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GPTQ-models-(4-bit-mode).md

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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

GPTQ-for-LLaMa branches

Different branches of GPTQ-for-LLaMa are available:

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

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: oobabooga#416 (comment)

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

⚠️ 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)

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
  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

  1. Install https://github.com/sterlind/GPTQ-for-LLaMa with this command:
pip install git+https://github.com/sterlind/GPTQ-for-LLaMa.git@lora_4bit
  1. Start the UI with the --monkey-patch flag:
python server.py --model llama-7b-4bit-128g --listen --lora tloen_alpaca-lora-7b --monkey-patch