Had there been no PyTorch, there would have been no LLM. Sounds a bit exaggerated, but there’s little doubt about the big role this deep learning framework has played in the development of AI technology. It provides an intuitive and flexible method for constructing neural networks, making it an ideal option for deep learning experiments and prototyping. Besides, the framework is distinctive for its excellent support for GPUs and its use of reverse-mode auto-differentiation, which enables computation graphs to be modified on the fly.
The wide popularity of the framework can be gauged from its performance on Hugging Face. In 2022, Hugging Face saw the addition of a remarkable 45,000 PyTorch-exclusive models, whereas only 4,000 new TensorFlow-exclusive models were introduced to the platform. This led to a significant 92% of models on Hugging Face being PyTorch-exclusive, leaving just 8% for TensorFlow.
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