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If you have a different, personalized checkpoint format you want to to convert to, you can increase the checkpoint converter script during the Product Zoo or Speak to Cerebras for support.
The illustrations cover scenarios such as converting a checkpoint using a single GPU with FP16 or BF16 precision, and converting a checkpoint with specific product hyperparameters.
You may transform any TensorFlow checkpoint for BERT (specifically the pre-properly trained models released by Google) inside a PyTorch conserve file by using the
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Modify the values while in the config.yaml file In keeping with your specifications. You'll be able to seek advice from the comments within the file for recommendations and offered choices.
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Here's an illustration of the conversion process for any pre-educated OpenAI GPT model, assuming that your NumPy checkpoint
the TensorFlow checkpoint from the PyTorch product and will save the resulting design in a regular PyTorch preserve file that could