Chapter 7: Finetuning to Follow Instructions

Main Chapter Code

• ch07.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 instruction finetuned model we created in this chapter

• gpt_instruction_finetuning.py is a standalone Python script to instruction finetune the model as described in the main chapter (think of it as a chapter summary focused on the finetuning parts)

Usage:

python gpt_instruction_finetuning.py

matplotlib version: 3.9.0
tiktoken version: 0.7.0
torch version: 2.3.1
tqdm version: 4.66.4
tensorflow version: 2.16.1
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Training set length: 935
Validation set length: 55
Test set length: 110
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Device: cpu
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File already exists and is up-to-date: gpt2/355M/checkpoint
File already exists and is up-to-date: gpt2/355M/encoder.json
File already exists and is up-to-date: gpt2/355M/hparams.json
File already exists and is up-to-date: gpt2/355M/model.ckpt.data-00000-of-00001
File already exists and is up-to-date: gpt2/355M/model.ckpt.index
File already exists and is up-to-date: gpt2/355M/model.ckpt.meta
File already exists and is up-to-date: gpt2/355M/vocab.bpe
Loaded model: gpt2-medium (355M)
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Initial losses
   Training loss: 3.839039182662964
   Validation loss: 3.7619192123413088
Ep 1 (Step 000000): Train loss 2.611, Val loss 2.668
Ep 1 (Step 000005): Train loss 1.161, Val loss 1.131
Ep 1 (Step 000010): Train loss 0.939, Val loss 0.973
...
Training completed in 15.66 minutes.
Plot saved as loss-plot-standalone.pdf
--------------------------------------------------
Generating responses
100%|█████████████████████████████████████████████████████████| 110/110 [06:57<00:00,  3.80s/it]
Responses saved as instruction-data-with-response-standalone.json
Model saved as gpt2-medium355M-sft-standalone.pth

• ollama_evaluate.py is a standalone Python script to evaluate the responses of the finetuned model as described in the main chapter (think of it as a chapter summary focused on the evaluation parts)

Usage:

python ollama_evaluate.py --file_path instruction-data-with-response-standalone.json

Ollama running: True
Scoring entries: 100%|███████████████████████████████████████| 110/110 [01:08<00:00,  1.62it/s]
Number of scores: 110 of 110
Average score: 51.75

• exercise_experiments.py is an optional scropt that implements the exercise solutions; for more details see exercise-solutions.ipynb