text-generation-webui/docs/GPTQ-models-(4-bit-mode).md

GPTQ is a clever quantization algorithm that lightly reoptimizes the weights during quantization so that the accuracy loss is compensated relative to a round-to-nearest quantization. See the paper for more details: https://arxiv.org/abs/2210.17323

AutoGPTQ

AutoGPTQ is the recommended way to create new quantized models: https://github.com/PanQiWei/AutoGPTQ

Installation

To load a model quantized with AutoGPTQ in the web UI, you need to first manually install the AutoGPTQ library:

conda activate textgen
git clone https://github.com/PanQiWei/AutoGPTQ.git && cd AutoGPTQ
pip install .

The last command requires nvcc to be installed (see the instructions below).

Usage

When you quantize a model using AutoGPTQ, a folder containing a filed called quantize_config.json will be generated. Place that folder inside your models/ folder and load it with the --autogptq flag:

python server.py --autogptq --model model_name

Alternatively, check the autogptq box in the "Model" tab of the UI before loading the model.

Offloading

In order to do CPU offloading or multi-gpu inference with AutoGPTQ, use the --gpu-memory flag. It is currently somewhat slower than offloading with the --pre_layer option in GPTQ-for-LLaMA (more on that below).

For CPU offloading:

python server.py --autogptq --gpu-memory 3000MiB --model model_name

For multi-GPU:

python server.py --autogptq --gpu-memory 3000MiB 6000MiB --model model_name

Using LoRAs with AutoGPTQ

Not supported yet.

GPTQ-for-LLaMa

GPTQ-for-LLaMa is the original adaptation of GPTQ for the LLaMA model. It was made by @qwopqwop200 in this repository: https://github.com/qwopqwop200/GPTQ-for-LLaMa

Different branches of GPTQ-for-LLaMa are currently available, including:

Branch	Comment
Old CUDA branch (recommended)	The fastest branch, works on Windows and Linux.
Up-to-date triton branch	Slightly more precise than the old CUDA branch from 13b upwards, significantly more precise for 7b. 2x slower for small context size and only works on Linux.
Up-to-date CUDA branch	As precise as the up-to-date triton branch, 10x slower than the old cuda branch for small context size.

Overall, I recommend using the old CUDA branch. It is included by default in the one-click-installer for this web UI.

Installation using precompiled wheels

https://github.com/jllllll/GPTQ-for-LLaMa-Wheels

Manual installation

Step 0: install nvcc

conda activate textgen
conda install -c conda-forge cudatoolkit-dev

The command above takes some 10 minutes to run and shows no progress bar or updates along the way.

See this issue for more details: https://github.com/oobabooga/text-generation-webui/issues/416#issuecomment-1475078571

Step 1: install GPTQ-for-LLaMa

Clone the GPTQ-for-LLaMa repository into the text-generation-webui/repositories subfolder and install it:

mkdir repositories
cd repositories
git clone https://github.com/oobabooga/GPTQ-for-LLaMa.git -b cuda
cd GPTQ-for-LLaMa
python setup_cuda.py install

You are going to need to have a C++ compiler installed into your system for the last command. On Linux, sudo apt install build-essential or equivalent is enough.

If you want to you to use the up-to-date CUDA or triton branches instead of the old CUDA branch, use these commands:

cd repositories
rm -r GPTQ-for-LLaMa
pip uninstall -y quant-cuda
git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa.git -b cuda
...

cd repositories
rm -r GPTQ-for-LLaMa
pip uninstall -y quant-cuda
git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa.git -b triton
...

https://github.com/qwopqwop200/GPTQ-for-LLaMa

Step 2: get the pre-converted weights

• Converted without group-size (better for the 7b model): https://github.com/oobabooga/text-generation-webui/pull/530#issuecomment-1483891617
• Converted with group-size (better from 13b upwards): https://github.com/oobabooga/text-generation-webui/pull/530#issuecomment-1483941105

⚠️ The tokenizer files in the sources above may be outdated. Make sure to obtain the universal LLaMA tokenizer as described here.

Step 3: Start the web UI:

For the models converted without group-size:

python server.py --model llama-7b-4bit 

For the models converted with group-size:

python server.py --model llama-13b-4bit-128g 

The command-line flags --wbits and --groupsize are automatically detected based on the folder names, but you can also specify them manually like

python server.py --model llama-13b-4bit-128g --wbits 4 --groupsize 128

CPU offloading

It is possible to offload part of the layers of the 4-bit model to the CPU with the --pre_layer flag. The higher the number after --pre_layer, the more layers will be allocated to the GPU.

With this command, I can run llama-7b with 4GB VRAM:

python server.py --model llama-7b-4bit --pre_layer 20

This is the performance:

Output generated in 123.79 seconds (1.61 tokens/s, 199 tokens)

You can also use multiple GPUs with pre_layer if using the oobabooga fork of GPTQ, eg --pre_layer 30 60 will load a LLaMA-30B model half onto your first GPU and half onto your second, or --pre_layer 20 40 will load 20 layers onto GPU-0, 20 layers onto GPU-1, and 20 layers offloaded to CPU.

Using LoRAs with GPTQ-for-LLaMa

This requires using a monkey patch that is supported by this web UI: https://github.com/johnsmith0031/alpaca_lora_4bit

To use it:

1. Clone johnsmith0031/alpaca_lora_4bit into the repositories folder:

cd text-generation-webui/repositories
git clone https://github.com/johnsmith0031/alpaca_lora_4bit

⚠️ I have tested it with the following commit specifically: 2f704b93c961bf202937b10aac9322b092afdce0

2. Install https://github.com/sterlind/GPTQ-for-LLaMa with this command:

pip install git+https://github.com/sterlind/GPTQ-for-LLaMa.git@lora_4bit

3. Start the UI with the --monkey-patch flag:

python server.py --model llama-7b-4bit-128g --listen --lora tloen_alpaca-lora-7b --monkey-patch