The Complete Guide to CPU Optimization
For environments without GPUs, performance can be significantly improved by the following technical means:
- quantitative technique: Reduce model size with 8-bit or 4-bit quantization
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-1.5B", load_in_8bit=True) - Batch optimization: Control memory usage by setting the padding and max_length parameters.
generator = pipeline("text-generation", max_length=512, truncation=True) - hardware acceleration: Enable Intel MKL or OpenBLAS math libraries to accelerate matrix operations
export OMP_NUM_THREADS=4
The measured data show that 4-bit quantization can reduce the memory footprint of the 7B parametric model from 13GB to 3.8GB while maintaining the original accuracy of 85%.
This answer comes from the articleTransformers: open source machine learning modeling framework with support for text, image and multimodal tasksThe
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