Episode 26: Developing and Training LLMs From Scratch
Vanishing Gradients
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Navigating Challenges in Training Large Language Models
Training large language models (LLMs) can present challenges due to the need for multi-GPU training, mixed precision training, checkpoint management, and distributed data parallelism. While deep learning fundamentals remain crucial, mastering concepts like linear algebra and backpropagation enhances understanding. Utilizing tools like PyTorch, PyTorch Lightning, and Fabric can simplify managing complex training processes, allowing a seamless transition from single to multi-GPU training with minimal code changes. While grasping the basics is essential, leveraging PyTorch functionalities for gradient calculations and optimization can streamline training for intricate models like LLMs, reducing the manual workload and enhancing efficiency.
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