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  • How to choose Batch Size and Number of Epochs When Fitting a . . .
    Choosing the right batch size and number of epochs is crucial for optimizing the performance of your machine learning models While there are general guidelines and best practices, the optimal values depend on your specific dataset, model architecture, and computational resources
  • Huggingface Trainer only doing 3 epochs no matter the . . .
    I would like to continue training beyond the 3 epochs to increase my accuracy and continue to decrease training and validation loss I tried changing the num_train_epochs=10 as you can see but nothing changes
  • All You Need to Know about Batch Size, Epochs and Training . . .
    Batch size refers to the number of training instances in the batch Epochs refer to the number of times the model sees the entire dataset A training step (iteration) is one gradient update
  • Tuning Hyperparameters | CodeFriends Resources
    In a practical setting, you can optimize the training process by adjusting batch size, learning rate, and number of epochs The speed at which the model updates its weights is determined by this hyperparameter
  • Step-by-Step Code for Fine-Tuning a pre-trained LLM
    This code defines the training arguments, including the number of epochs, batch size, learning rate, and other parameters The Trainer class from Hugging Face provides a convenient way to handle the training loop
  • Fine-tuning Configuration Guide | runpod-workers llm-fine . . .
    The fine-tuning configuration system allows you to control every aspect of the training process through a structured JSON request The system validates your configuration, converts it to the format expected by Axolotl (the underlying fine-tuning framework), and executes the training process
  • How to adjust the learning rate after N number of epochs?
    scheduler = get_constant_schedule_with_warmup(optimizer, num_warmup_steps = N batch_size) where N is number of epochs after which you want to use the constant lr This will increase your lr from 0 to initial_lr specified in your optimizer in num_warmup_steps, after which it becomes constant





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