catid/cat-llama-3-70b-hqq

AI Model Name: Llama 3 70B "Built with Meta Llama 3" https://llama.meta.com/llama3/license/

How to quantize 70B model so it will fit on 2x4090 GPUs:

I tried EXL2, AutoAWQ, and SqueezeLLM and they all failed for different reasons (issues opened).

HQQ worked:

I rented a 4x GPU 1TB RAM ($19/hr) instance on runpod with 1024GB container and 1024GB workspace disk space. I think you only need 2x GPU with 80GB VRAM and 512GB+ system RAM so probably overpaid.

Note you need to fill in the form to get access to the 70B Meta weights.

You can copy/paste this on the console and it will just set up everything automatically:

apt update
apt install git-lfs vim -y

mkdir -p ~/miniconda3
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh
bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3
~/miniconda3/bin/conda init bash
source ~/.bashrc

conda create -n hqq python=3.10 -y && conda activate hqq

git lfs install
git clone https://github.com/mobiusml/hqq.git
cd hqq

pip install torch
pip install .

pip install huggingface_hub[hf_transfer]
export HF_HUB_ENABLE_HF_TRANSFER=1

huggingface-cli login

Create quantize.py file by copy/pasting this into console:

echo "
import torch

model_id      = 'meta-llama/Meta-Llama-3-70B-Instruct'
save_dir   = 'cat-llama-3-70b-hqq'
compute_dtype = torch.bfloat16

from hqq.core.quantize import *
quant_config = BaseQuantizeConfig(nbits=4, group_size=64, offload_meta=True)
zero_scale_group_size = 128
quant_config['scale_quant_params']['group_size']     = zero_scale_group_size
quant_config['zero_quant_params']['group_size']      = zero_scale_group_size

from hqq.engine.hf import HQQModelForCausalLM, AutoTokenizer
model = HQQModelForCausalLM.from_pretrained(model_id)

from hqq.models.hf.base import AutoHQQHFModel
AutoHQQHFModel.quantize_model(model, quant_config=quant_config,
                                    compute_dtype=compute_dtype)

AutoHQQHFModel.save_quantized(model, save_dir)
model = AutoHQQHFModel.from_quantized(save_dir)

model.eval()

" > quantize.py

Run script:

python quantize.py