fbaldassarri/HuggingFaceTB_SmolLM2-1.7B-Instruct-auto_round-int4-gs128-asym

Model Information

Quantized version of HuggingFaceTB/SmolLM2-1.7B-Instruct using torch.float32 for quantization tuning.

• 4 bits (INT4)
• group size = 128
• Asymmetrical Quantization
• Method AutoRound (WOQ)

Fast and low memory, 2-3X speedup (slight accuracy drop at W4G128)

Quantization framework: Intel AutoRound

Note: this INT4 version of SmolLM2-1.7B-Instruct 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.

python -m pip install <package> --upgrade

• accelerate==1.0.1
• auto_gptq==0.7.1
• neural_compressor==3.1
• torch==2.3.0+cpu
• torchaudio==2.5.0+cpu
• torchvision==0.18.0+cpu
• transformers==4.45.2

Step 2 Build Intel Autoround wheel from sources

python -m pip install git+https://github.com/intel/auto-round.git

Step 3 Script for Quantization

  from transformers import AutoModelForCausalLM, AutoTokenizer
  model_name = "HuggingFaceTB/SmolLM2-1.7B-Instruct"
  model = AutoModelForCausalLM.from_pretrained(model_name)
  tokenizer = AutoTokenizer.from_pretrained(model_name)
  from auto_round import AutoRound
  bits, group_size, sym = 4, 128, False
  autoround = AutoRound(model, tokenizer, nsamples=128, iters=200, seqlen=512, batch_size=4, bits=bits, group_size=group_size, sym=sym)
  autoround.quantize()
  output_dir = "./AutoRound/HuggingFaceTB_SmolLM2-1.7B-Instruct-auto_round-int4-gs128-asym"
  autoround.save_quantized(output_dir, format='auto_round', inplace=True)

License

Apache 2.0 License

Disclaimer

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