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.
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
To download a protected model, set env vars HF_USER and HF_PASS to your Hugging Face username and password (or User Access Token). The model's terms must first be accepted on the HF website.
GGML models
You can drop these directly into the models/ folder, making sure that the file name contains ggml somewhere and ends in .bin.
GPT-4chan
Instructions
GPT-4chan has been shut down from Hugging Face, so you need to download it elsewhere. You have two options:
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.
--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 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, flexgen
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.
llama.cpp
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.
--cache-capacity CACHE_CAPACITY
Maximum cache capacity. Examples: 2000MiB, 2GiB. When provided without units, bytes will be assumed.
--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_seed SEED
Seed for llama-cpp models. Default 0 (random).
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.
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.
--compress_pos_emb COMPRESS_POS_EMB
Positional embeddings compression factor. Should typically be set to max_seq_len / 2048.
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.
--quant_attn
(triton) Enable quant attention.
--warmup_autotune
(triton) Enable warmup autotune.
--fused_mlp
(triton) Enable fused mlp.
FlexGen
Flag
Description
--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%).
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.
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"
API
Flag
Description
--api
Enable the API extension.
--public-api
Create a public URL for the API using Cloudfare.
--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.
