Hugging Face Transformers 原生支援量化方案全解析：bitsandbytes 與 GPTQ 實戰指南
Original: Overview of natively supported quantization schemes in 🤗 Transformers
As the parameter count of large language models (LLMs) has grown dramatically, running and fine-tuning these models on consumer-grade GPUs…
本文介紹 Hugging Face Transformers 庫中原生整合的量化方案。主要涵蓋 bitsandbytes（包含 8-bit 與用於 QLoRA 的 4-bit 量化）以及 GPTQ 技術。文章詳細解析了各量化方案的運作原理、記憶體節省幅度、推論速度表現，並提供對應的程式碼範例，幫助開發者在有限的硬體資源下部署與微調大型語言模型。
As the parameter count of large language models (LLMs) has grown dramatically, running and fine-tuning these models on consumer-grade GPUs or limited hardware has become one of the biggest challenges developers face. Hugging Face Transformers addresses this by natively integrating multiple quantization schemes, allowing users to drastically reduce a model's VRAM footprint with just a few lines of code changes. This article provides a detailed introduction to the two mainstream techniques built into Transformers: bitsandbytes and GPTQ.
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