Dropdown menu for quickly switching between different models.
Large number of extensions (built-in and user-contributed), including Coqui TTS for realistic voice outputs, Whisper STT for voice inputs, translation, multimodal pipelines, vector databases, Stable Diffusion integration, and a lot more. See the wiki and the extensions directory for details.
Precise chat templates for instruction-following models, including Llama-2-chat, Alpaca, Vicuna, Mistral.
LoRA: train new LoRAs with your own data, load/unload LoRAs on the fly for generation.
Transformers library integration: load models in 4-bit or 8-bit precision through bitsandbytes, use llama.cpp with transformers samplers (llamacpp_HF loader), CPU inference in 32-bit precision using PyTorch.
OpenAI-compatible API server with Chat and Completions endpoints -- see the examples.
Run the start_linux.sh, start_windows.bat, start_macos.sh, or start_wsl.bat script depending on your OS.
Select your GPU vendor when asked.
Once the installation ends, browse to http://localhost:7860/?__theme=dark.
Have fun!
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_linux.sh, update_windows.bat, update_macos.sh, or update_wsl.bat.
Setup details and information about installing manually
One-click-installer
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.
There is no need to run any of those scripts (start_, update_, or cmd_) as admin/root.
For additional instructions about AMD and WSL setup, consult the documentation.
For automated installation, you can use the 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=FALSE ./start_linux.sh.
Manual installation using Conda
Recommended if you have some experience with the command-line.
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.
Alternative: Docker
ln -s docker/{nvidia/Dockerfile,docker-compose.yml,.dockerignore} .
cp docker/.env.example .env
# 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
docker compose up --build
You need to have Docker Compose v2.17 or higher installed. See this guide for instructions.
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
List of 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 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.
Model loader
Flag
Description
--loader LOADER
Choose the model loader manually, otherwise, it will get autodetected. Valid options: Transformers, llama.cpp, llamacpp_HF, ExLlamav2_HF, ExLlamav2, AutoGPTQ, AutoAWQ, GPTQ-for-LLaMa, ctransformers, QuIP#.
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 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.
bitsandbytes 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).
--use_double_quant
use_double_quant for 4-bit.
--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.
llama.cpp
Flag
Description
--tensorcores
Use llama-cpp-python compiled with tensor cores support. This increases performance on RTX cards. NVIDIA only.
--n_ctx N_CTX
Size of the prompt context.
--threads
Number of threads to use.
--threads-batch THREADS_BATCH
Number of threads to use for batches/prompt processing.
--no_mul_mat_q
Disable the mulmat kernels.
--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.
--n-gpu-layers N_GPU_LAYERS
Number of layers to offload to the GPU.
--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.
ExLlamav2
Flag
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.
--num_experts_per_token NUM_EXPERTS_PER_TOKEN
Number of experts to use for generation. Applies to MoE models like Mixtral.
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.
--disable_exllamav2
Disable ExLlamav2 kernel.
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.
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.
HQQ
Flag
Description
--hqq-backend
Backend for the HQQ loader. Valid options: PYTORCH, PYTORCH_COMPILE, ATEN.
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.
RoPE (for llama.cpp, 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-port LISTEN_PORT
The listening port that the server will use.
--listen-host LISTEN_HOST
The hostname 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 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.
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-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.
Multimodal
Flag
Description
--multimodal-pipeline PIPELINE
The multimodal pipeline to use. Examples: llava-7b, llava-13b.
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.
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.
