- ch07.ipynb 包含本章中出现的所有代码
- previous_chapters.py 是一个Python模块，包含我们在前几章中编码和训练的GPT模型以及许多实用功能，我们在本章中重用这些功能
- gpt_download.py 包含下载预训练GPT模型权重的实用函数
- exercise-solutions.ipynb 包含本章的习题解答
- load-finetuned-model.ipynb 是一个独立的Jupyter笔记本，用于加载我们在本章创建的指令微调模型
- gpt_instruction_finetuning.py 是一个独立的Python脚本，用于按本章所述进行指令微调（可以视为本章微调部分的总结）
用法：
python gpt_instruction_finetuning.py
```bash
python gpt_instruction_finetuning.pymatplotlib version: 3.9.0
tiktoken version: 0.7.0
torch version: 2.3.1
tqdm version: 4.66.4
tensorflow version: 2.16.1
--------------------------------------------------
Training set length: 935
Validation set length: 55
Test set length: 110
--------------------------------------------------
Device: cpu
--------------------------------------------------
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)
--------------------------------------------------
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 是一个独立的Python脚本，用于评估微调模型的响应，如本章所述（可以视为本章评估部分的总结）
用法：
python ollama_evaluate.py --file_path instruction-data-with-response-standalone.json
```bash
python ollama_evaluate.py --file_path instruction-data-with-response-standalone.jsonOllama 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 是一个可选的脚本，实现了习题解答；更多细节请见 exercise-solutions.ipynb