LLMs-from-scratch/ch05

Chapter 5: Pretraining on Unlabeled Data

 

Main Chapter Code

• 01_main-chapter-code contains the main chapter code

 

Bonus Materials

• 02_alternative_weight_loading contains code to load the GPT model weights from alternative places in case the model weights become unavailable from OpenAI
• 03_bonus_pretraining_on_gutenberg contains code to pretrain the LLM longer on the whole corpus of books from Project Gutenberg
• 04_learning_rate_schedulers contains code implementing a more sophisticated training function including learning rate schedulers and gradient clipping
• 05_bonus_hparam_tuning contains an optional hyperparameter tuning script
• 06_user_interface implements an interactive user interface to interact with the pretrained LLM
• 08_memory_efficient_weight_loading contains a bonus notebook showing how to load model weights via PyTorch's load_state_dict method more efficiently
• 09_extending-tokenizers contains a from-scratch implementation of the GPT-2 BPE tokenizer
• 10_llm-training-speed shows PyTorch performance tips to improve the LLM training speed

 

LLM Architectures From Scratch

 

• 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
• 11_qwen3 A from-scratch implementation of Qwen3 0.6B and Qwen3 30B-A3B (Mixture-of-Experts) including code to load the pretrained weights of the base, reasoning, and coding model variants
• 12_gemma3 A from-scratch implementation of Gemma 3 270M and alternative with KV cache, including code to load the pretrained weights
• 13_olmo3 A from-scratch implementation of Olmo 3 7B and 32B (Base, Instruct, and Think variants) and alternative with KV cache, including code to load the pretrained weights

 

Code-Along Video for This Chapter