  from transformers import AutoModelForCausalLM, AutoTokenizer
  model_name = "meta-llama/Llama-3.2-3B-Instruct"
  model = AutoModelForCausalLM.from_pretrained(model_name)
  tokenizer = AutoTokenizer.from_pretrained(model_name)
  from auto_round import AutoRound
  bits, group_size, sym = 4, 128, True
  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/meta-llama_Llama-3.2-3B-Instruct-auto_gptq-int4-gs128-sym"
  autoround.save_quantized(output_dir, format='auto_gptq', inplace=True)

License

Llama 3.2 Community License

Disclaimer

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

fbaldassarri/meta-llama_Llama-3.2-3B-Instruct-auto_gptq-int4-gs128-sym

Model Information

Replication Recipe

Step 1 Install Requirements

Step 2 Build Intel Autoround wheel from sources

Step 3 Script for Quantization

License

Disclaimer

fbaldassarri/
meta-llama_Llama-3.2-3B-Instruct-auto_gptq-int4-gs128-sym