metadata
language:
- en
- de
- fr
- it
- pt
- hi
- es
- th
license: llama3.2
library_name: transformers
tags:
- autoround
- auto-round
- autogptq
- gptq
- auto-gptq
- woq
- meta
- pytorch
- llama
- llama-3
- intel-autoround
- intel
model_name: Llama 3.2 1B Instruct
base_model: meta-llama/Llama-3.2-1B-Instruct
inference: false
model_creator: meta-llama
pipeline_tag: text-generation
prompt_template: '{prompt} '
quantized_by: fbaldassarri
Model Information
Quantized version of meta-llama/Llama-3.2-1B-Instruct using torch.float32 for quantization tuning.
- 8 bits (INT8)
- group size = 128
- Asymmetrical Quantization
- Method AutoGPTQ
Quantization framework: Intel AutoRound
Note: this INT8 version of Llama-3.2-1B-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.
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
Step 2 Build Intel AutoRound wheel from sources
pip install -vvv --no-build-isolation -e .[cpu]
Step 3 Script for Quantization
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "meta-llama/Llama-3.2-1B-Instruct"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
from auto_round import AutoRound
bits, group_size, sym, device, amp = 8, 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/meta-llama_Llama-3.2-1B-Instruct-auto_gptq-int8-gs128-asym"
autoround.save_quantized(output_dir, format='auto_gptq', inplace=True)
License
Disclaimer
This quantized model comes with no warrenty. It has been developed only for research purposes.