metadata
language:
- en
tags:
- pytorch
- causal-lm
- pythia
- autoround
- intel
- intel-autoround
- awq
- autoawq
- auto-awq
- woq
license: apache-2.0
model_name: Pythia 12b
base_model: EleutherAI/pythia-12b
inference: false
model_creator: EleutherAI
datasets:
- EleutherAI/pile
pipeline_tag: text-generation
prompt_template: '{prompt} '
quantized_by: fbaldassarri
Model Information
Quantized version of EleutherAI/pythia-12b using torch.float32 for quantization tuning.
- 4 bits (INT4)
- group size = 128
- Symmetrical Quantization
- Method AutoAWQ
Quantization framework: Intel AutoRound v0.4.2
Note: this INT4 version of pythia-12b 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==1.2.0
- autoawq==0.2.7.post3
- auto_gptq==0.7.1
- neural_compressor==3.1.1
- torch==2.4.1+cpu
- torchaudio==2.4.1+cpu
- torchvision==0.19.1+cpu
- transformers==4.47.0
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 = "EleutherAI/pythia-12b"
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/EleutherAI_pythia-12b-autoawq-int4-gs128-sym"
autoround.save_quantized(output_dir, format='auto_awq', inplace=True)
License
Disclaimer
This quantized model comes with no warrenty. It has been developed only for research purposes.