Q2BSTUDIO, a leading company in technology development and services, is at the forefront of optimizing and training deep learning models. In this analysis, we explore the impact of quantization on large language models (LLMs) and how precise parameter selection can enhance their performance.
Artificial intelligence models rely on a large number of parameters to function efficiently. However, not all parameters have the same influence on model performance. A small subset, called cherry parameters, has a disproportionate effect on model quality. Indiscriminate quantization of these parameters can significantly affect model accuracy.
To address this issue, a unified mixed-precision training approach is proposed. This method protects cherry parameters by keeping them in high precision during the quantization process, ensuring their critical information is not degraded. Instead of applying traditional post-training quantization (PTQ) methods, which restrict the models' ability to reach optimal values, quantization-aware training (QAT) is adopted. This approach allows for the simultaneous optimization of both cherry parameters and normal parameters.
During backpropagation, high-precision cherry parameters are updated using standard gradient descent, while low-precision normal parameters employ the Straight-Through Estimator (STE) method to adapt. This strategy enables end-to-end optimization, improving the quality and efficiency of quantized models.
At Q2BSTUDIO, we specialize in implementing advanced artificial intelligence solutions and model optimization for various industries. Our expertise in deep learning technologies allows us to develop innovative solutions that maximize the performance and computational efficiency of AI models. We focus on providing technological tools and services aimed at enhancing the development of high-quality AI models, ensuring their scalability and effective application across different sectors.
