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LLMs-from-scratch/ch06/01_main-chapter-code
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Mingyuan Xu f77c376b05
Run generate example in ch06 optionally on GPU (#352) 
* model.to("cuda")

model.to("cuda")

* update device placement

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Co-authored-by: rasbt <mail@sebastianraschka.com>
2024-09-13 08:01:52 -05:00
..
ch06.ipynb
Note about MPS in ch06 and ch07 (#325)
2024-08-19 08:11:33 -05:00
exercise-solutions.ipynb
minor markdown fixes (#236)
2024-06-21 13:55:34 -05:00
gpt_class_finetune.py
Add standalone finetuning and evaluation scripts for chapter 7 (#234)
2024-06-21 05:23:24 -05:00
gpt_download.py
Add download help message (#274)
2024-07-19 08:29:29 -05:00
load-finetuned-model.ipynb
Run generate example in ch06 optionally on GPU (#352)
2024-09-13 08:01:52 -05:00
previous_chapters.py
fixed num_workers (#229)
2024-06-19 17:36:46 -05:00
README.md
show how to use the finetuned model
2024-07-09 06:43:26 -07:00
tests.py
Add standalone finetuning and evaluation scripts for chapter 7 (#234)
2024-06-21 05:23:24 -05:00

README.md

Chapter 6: Finetuning for Classification

Main Chapter Code

  • ch06.ipynb contains all the code as it appears in the chapter
  • previous_chapters.py is a Python module that contains the GPT model we coded and trained in previous chapters, alongside many utility functions, which we reuse in this chapter
  • gpt_download.py contains the utility functions for downloading the pretrained GPT model weights
  • exercise-solutions.ipynb contains the exercise solutions for this chapter

Optional Code

load-finetuned-model.ipynb is a standalone Jupyter notebook to load the finetuned model we created in this chapter

  • gpt_class_finetune.py is a standalone Python script file with the code that we implemented in ch06.ipynb to finetune the GPT model (you can think of it as a chapter summary)