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Quantization of Large Language Models for Code Generation: A Look at Current Research

Large language models (LLMs) have demonstrated their impressive ability to generate code, particularly in the automatic implementation of requirements described in natural language. The effectiveness of LLMs typically increases with their size: the higher the number of trainable parameters of the LLM, the better its ability to implement code. However, when deploying LLM-based code generators, larger LLMs pose significant challenges in terms of their memory requirements (and consequently their CO2 footprint).

Previous work by Wei et al. proposed using quantization techniques to reduce the memory requirements of LLM-based code generators without significantly impacting their effectiveness. In short, they investigated LLMs with up to 16 billion parameters, quantized their precision from 32-bit floating-point numbers to 8-bit integers, and demonstrated their limited impact on code generation performance.

Given the rapid development of LLM capabilities and quantization techniques, a current research paper presents a nuanced replication of the work by Wei et al. It takes into account: (i) newer and larger code-related LLMs with up to 34 billion parameters; (ii) the latest advances in model quantization techniques, which allow compression to the extreme quantization level of 2 bits per model parameter; and (iii) different types of calibration datasets to control the quantization process, including code-specific datasets.

Results of Current Research

The empirical evaluation of the new research shows that the new frontier for LLM quantization is at 4-bit precision, resulting in an average reduction in memory requirements of 70% compared to the original model, without any significant performance degradation. When the quantization becomes even more extreme (3 and 2 bits), a code-specific calibration dataset helps to limit the performance loss.

These results are particularly relevant for companies like Mindverse, which develop AI-powered content tools and customized solutions such as chatbots, voicebots, AI search engines, and knowledge systems. By applying quantization techniques, these solutions can be deployed and operated more efficiently without compromising the performance of the underlying LLMs.

The research underscores the importance of the continuous development of quantization techniques in the context of the rapid evolution of LLMs. The results suggest that by carefully selecting quantization levels and calibration datasets, significant savings in memory requirements can be achieved without affecting the quality of code generation. This opens up new possibilities for the efficient deployment of LLM-based applications in various fields.

Bibliography: - https://arxiv.org/abs/2503.07103 - https://arxiv.org/html/2503.07103v1 - https://huggingface.co/collections/Devy1/quantization-for-code-generation-67c9b83b34ed9a5a84fb714d - https://github.com/Devy99/lowbit-quantization - https://huggingface.co/papers - https://github.com/codefuse-ai/Awesome-Code-LLM - https://toolbox.google.com/datasetsearch/search?query=Model%20Quantization - https://proceedings.neurips.cc/paper_files/paper/2024/file/6fcc2190f456464160921e98393bf50e-Paper-Conference.pdf - https://openreview.net/forum?id=0wfmHoKQX6 - https://jmlr.org/tmlr/papers/ ```