Efficient Deployment of Large Language Models via Quantization

Large language models (LLMs) have gained significant attention in recent years due to their impressive performance in handling complex natural language tasks such as language generation, translation, question answering, and text summarization. However, these models come with certain challenges, particularly their vast size, which requires substantial computational resources for inference and deployment. To address this issue, researchers have developed model compression techniques like network pruning, knowledge distillation, network quantization, and neural Huffman coding.
One such approach is cross-block quantization (CBQ), proposed by Han et al. (2020b). CBQ aims to improve the overall accuracy and stability of the reconstruction process by optimizing the average of two homologous reconstruction errors: one calculated based on the model’s weights, and another based on the input data. By minimizing this optimization objective, CBQ can effectively reduce the computational requirements of LLMs without compromising their performance.
To validate the superiority of CBQ over state-of-the-art block reconstruction methods, Hubara et al. (2020) conducted extensive experiments on quantizing the LLAMA-30B model. The results demonstrated that CBQ outperformed other methods across various datasets, providing compelling evidence of its effectiveness and superiority.
To further analyze the contribution of each component in the proposed CBQ method, ablation experiments were conducted on the WikiText2 dataset using perplexity as the evaluation metric. The results showed that CBQ’s performance improvement is attributed to the optimization of both weight-based and input-based reconstruction errors.
In summary, CBQ is a promising technique for improving the efficiency of large language models without compromising their accuracy. By optimizing the average of two homologous reconstruction errors, CBQ can significantly reduce the computational requirements of LLMs while maintaining their performance. This approach has important implications for researchers and organizations with limited access to high-performance computing infrastructure, as it enables them to utilize LLMs more effectively and efficiently.