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Batch Size and Gradient Accumulation

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Batch Size and Gradient Accumulation has 8 facts recorded in Dontopedia across 2 references, with 3 live disagreements.

8 facts·4 predicates·2 sources·3 in dispute

Mostly:has component(2), rdf:type(2), has member(2)

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isStrategyForbeam/21edf814-3c0d-4bbd-9625-954e304f7ed2
ex:CPU-fine-tuning
hasComponentbeam/21edf814-3c0d-4bbd-9625-954e304f7ed2
ex:smaller-batch-sizes
hasComponentbeam/21edf814-3c0d-4bbd-9625-954e304f7ed2
ex:gradient-accumulation
typebeam/21edf814-3c0d-4bbd-9625-954e304f7ed2
ex:OptimizationTechnique
labelbeam/21edf814-3c0d-4bbd-9625-954e304f7ed2
Batch Size and Gradient Accumulation
typebeam/5afb4970-5c3b-4a25-839f-b4f61ca11963
ex:RelatedHyperparameterPair
hasMemberbeam/5afb4970-5c3b-4a25-839f-b4f61ca11963
ex:batch-size
hasMemberbeam/5afb4970-5c3b-4a25-839f-b4f61ca11963
ex:gradient-accumulation-steps

References (2)

2 references
  1. ctx:claims/beam/21edf814-3c0d-4bbd-9625-954e304f7ed2
    • full textbeam-chunk
      text/plain1 KBdoc:beam/21edf814-3c0d-4bbd-9625-954e304f7ed2
      Show excerpt
      [Turn 2485] Assistant: Certainly! While GPUs significantly speed up the training process, you can still fine-tune the model effectively using CPUs. Here are some strategies to help you manage the fine-tuning process on CPUs: ### Strategies
  2. ctx:claims/beam/5afb4970-5c3b-4a25-839f-b4f61ca11963
    • full textbeam-chunk
      text/plain1 KBdoc:beam/5afb4970-5c3b-4a25-839f-b4f61ca11963
      Show excerpt
      - **Strategy**: Use a learning rate scheduler to adjust the learning rate during training. 2. **Batch Size (`per_device_train_batch_size`)**: - **Description**: Number of samples processed before the model is updated. - **Range**:

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