Model quantization techniques#

Applies to Linux


11 min read time

Quantization reduces the model size compared to its native full-precision version, making it easier to fit large models onto accelerators or GPUs with limited memory usage. This section explains how to perform LLM quantization using GPTQ and bitsandbytes on AMD Instinct hardware.


GPTQ is a post-training quantization technique where each row of the weight matrix is quantized independently to find a version of the weights that minimizes error. These weights are quantized to int4 but are restored to fp16 on the fly during inference. This can save your memory usage by a factor of four. A speedup in inference is expected because inference of GPTQ models uses a lower bit width, which takes less time to communicate.

Before setting up the GPTQ configuration in Transformers, ensure the AutoGPTQ library is installed.

Installing AutoGPTQ#

The AutoGPTQ library implements the GPTQ algorithm.

  1. Use the following command to install the latest stable release of AutoGPTQ from pip.

    # This will install pre-built wheel for a specific ROCm version.
    pip install auto-gptq --no-build-isolation --extra-index-url

    Or, install AutoGPTQ from source for the appropriate ROCm version (for example, ROCm 6.1).

    # Clone the source code.
    git clone
    cd AutoGPTQ
    # Speed up the compilation by specifying PYTORCH_ROCM_ARCH to target device.
    PYTORCH_ROCM_ARCH=gfx942 ROCM_VERSION=6.1 pip install .
    # Show the package after the installation
  2. Run pip show auto-gptq to print information for the installed auto-gptq package. Its output should look like this:

    Name: auto-gptq
    Version: 0.8.0.dev0+rocm6.1

Using GPTQ with AutoGPTQ#

  1. Run the following code snippet.

    from transformers import AutoTokenizer, TextGenerationPipeline
    from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
    base_model_name = "NousResearch/Llama-2-7b-hf"
    quantized_model_name = "llama-2-7b-hf-gptq"
    tokenizer = AutoTokenizer.from_pretrained(base_model_name, use_fast=True)
    examples = [
            "auto-gptq is an easy-to-use model quantization library with user-friendly apis, based on GPTQ algorithm."

    The resulting examples should be a list of dictionaries whose keys are input_ids and attention_mask.

  2. Set up the quantization configuration using the following snippet.

    quantize_config = BaseQuantizeConfig(
        bits=4,               # quantize model to 4-bit
        group_size=128,       # it is recommended to set the value to 128
  3. Load the non-quantized model using the AutoGPTQ class and run the quantization.

    # Import auto_gptq class.
    from auto_gptq import AutoGPTQForCausalLM
    # Load non-quantized model.
    base_model = AutoGPTQForCausalLM.from_pretrained(base_model_name, quantize_config, device_map = "auto")
    # Save quantized model.

Using GPTQ with Hugging Face Transformers#

  1. To perform a GPTQ quantization using Hugging Face Transformers, you need to create a GPTQConfig instance and set the number of bits to quantize to, and a dataset to calibrate the weights.

    from transformers import AutoModelForCausalLM, AutoTokenizer, GPTQConfig
    base_model_name = " NousResearch/Llama-2-7b-hf"
    tokenizer = AutoTokenizer.from_pretrained(base_model_name)
    gptq_config = GPTQConfig(bits=4, dataset="c4", tokenizer=tokenizer)
  2. Load a model to quantize using AutoModelForCausalLM and pass the gptq_config to its from_pretained method. Set device_map=”auto” to automatically offload the model to available GPU resources.

    quantized_model = AutoModelForCausalLM.from_pretrained(
  3. Once the model is quantized, you can push the model and tokenizer to Hugging Face Hub for easy share and access.


    Or, you can save the model locally using the following snippet.


ExLlama-v2 support#

ExLlama is a Python/C++/CUDA implementation of the Llama model that is designed for faster inference with 4-bit GPTQ weights. The ExLlama kernel is activated by default when users create a GPTQConfig object. To boost inference speed even further on Instinct accelerators, use the ExLlama-v2 kernels by configuring the exllama_config parameter as the following.

from transformers import AutoModelForCausalLM, GPTQConfig
#pretrained_model_dir = "meta-llama/Llama-2-7b"
base_model_name = "NousResearch/Llama-2-7b-hf"
gptq_config = GPTQConfig(bits=4, dataset="c4", exllama_config={"version":2})
quantized_model = AutoModelForCausalLM.from_pretrained(


The ROCm-aware bitsandbytes library is a lightweight Python wrapper around CUDA custom functions, in particular 8-bit optimizer, matrix multiplication, and 8-bit and 4-bit quantization functions. The library includes quantization primitives for 8-bit and 4-bit operations through bitsandbytes.nn.Linear8bitLt and bitsandbytes.nn.Linear4bit and 8-bit optimizers through the bitsandbytes.optim module. These modules are supported on AMD Instinct accelerators.

Installing bitsandbytes#

  1. To install bitsandbytes for ROCm 6.0 (and later), use the following commands.

    # Clone the github repo
    git clone --recurse
    cd bitsandbytes
    git checkout rocm_enabled
    # Install dependencies
    pip install -r requirements-dev.txt
    # Use -DBNB_ROCM_ARCH to specify target GPU arch
    cmake -DBNB_ROCM_ARCH="gfx942" -DCOMPUTE_BACKEND=hip -S .
    # Install
    python install
  2. Run pip show bitsandbytes to show the information about the installed bitsandbytes package. Its output should look like the following.

    Name: bitsandbytes
    Version: 0.44.0.dev0

Using bitsandbytes primitives#

To get started with bitsandbytes primitives, use the following code as reference.

import bitsandbytes as bnb

# Use Int8 Matrix Multiplication
bnb.matmul(..., threshold=6.0)

# Use bitsandbytes 8-bit Optimizers
adam = bnb.optim.Adam8bit(model.parameters(), lr=0.001, betas=(0.9, 0.995))

Using bitsandbytes with Hugging Face Transformers#

To load a Transformers model in 4-bit, set load_int_4bt=true in BitsAndBytesConfig.

from transformers import AutoModelForCausalLM
from bitsandbytes import BitsAndBytesConfig

base_model_name = "NousResearch/Llama-2-7b-hf"
quantization_config = BitsAndBytesConfig(load_in_4bit=True)
bnb_model_4bit = AutoModelForCausalLM.from_pretrained(

# Check the memory footprint with get_memory_footprint method

To load a model in 8-bit for inference, use the load_in_8bit option.

from transformers import AutoModelForCausalLM, AutoTokenizer
from bitsandbytes import BitsAndBytesConfig

base_model_name = "NousResearch/Llama-2-7b-hf"

tokenizer = AutoTokenizer.from_pretrained(base_model_name)
quantization_config = BitsAndBytesConfig(load_in_8bit=True)
tokenizer = AutoTokenizer.from_pretrained(base_model_name)
bnb_model_8bit = AutoModelForCausalLM.from_pretrained(

prompt = "What is a large language model?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
generated_ids = model.generate(**inputs)
outputs = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)