tree/main/docs
Pull requests, suggestions, and issue reports are welcome.
You are also welcome to review open pull requests.
Before reporting a bug, make sure that you have: