wget https://github.com/intel/auto-round/archive/refs/tags/v0.4.7.tar.gz
tar -xvzf v0.4.7.tar.gz
cd auto-round-0.4.7
pip install -r requirements-cpu.txt --upgrade

Step 2 Build Intel AutoRound wheel from sources

pip install -vvv --no-build-isolation -e .[cpu]

Step 3 Script for Quantization

  from transformers import AutoModelForCausalLM, AutoTokenizer
  model_name = "EleutherAI/pythia-160m"
  model = AutoModelForCausalLM.from_pretrained(model_name)
  tokenizer = AutoTokenizer.from_pretrained(model_name)
  from auto_round import AutoRound
  bits, group_size, sym, device = 4, 64, False, 'cpu'
  autoround = AutoRound(model, tokenizer, nsamples=128, iters=200, seqlen=512, batch_size=4, bits=bits, group_size=group_size, sym=sym, device=device)
  autoround.quantize()
  output_dir = "./AutoRound/EleutherAI_pythia-160m-autoawq-int4-gs64-asym"
  auto

## License

[Apache 2.0 License](https://choosealicense.com/licenses/apache-2.0/)

## Disclaimer

This quantized model comes with no warrenty. It has been developed only for research purposes.

fbaldassarri/EleutherAI_pythia-160m-autoawq-int4-gs64-asym

Model Information

Replication Recipe

Step 1 Install Requirements

Step 2 Build Intel AutoRound wheel from sources

Step 3 Script for Quantization

fbaldassarri/
EleutherAI_pythia-160m-autoawq-int4-gs64-asym