AutoGPTQ-INT4-gs128
Collection
A collection of models quantized in AutoGPTQ format using Intel AutoRound, INT4, groupsize 128
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66 items
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Updated
Quantized version of meta-llama/Llama-3.1-8B using torch.float32 for quantization tuning.
Quantization framework: Intel AutoRound
Note: this INT4 version of Llama-3.1-8B has been quantized to run inference through CPU.
I suggest to install requirements into a dedicated python-virtualenv or a conda enviroment.
python -m pip install <package> --upgrade
python -m pip install git+https://github.com/intel/auto-round.git
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "meta-llama/Llama-3.1-8B"
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, True, '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/meta-llama_Llama-3.1-8B-auto_gptq-int4-gs128-sym"
autoround.save_quantized(output_dir, format='auto_gptq', inplace=True)
This quantized model comes with no warrenty. It has been developed only for research purposes.
Base model
meta-llama/Llama-3.1-8B