Are you eager to get hands-on with the powerful Llama 3 70B model but daunted by the hardware requirements? Worry not! In this guide, we’ll walk you through the steps to quantize the model so that it can comfortably fit into your 2×4090 GPUs. Let’s dive in!

Understanding the Basics of Quantization

Quantization can be thought of as packing a suitcase for a long trip. Just like you may need to fold your clothes and leave behind some items to fit everything into your suitcase, quantization helps to reduce the size of your model while retaining its essential functionality. In this case, by adjusting the precision of model weights from floating-point representations to lower-precision formats, you make it feasible to run on less powerful hardware.

Requirements Before You Start

• GPUs: You will need at least 2x NVIDIA 4090 GPUs with at least 80GB VRAM.
• RAM: A minimum of 512GB system RAM is recommended.
• Access to Weights: Fill out the form to get access to the 70B Meta weights.

Setting Up the Environment

To ensure everything runs smoothly, you need to set up your environment properly. Follow these commands in your terminal:

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

These commands will set up Miniconda and install the necessary libraries to prepare for the quantization process.

Creating the Quantization Script

Now it’s time to create the quantization script. Just like when you write a recipe for a dish you want to cook, this script will guide the computer through the steps it needs to run. Here’s how to create the quantize.py file:

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

This script orchestrates the quantization process, tuning the model to the required specifications.

Running the Quantization Process

Finally, trigger the quantization process by running the following command in your terminal:

python quantize.py

And just like that, your model will be prepared for performance on your GPUs!

Troubleshooting Tips

If you encounter issues while following this guide, here are some troubleshooting tips to help you out:

• Check Your GPU Resources: Ensure that the GPUs are properly detected and have sufficient VRAM and RAM available.
• Review Error Messages: Pay attention to any error messages displayed in the terminal; they can provide specific clues about what went wrong.
• Dependencies Installation: Make sure that all libraries are correctly installed. You may need to reinstall components if errors persist.

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Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.