AutoAWQ-INT4-gs128
Collection
A collection of models quantized in AutoAWQ format using Intel AutoRound, INT4, groupsize 128
•
57 items
•
Updated
Quantized version of EleutherAI/pythia-12b-deduped using torch.float32 for quantization tuning.
Quantization framework: Intel AutoRound v0.4.3
Note: this INT4 version of pythia-12b-deduped has been quantized to run inference through CPU.
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
pip install -vvv --no-build-isolation -e .[cpu]
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "EleutherAI/pythia-12b-deduped"
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/EleutherAI_pythia-12b-deduped-autoawq-int4-gs128-asym"
autoround.save_quantized(output_dir, format='auto_awq', inplace=True)
This quantized model comes with no warranty. It has been developed only for research purposes.
Base model
EleutherAI/pythia-12b-deduped