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

In 4-bit mode, models are loaded with just 25% of their regular VRAM usage. So LLaMA-7B fits into a 6GB GPU, and LLaMA-30B fits into a 24GB GPU.

This is possible thanks to @qwopqwop200's adaptation of the GPTQ algorithm for LLaMA: https://github.com/qwopqwop200/GPTQ-for-LLaMa

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

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.

https://github.com/oobabooga/GPTQ-for-LLaMa corresponds to commit a6f363e3f93b9fb5c26064b5ac7ed58d22e3f773 in the cuda branch of the original repository and is recommended by default for stability. Some models might require you to use the up-to-date CUDA or triton branches:

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

Note: the tokenizer files in those torrents are not up to date.

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)

Using LoRAs in 4-bit mode

At the moment, this feature is not officially supported by the relevant libraries, but a patch exists and is supported by this web UI: https://github.com/johnsmith0031/alpaca_lora_4bit

In order to use it:

1. Make sure that your requirements are up to date:

cd text-generation-webui
pip install -r requirements.txt --upgrade

2. Clone johnsmith0031/alpaca_lora_4bit into the repositories folder:

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

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

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

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

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