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26 lines
1.6 KiB
Markdown
26 lines
1.6 KiB
Markdown
# Chapter 5: Pretraining on Unlabeled Data
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## Main Chapter Code
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- [01_main-chapter-code](01_main-chapter-code) contains the main chapter code
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## Bonus Materials
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- [02_alternative_weight_loading](02_alternative_weight_loading) contains code to load the GPT model weights from alternative places in case the model weights become unavailable from OpenAI
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- [03_bonus_pretraining_on_gutenberg](03_bonus_pretraining_on_gutenberg) contains code to pretrain the LLM longer on the whole corpus of books from Project Gutenberg
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- [04_learning_rate_schedulers](04_learning_rate_schedulers) contains code implementing a more sophisticated training function including learning rate schedulers and gradient clipping
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- [05_bonus_hparam_tuning](05_bonus_hparam_tuning) contains an optional hyperparameter tuning script
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- [06_user_interface](06_user_interface) implements an interactive user interface to interact with the pretrained LLM
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- [07_gpt_to_llama](07_gpt_to_llama) contains a step-by-step guide for converting a GPT architecture implementation to Llama 3.2 and loads pretrained weights from Meta AI
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- [08_memory_efficient_weight_loading](08_memory_efficient_weight_loading) contains a bonus notebook showing how to load model weights via PyTorch's `load_state_dict` method more efficiently
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- [09_extending-tokenizers](09_extending-tokenizers) contains a from-scratch implementation of the GPT-2 BPE tokenizer
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- [10_llm-training-speed](10_llm-training-speed) shows PyTorch performance tips to improve the LLM training speed
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<br>
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<br>
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[](https://www.youtube.com/watch?v=Zar2TJv-sE0) |