Pretraining GPT from Scratch: Training Loop, Loss & Decoding Strategies | Chapter 5

onepagecode · Advanced ·🧠 Large Language Models ·2w ago

About this lesson

Download the source code from here: https://onepagecode.substack.com/ In Chapter 5 of "Build a Large Language Model (From Scratch)", we finally train the GPT model we built in previous chapters. We start by implementing a proper training loop and evaluation functions to track training and validation loss. We then explore advanced text generation techniques, including temperature scaling and top-k sampling, to produce more diverse and creative outputs. Finally, we load the official pretrained weights from OpenAI’s GPT-2 model into our custom implementation, giving us a powerful base model ready for fine-tuning. What you’ll learn in this chapter: • How to compute training and validation loss for LLMs • Implementing a full training loop for GPT models • Saving and loading model checkpoints • Temperature scaling for controlling output randomness • Top-k sampling to improve text quality and diversity • Loading official OpenAI GPT-2 pretrained weights • Generating coherent text with a pretrained model By the end of this chapter, you will have a fully functional, pretrained GPT model that you can use as a starting point for fine-tuning on custom tasks. #PretrainingLLM #GPTTraining #TemperatureScaling #TopkSampling #LoadGPT2Weights #BuildLLMFromScratch #Chapter5

Original Description

Download the source code from here: https://onepagecode.substack.com/ In Chapter 5 of "Build a Large Language Model (From Scratch)", we finally train the GPT model we built in previous chapters. We start by implementing a proper training loop and evaluation functions to track training and validation loss. We then explore advanced text generation techniques, including temperature scaling and top-k sampling, to produce more diverse and creative outputs. Finally, we load the official pretrained weights from OpenAI’s GPT-2 model into our custom implementation, giving us a powerful base model ready for fine-tuning. What you’ll learn in this chapter: • How to compute training and validation loss for LLMs • Implementing a full training loop for GPT models • Saving and loading model checkpoints • Temperature scaling for controlling output randomness • Top-k sampling to improve text quality and diversity • Loading official OpenAI GPT-2 pretrained weights • Generating coherent text with a pretrained model By the end of this chapter, you will have a fully functional, pretrained GPT model that you can use as a starting point for fine-tuning on custom tasks. #PretrainingLLM #GPTTraining #TemperatureScaling #TopkSampling #LoadGPT2Weights #BuildLLMFromScratch #Chapter5

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