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Enhancing 2-Bit LLM Performance with EoRA: Optimising Model Compression for Real‑World Use

Reducing the memory footprint of large language models doesn’t have to be a headache—it’s all about smart quantisation. By converting parameters from high‑precision formats like 32‑bit floating point into leaner options such as INT8 or INT4, you can dramatically cut down on resource demands. For example, a 4‑bit quantised model uses just 0.5 bytes per […]

16 May 2025 · 2 MIN READRead article →
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