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LLMs-from-scratch/ch05
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Daniel Kleine 81eed9afe2
updated RoPE statement (#423) 
* updated RoPE statement

* updated .gitignore

* Update ch05/07_gpt_to_llama/converting-gpt-to-llama2.ipynb

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Co-authored-by: Sebastian Raschka <mail@sebastianraschka.com>
2024-10-30 08:00:08 -05:00
..
01_main-chapter-code
llama note
2024-09-26 07:41:11 -05:00
02_alternative_weight_loading
fixed num_workers (#229)
2024-06-19 17:36:46 -05:00
03_bonus_pretraining_on_gutenberg
Update README.md
2024-08-10 07:54:51 -05:00
04_learning_rate_schedulers
Add and link bonus material (#84)
2024-03-23 07:27:43 -05:00
05_bonus_hparam_tuning
total training iters may equal to warmup_iters (#301)
2024-08-06 07:10:05 -05:00
06_user_interface
Add user interface to ch06 and ch07 (#366)
2024-09-21 20:33:00 -05:00
07_gpt_to_llama
updated RoPE statement (#423)
2024-10-30 08:00:08 -05:00
08_memory_efficient_weight_loading
update mmap section
2024-10-14 14:27:19 -05:00
README.md
Memory efficient weight loading (#401)
2024-10-14 10:30:25 -05:00

README.md

Chapter 5: Pretraining on Unlabeled Data

 

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
  • 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
  • 08_memory_efficient_weight_loading contains a bonus notebook showing how to load model weights via PyTorch's load_state_dict method more efficiently