引导式测验 · 中等 等级

检查点分片和可恢复训练测验

练习检查点元数据、训练状态恢复和重新启动验证，包括权重分片和可恢复状态之间的差异。

相关引导路径检查点分片和可恢复训练
问题 1 的 8

为什么大模型检查点要分成分片？

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  1. 为什么大模型检查点要分成分片？
    • 使模型在小输入上训练得更快
    • 对用户隐藏模型权重
    • 因为分片提高了准确性
    • 因为单个检查点对于一个文件或机器来说可能太大而无法有效处理
  2. Which extra state is typically needed for training resumption beyond model weights?
    • 仅最终准确率分数
    • Optimizer and scheduler state, step progress, RNG state, and relevant data-loader progress
    • Only a model-weight shard index
    • A screenshot of the training dashboard
  3. 长期工作可恢复培训的主要好处是什么？
    • 它消除了对验证集的需要
    • 它保证模型永远不会过度拟合
    • 崩溃或抢占不会强制从头开始重新启动
    • 它消除了优化器
  4. In a sharded distributed checkpoint, how can ranks avoid gathering the entire state on one worker?
    • By saving their assigned local tensor shards with checkpoint metadata
    • By each emailing a full model copy
    • By deleting optimizer state before every save
    • By disabling all state serialization
  5. 索引文件在分片检查点中起什么作用？
    • 它存储用户的密码
    • 它将每个参数名称映射到包含它的分片文件
    • 它压缩数据集中的图像
    • 它安排学习率
  6. 哪种格式通常用于模型分片的安全、内存映射加载？
    • MP3
    • CSV
    • 安全张量
    • 仅 ZIP 存档
  7. A package contains safetensors weight shards and an index, but no optimizer state. What can it support by itself?
    • Loading model weights, without fully restoring optimizer training history
    • Guaranteed exact resumption of every interrupted run
    • Recovering the next batch position automatically
    • Reconstructing every random-number-generator state
  8. A sharded checkpoint loader supports a new rank layout. What should the team verify before trusting recovery?
    • That fewer ranks guarantee identical floating-point results
    • That the weight index alone includes every application state
    • That no restart test is necessary
    • That the necessary saved state loads correctly and the resumed training step behaves as expected