Model Information

Quantized version of mistralai/Mistral-7B-v0.3 using torch.float32 for quantization tuning.

4 bits (INT4)
group size = 128
Asymmetrical Quantization
Method AutoAWQ

Quantization framework: Intel AutoRound v0.4.3

Note: this INT4 version of Mistral-7B-v0.3 has been quantized to run inference through CPU.

Replication Recipe

Step 1 Install Requirements

I suggest to install requirements into a dedicated python-virtualenv or a conda enviroment.

wget https://github.com/intel/auto-round/archive/refs/tags/v0.4.3.tar.gz
tar -xvzf v0.4.3.tar.gz
cd auto-round-0.4.3
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 = "mistralai/Mistral-7B-v0.3"
  model = AutoModelForCausalLM.from_pretrained(model_name)
  tokenizer = AutoTokenizer.from_pretrained(model_name)
  from auto_round import AutoRound
  bits, group_size, sym, device, amp = 4, 128, 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/mistralai_Mistral-7B-v0.3-autoawq-int4-gs128-asym"
  autoround.save_quantized(output_dir, format='auto_awq', inplace=True)