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@ -65,17 +65,17 @@ You can alternatively view this and other files on GitHub at [https://github.com
|------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------|-------------------------------| |------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------|-------------------------------|
| [Setup recommendations](setup) | - | - | | [Setup recommendations](setup) | - | - |
| Ch 1: Understanding Large Language Models | No code | - | | Ch 1: Understanding Large Language Models | No code | - |
| 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 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](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 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](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 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](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 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](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 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](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) | | 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](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 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) |
| Appendix B: References and Further Reading | No code | - | | Appendix B: References and Further Reading | No code | - |
| Appendix C: Exercise Solutions | No code | - | | Appendix C: Exercise Solutions | No code | - |
| 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 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](appendix-E/01_main-chapter-code/appendix-E.ipynb) | [./appendix-E](./appendix-E) | | 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) |
<br> <br>
@ -102,28 +102,28 @@ Several folders contain optional materials as a bonus for interested readers:
- [Installing Python Packages and Libraries Used In This Book](setup/02_installing-python-libraries) - [Installing Python Packages and Libraries Used In This Book](setup/02_installing-python-libraries)
- [Docker Environment Setup Guide](setup/03_optional-docker-environment) - [Docker Environment Setup Guide](setup/03_optional-docker-environment)
- **Chapter 2:** - **Chapter 2:**
- [Comparing Various Byte Pair Encoding (BPE) Implementations](ch02/02_bonus_bytepair-encoder) - [Comparing Various Byte Pair Encoding (BPE) Implementations](https://nbviewer.org/github/rasbt/LLMs-from-scratch/blob/main/ch02/02_bonus_bytepair-encoder)
- [Understanding the Difference Between Embedding Layers and Linear Layers](ch02/03_bonus_embedding-vs-matmul) - [Understanding the Difference Between Embedding Layers and Linear Layers](https://nbviewer.org/github/rasbt/LLMs-from-scratch/blob/main/ch02/03_bonus_embedding-vs-matmul)
- [Dataloader Intuition with Simple Numbers](ch02/04_bonus_dataloader-intuition) - [Dataloader Intuition with Simple Numbers](https://nbviewer.org/github/rasbt/LLMs-from-scratch/blob/main/ch02/04_bonus_dataloader-intuition)
- **Chapter 3:** - **Chapter 3:**
- [Comparing Efficient Multi-Head Attention Implementations](ch03/02_bonus_efficient-multihead-attention/mha-implementations.ipynb) - [Comparing Efficient Multi-Head Attention Implementations](https://nbviewer.org/github/rasbt/LLMs-from-scratch/blob/main/ch03/02_bonus_efficient-multihead-attention/mha-implementations.ipynb)
- [Understanding PyTorch Buffers](ch03/03_understanding-buffers/understanding-buffers.ipynb) - [Understanding PyTorch Buffers](https://nbviewer.org/github/rasbt/LLMs-from-scratch/blob/main/ch03/03_understanding-buffers/understanding-buffers.ipynb)
- **Chapter 4:** - **Chapter 4:**
- [FLOPS Analysis](ch04/02_performance-analysis/flops-analysis.ipynb) - [FLOPS Analysis](https://nbviewer.org/github/rasbt/LLMs-from-scratch/blob/main/ch04/02_performance-analysis/flops-analysis.ipynb)
- **Chapter 5:** - **Chapter 5:**
- [Alternative Weight Loading from Hugging Face Model Hub using Transformers](ch05/02_alternative_weight_loading/weight-loading-hf-transformers.ipynb) - [Alternative Weight Loading from Hugging Face Model Hub using Transformers](https://nbviewer.org/github/rasbt/LLMs-from-scratch/blob/main/ch05/02_alternative_weight_loading/weight-loading-hf-transformers.ipynb)
- [Pretraining GPT on the Project Gutenberg Dataset](ch05/03_bonus_pretraining_on_gutenberg) - [Pretraining GPT on the Project Gutenberg Dataset](https://nbviewer.org/github/rasbt/LLMs-from-scratch/blob/main/ch05/03_bonus_pretraining_on_gutenberg)
- [Adding Bells and Whistles to the Training Loop](ch05/04_learning_rate_schedulers) - [Adding Bells and Whistles to the Training Loop](https://nbviewer.org/github/rasbt/LLMs-from-scratch/blob/main/ch05/04_learning_rate_schedulers)
- [Optimizing Hyperparameters for Pretraining](ch05/05_bonus_hparam_tuning) - [Optimizing Hyperparameters for Pretraining](https://nbviewer.org/github/rasbt/LLMs-from-scratch/blob/main/ch05/05_bonus_hparam_tuning)
- **Chapter 6:** - **Chapter 6:**
- [Additional experiments finetuning different layers and using larger models](ch06/02_bonus_additional-experiments) - [Additional experiments finetuning different layers and using larger models](https://nbviewer.org/github/rasbt/LLMs-from-scratch/blob/main/ch06/02_bonus_additional-experiments)
- [Finetuning different models on 50k IMDB movie review dataset](ch06/03_bonus_imdb-classification) - [Finetuning different models on 50k IMDB movie review dataset](https://nbviewer.org/github/rasbt/LLMs-from-scratch/blob/main/ch06/03_bonus_imdb-classification)
- **Chapter 7:** - **Chapter 7:**
- [Dataset Utilities for Finding Near Duplicates and Creating Passive Voice Entries](ch07/02_dataset-utilities) - [Dataset Utilities for Finding Near Duplicates and Creating Passive Voice Entries](https://nbviewer.org/github/rasbt/LLMs-from-scratch/blob/main/ch07/02_dataset-utilities)
- [Evaluating Instruction Responses Using the OpenAI API and Ollama](ch07/03_model-evaluation) - [Evaluating Instruction Responses Using the OpenAI API and Ollama](https://nbviewer.org/github/rasbt/LLMs-from-scratch/blob/main/ch07/03_model-evaluation)
- [Generating a Dataset for Instruction Finetuning](ch07/05_dataset-generation) - [Generating a Dataset for Instruction Finetuning](https://nbviewer.org/github/rasbt/LLMs-from-scratch/blob/main/ch07/05_dataset-generation)
- [Generating a Preference Dataset with Llama 3.1 70B and Ollama](ch07/04_preference-tuning-with-dpo/create-preference-data-ollama.ipynb) - [Generating a Preference Dataset with Llama 3.1 70B and Ollama](https://nbviewer.org/github/rasbt/LLMs-from-scratch/blob/main/ch07/04_preference-tuning-with-dpo/create-preference-data-ollama.ipynb)
- [Direct Preference Optimization (DPO) for LLM Alignment](ch07/04_preference-tuning-with-dpo/dpo-from-scratch.ipynb) - [Direct Preference Optimization (DPO) for LLM Alignment](https://nbviewer.org/github/rasbt/LLMs-from-scratch/blob/main/ch07/04_preference-tuning-with-dpo/dpo-from-scratch.ipynb)
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