Prediction. Error. Update. Repeat.
A fictional cat supervises a simplified next-token pretraining lesson. A text-generating model predicts a token, the actual continuation in the training text provides the target, a loss measures the prediction error, backpropagation computes gradients, and an optimizer updates weights. GPUs perform parallel calculations and memory holds weights and training state. The pizza and mat example and any displayed numbers are invented teaching illustrations, not a real model run or a claim that pizza is impossible in natural language. Original photoreal concept imagery and native educational animation; AI-generated English narration. Separate English and Traditional Chinese captions. Next: learning to follow instructions.
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Sources:
https://huggingface.co/learn/llm-course/chapter1/4
https://docs.pytorch.org/tutorials/beginner/basics/optimization_tutorial.html
https://huggingface.co/docs/transformers/model_memory_anatomy
https://docs.pytorch.org/docs/2.14/generated/torch.nn.CrossEntropyLoss.html
https://docs.nvidia.com/cuda/cuda-programming-guide/01-introduction/programming-model.html
#AI #LLM #MachineLearning #Cats #Shorts
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