使用 PyTorch FSDP 高效微調 Llama 2 70B：解決 CPU 記憶體不足的實務指南
Original: Fine-tuning Llama 2 70B using PyTorch FSDP
When fine-tuning massively large open-source models like Llama 2 70B — with its 70 billion parameters — developers frequently encounter a…
微調 Llama 2 70B 等超大型模型時，開發者常因多進程重複載入模型而面臨 CPU 記憶體崩潰（OOM）的困境。本文介紹如何結合 PyTorch FSDP（完全分片數據並行）與 Hugging Face Accelerate 的延遲初始化與分片載入技術，在有限的硬體資源下實現記憶體高效的微調流程，大幅降低大模型訓練的門檻。
When fine-tuning massively large open-source models like Llama 2 70B — with its 70 billion parameters — developers frequently encounter a bottleneck that goes beyond GPU VRAM: the host system's CPU RAM. In a traditional multi-GPU training initialization process, each GPU process typically attempts to load the complete model weights into CPU memory. For a 70B model (approximately 140GB in FP16 format), this instantly causes the system to crash due to CPU OOM (out-of-memory) errors.
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