wget https://github.com/intel/auto-round/archive/refs/tags/v0.4.6.tar.gz
tar -xvzf v0.4.6.tar.gz
cd auto-round-0.4.6
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, GPTNeoXModel
  model_name = "iGeniusAI/Italia-9B-Instruct-v0.1"
  model = GPTNeoXModel.from_pretrained(model_name, trust_remote_code=True)
  tokenizer = AutoTokenizer.from_pretrained(model_name)
  from auto_round import AutoRound
  bits, group_size, sym, device, amp = 4, 64, False, 'cpu', False
  autoround = AutoRound(model, tokenizer, nsamples=128, iters=200, seqlen=512, batch_size=4, bits=bits, group_size=group_size, sym=sym, device=device, amp=amp)
  autoround.quantize()
  output_dir = "./AutoRound/iGeniusAI_Italia-9B-Instruct-v0.1-autoawq-int4-gs64-asym"
  autoround.save_quantized(output_dir, format='auto_awq', inplace=True)

Note: the GPTNeoXSdpaAttention class is deprecated in favor of simply modifying the config._attn_implementationattribute of the GPTNeoXAttention class. So this require transformers<4.48.

License

MIT

Disclaimer

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

fbaldassarri/iGeniusAI_Italia-9B-Instruct-v0.1-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

License

Disclaimer

fbaldassarri/
iGeniusAI_Italia-9B-Instruct-v0.1-autoawq-int4-gs64-asym