如何使用 Megatron-LM 訓練大型語言模型：Hugging Face 實戰指南
Original: How to train a Language Model with Megatron-LM
As language model scales continue to expand, the memory (VRAM) of a single GPU has long been unable to accommodate models with tens or…
這是一篇由 Hugging Face 發布的實用教學，指導開發者如何使用 NVIDIA 的 Megatron-LM 框架來訓練超越單張 GPU 顯存限制的大型語言模型（LLM）。文章深入探討了張量並行（Tensor Parallelism）與流水線並行（Pipeline Parallelism）的核心概念，並詳細說明了從數據準備、訓練配置到最後將 Megatron 權重轉換回 Hugging Face Transformers 格式的完整工作流。
As language model scales continue to expand, the memory (VRAM) of a single GPU has long been unable to accommodate models with tens or hundreds of billions of parameters. To address this hardware bottleneck, NVIDIA developed the Megatron-LM framework, specifically designed for efficient large-scale distributed training across GPU clusters. This Hugging Face blog post provides a detailed guide on how to use Megatron-LM to train large language models and integrate them seamlessly with the Hugging Face ecosystem.
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