透過 DeepSpeed 與 FairScale 的 ZeRO 技術，讓 Hugging Face 訓練容納更多參數且速度更快
Original: Fit More and Train Faster With ZeRO via DeepSpeed and FairScale
As the parameter scale of Transformer models (such as GPT, T5, etc.) grows exponentially, deep learning faces a severe "Memory Wall"…
Hugging Face 宣布在其 Trainer 中整合 Microsoft DeepSpeed 與 Facebook FairScale 的 ZeRO（零冗餘優化器）技術。這項技術透過將優化器狀態、梯度和模型參數分片到多個 GPU 上，顯著降低顯存佔用。開發者現在可以輕鬆在有限的硬體資源下，訓練原本無法容納的超大型 Transformer 模型，並大幅提升訓練效率。
As the parameter scale of Transformer models (such as GPT, T5, etc.) grows exponentially, deep learning faces a severe "Memory Wall" challenge. With limited GPU VRAM, training models with tens of billions or even hundreds of billions of parameters becomes extremely difficult — in addition to the model parameters themselves, optimizer states (e.g., for Adam) and gradients also consume enormous amounts of memory.
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