Summary of Abq-llm: Arbitrary-bit Quantized Inference Acceleration For Large Language Models, by Chao Zeng et al.
ABQ-LLM: Arbitrary-Bit Quantized Inference Acceleration for Large Language Models
by Chao Zeng, Songwei Liu, Yusheng Xie, Hong Liu, Xiaojian Wang, Miao Wei, Shu Yang, Fangmin Chen, Xing Mei
First submitted to arxiv on: 16 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel algorithm called ABQ-LLM (Arbitrary-Bit Quantization Large Language Models) that tackles the challenges of deploying Post-training quantization (PTQ) for Large Language Models (LLMs). The authors introduce a distribution correction method to mitigate performance degradation caused by full quantization of weights and activations, as well as a bit balance strategy to counteract performance issues at very low bit-widths. Additionally, they propose an innovative quantization acceleration framework that reconstructs matrix multiplication operations with arbitrary precision combinations based on Binary TensorCore (BTC) equivalents. The ABQ-LLM algorithm achieves superior performance across various quantization settings and enables efficient arbitrary-precision quantized inference on the GPU, with applications in LLM model compression and mixed precision computing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making Large Language Models (LLMs) run faster and use less memory. Right now, these models are very powerful but also take up a lot of computer resources. The authors create a new way to make them work better called ABQ-LLM. It helps by fixing some problems that make the models slower or less accurate when they’re used in certain ways. This innovation makes it possible to run LLMs with different levels of precision (like 2-bit or 8-bit) on graphics processing units (GPUs), which is important for tasks like text generation and language translation. |
Keywords
» Artificial intelligence » Inference » Model compression » Precision » Quantization » Text generation » Translation
