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@ -51,6 +51,7 @@ You can alternatively view this and other files on GitHub at [https://github.com
> [!TIP]
> If you're seeking guidance on installing Python and Python packages and setting up your code environment, I suggest reading the [README.md](setup/README.md) file located in the [setup](setup) directory.
<br>
<br>
[![Code tests (Linux)](https://github.com/rasbt/LLMs-from-scratch/actions/workflows/basic-tests-linux.yml/badge.svg)](https://github.com/rasbt/LLMs-from-scratch/actions/workflows/basic-tests-linux.yml)
@ -65,17 +66,17 @@ You can alternatively view this and other files on GitHub at [https://github.com
|------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------|-------------------------------|
| [Setup recommendations](setup) | - | - |
| Ch 1: Understanding Large Language Models | No code | - |
| Ch 2: Working with Text Data | - [ch02.ipynb](https://nbviewer.org/github/rasbt/LLMs-from-scratch/blob/main/ch02/01_main-chapter-code/ch02.ipynb)<br/>- [dataloader.ipynb](https://nbviewer.org/github/rasbt/LLMs-from-scratch/blob/main/ch02/01_main-chapter-code/dataloader.ipynb) (summary)<br/>- [exercise-solutions.ipynb](https://nbviewer.org/github/rasbt/LLMs-from-scratch/blob/main/ch02/01_main-chapter-code/exercise-solutions.ipynb) | [./ch02](./ch02) |
| Ch 3: Coding Attention Mechanisms | - [ch03.ipynb](https://nbviewer.org/github/rasbt/LLMs-from-scratch/blob/main/ch03/01_main-chapter-code/ch03.ipynb)<br/>- [multihead-attention.ipynb](https://nbviewer.org/github/rasbt/LLMs-from-scratch/blob/main/ch03/01_main-chapter-code/multihead-attention.ipynb) (summary) <br/>- [exercise-solutions.ipynb](https://nbviewer.org/github/rasbt/LLMs-from-scratch/blob/main/ch03/01_main-chapter-code/exercise-solutions.ipynb)| [./ch03](./ch03) |
| Ch 4: Implementing a GPT Model from Scratch | - [ch04.ipynb](https://nbviewer.org/github/rasbt/LLMs-from-scratch/blob/main/ch04/01_main-chapter-code/ch04.ipynb)<br/>- [gpt.py](https://nbviewer.org/github/rasbt/LLMs-from-scratch/blob/main/ch04/01_main-chapter-code/gpt.py) (summary)<br/>- [exercise-solutions.ipynb](https://nbviewer.org/github/rasbt/LLMs-from-scratch/blob/main/ch04/01_main-chapter-code/exercise-solutions.ipynb) | [./ch04](./ch04) |
| Ch 5: Pretraining on Unlabeled Data | - [ch05.ipynb](https://nbviewer.org/github/rasbt/LLMs-from-scratch/blob/main/ch05/01_main-chapter-code/ch05.ipynb)<br/>- [gpt_train.py](https://nbviewer.org/github/rasbt/LLMs-from-scratch/blob/main/ch05/01_main-chapter-code/gpt_train.py) (summary) <br/>- [gpt_generate.py](https://nbviewer.org/github/rasbt/LLMs-from-scratch/blob/main/ch05/01_main-chapter-code/gpt_generate.py) (summary) <br/>- [exercise-solutions.ipynb](https://nbviewer.org/github/rasbt/LLMs-from-scratch/blob/main/ch05/01_main-chapter-code/exercise-solutions.ipynb) | [./ch05](./ch05) |
| Ch 6: Finetuning for Text Classification | - [ch06.ipynb](https://nbviewer.org/github/rasbt/LLMs-from-scratch/blob/main/ch06/01_main-chapter-code/ch06.ipynb) <br/>- [gpt_class_finetune.py](https://nbviewer.org/github/rasbt/LLMs-from-scratch/blob/main/ch06/01_main-chapter-code/gpt_class_finetune.py) <br/>- [exercise-solutions.ipynb](https://nbviewer.org/github/rasbt/LLMs-from-scratch/blob/main/ch06/01_main-chapter-code/exercise-solutions.ipynb) | [./ch06](./ch06) |
| Ch 7: Finetuning to Follow Instructions | - [ch07.ipynb](https://nbviewer.org/github/rasbt/LLMs-from-scratch/blob/main/ch07/01_main-chapter-code/ch07.ipynb)<br/>- [gpt_instruction_finetuning.py](https://nbviewer.org/github/rasbt/LLMs-from-scratch/blob/main/ch07/01_main-chapter-code/gpt_instruction_finetuning.py) (summary)<br/>- [ollama_evaluate.py](https://nbviewer.org/github/rasbt/LLMs-from-scratch/blob/main/ch07/01_main-chapter-code/ollama_evaluate.py) (summary)<br/>- [exercise-solutions.ipynb](https://nbviewer.org/github/rasbt/LLMs-from-scratch/blob/main/ch07/01_main-chapter-code/exercise-solutions.ipynb) | [./ch07](./ch07) |
| Appendix A: Introduction to PyTorch | - [code-part1.ipynb](https://nbviewer.org/github/rasbt/LLMs-from-scratch/blob/main/appendix-A/01_main-chapter-code/code-part1.ipynb)<br/>- [code-part2.ipynb](https://nbviewer.org/github/rasbt/LLMs-from-scratch/blob/main/appendix-A/01_main-chapter-code/code-part2.ipynb)<br/>- [DDP-script.py](https://nbviewer.org/github/rasbt/LLMs-from-scratch/blob/main/appendix-A/01_main-chapter-code/DDP-script.py)<br/>- [exercise-solutions.ipynb](https://nbviewer.org/github/rasbt/LLMs-from-scratch/blob/main/appendix-A/01_main-chapter-code/exercise-solutions.ipynb) | [./appendix-A](./appendix-A) |
| Ch 2: Working with Text Data | - [ch02.ipynb](ch02/01_main-chapter-code/ch02.ipynb)<br/>- [dataloader.ipynb](ch02/01_main-chapter-code/dataloader.ipynb) (summary)<br/>- [exercise-solutions.ipynb](ch02/01_main-chapter-code/exercise-solutions.ipynb) | [./ch02](./ch02) |
| Ch 3: Coding Attention Mechanisms | - [ch03.ipynb](ch03/01_main-chapter-code/ch03.ipynb)<br/>- [multihead-attention.ipynb](ch03/01_main-chapter-code/multihead-attention.ipynb) (summary) <br/>- [exercise-solutions.ipynb](ch03/01_main-chapter-code/exercise-solutions.ipynb)| [./ch03](./ch03) |
| Ch 4: Implementing a GPT Model from Scratch | - [ch04.ipynb](ch04/01_main-chapter-code/ch04.ipynb)<br/>- [gpt.py](ch04/01_main-chapter-code/gpt.py) (summary)<br/>- [exercise-solutions.ipynb](ch04/01_main-chapter-code/exercise-solutions.ipynb) | [./ch04](./ch04) |
| Ch 5: Pretraining on Unlabeled Data | - [ch05.ipynb](ch05/01_main-chapter-code/ch05.ipynb)<br/>- [gpt_train.py](ch05/01_main-chapter-code/gpt_train.py) (summary) <br/>- [gpt_generate.py](ch05/01_main-chapter-code/gpt_generate.py) (summary) <br/>- [exercise-solutions.ipynb](ch05/01_main-chapter-code/exercise-solutions.ipynb) | [./ch05](./ch05) |
| Ch 6: Finetuning for Text Classification | - [ch06.ipynb](ch06/01_main-chapter-code/ch06.ipynb) <br/>- [gpt_class_finetune.py](ch06/01_main-chapter-code/gpt_class_finetune.py) <br/>- [exercise-solutions.ipynb](ch06/01_main-chapter-code/exercise-solutions.ipynb) | [./ch06](./ch06) |
| Ch 7: Finetuning to Follow Instructions | - [ch07.ipynb](ch07/01_main-chapter-code/ch07.ipynb)<br/>- [gpt_instruction_finetuning.py](ch07/01_main-chapter-code/gpt_instruction_finetuning.py) (summary)<br/>- [ollama_evaluate.py](ch07/01_main-chapter-code/ollama_evaluate.py) (summary)<br/>- [exercise-solutions.ipynb](ch07/01_main-chapter-code/exercise-solutions.ipynb) | [./ch07](./ch07) |
| Appendix A: Introduction to PyTorch | - [code-part1.ipynb](appendix-A/01_main-chapter-code/code-part1.ipynb)<br/>- [code-part2.ipynb](appendix-A/01_main-chapter-code/code-part2.ipynb)<br/>- [DDP-script.py](appendix-A/01_main-chapter-code/DDP-script.py)<br/>- [exercise-solutions.ipynb](appendix-A/01_main-chapter-code/exercise-solutions.ipynb) | [./appendix-A](./appendix-A) |
| Appendix B: References and Further Reading | No code | - |
| Appendix C: Exercise Solutions | No code | - |
| Appendix D: Adding Bells and Whistles to the Training Loop | - [appendix-D.ipynb](https://nbviewer.org/github/rasbt/LLMs-from-scratch/blob/main/appendix-D/01_main-chapter-code/appendix-D.ipynb) | [./appendix-D](./appendix-D) |
| Appendix E: Parameter-efficient Finetuning with LoRA | - [appendix-E.ipynb](https://nbviewer.org/github/rasbt/LLMs-from-scratch/blob/main/appendix-E/01_main-chapter-code/appendix-E.ipynb) | [./appendix-E](./appendix-E) |
| Appendix D: Adding Bells and Whistles to the Training Loop | - [appendix-D.ipynb](appendix-D/01_main-chapter-code/appendix-D.ipynb) | [./appendix-D](./appendix-D) |
| Appendix E: Parameter-efficient Finetuning with LoRA | - [appendix-E.ipynb](appendix-E/01_main-chapter-code/appendix-E.ipynb) | [./appendix-E](./appendix-E) |
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