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@@ -125,7 +125,7 @@ The code in the main chapters of this book is designed to run on conventional la
[*Build A Reasoning Model (From Scratch)*](https://mng.bz/lZ5B), while a standalone book, can be considered as a sequel to *Build A Large Language Model (From Scratch)*. [*Build A Reasoning Model (From Scratch)*](https://mng.bz/lZ5B), while a standalone book, can be considered as a sequel to *Build A Large Language Model (From Scratch)*.
It starts with a pretrained model and implements different reasoning approaches, including inference-time scaling, reinforcement learning, and distillation, to improve the model's reasoning capabilities. It starts with a pretrained model and implements different reasoning approaches, including inference-time scaling, reinforcement learning, and distillation, to improve the model's reasoning capabilities.
Similar to *Build A Large Language Model (From Scratch)*, [*Build A Reasoning Model (From Scratch)*](https://mng.bz/lZ5B) takes a hands-on approach implementing these methods from scratch. Similar to *Build A Large Language Model (From Scratch)*, [*Build A Reasoning Model (From Scratch)*](https://mng.bz/lZ5B) takes a hands-on approach implementing these methods from scratch.
@@ -146,35 +146,36 @@ In addition to the code exercises, you can download a free 170-page PDF titled
<a href="https://www.manning.com/books/test-yourself-on-build-a-large-language-model-from-scratch"><img src="https://sebastianraschka.com/images/LLMs-from-scratch-images/test-yourself-cover.jpg?123" width="150px"></a> <a href="https://www.manning.com/books/test-yourself-on-build-a-large-language-model-from-scratch"><img src="https://sebastianraschka.com/images/LLMs-from-scratch-images/test-yourself-cover.jpg?123" width="150px"></a>
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## Bonus Material ## Bonus Material
Several folders contain optional materials as a bonus for interested readers: Several folders contain optional materials as a bonus for interested readers:
- **Setup** - **Setup**
- [Python Setup Tips](setup/01_optional-python-setup-preferences) - [Python Setup Tips](setup/01_optional-python-setup-preferences)
- [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: Working with text data**
- **Chapter 2: Working With Text Data**
- [Byte Pair Encoding (BPE) Tokenizer From Scratch](ch02/05_bpe-from-scratch/bpe-from-scratch-simple.ipynb) - [Byte Pair Encoding (BPE) Tokenizer From Scratch](ch02/05_bpe-from-scratch/bpe-from-scratch-simple.ipynb)
- [Comparing Various Byte Pair Encoding (BPE) Implementations](ch02/02_bonus_bytepair-encoder) - [Comparing Various Byte Pair Encoding (BPE) Implementations](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](ch02/03_bonus_embedding-vs-matmul)
- [Dataloader Intuition with Simple Numbers](ch02/04_bonus_dataloader-intuition) - [Dataloader Intuition With Simple Numbers](ch02/04_bonus_dataloader-intuition)
- **Chapter 3: Coding attention mechanisms**
- **Chapter 3: Coding Attention Mechanisms**
- [Comparing Efficient Multi-Head Attention Implementations](ch03/02_bonus_efficient-multihead-attention/mha-implementations.ipynb) - [Comparing Efficient Multi-Head Attention Implementations](ch03/02_bonus_efficient-multihead-attention/mha-implementations.ipynb)
- [Understanding PyTorch Buffers](ch03/03_understanding-buffers/understanding-buffers.ipynb) - [Understanding PyTorch Buffers](ch03/03_understanding-buffers/understanding-buffers.ipynb)
- **Chapter 4: Implementing a GPT model from scratch**
- [FLOPS Analysis](ch04/02_performance-analysis/flops-analysis.ipynb) - **Chapter 4: Implementing a GPT Model From Scratch**
- [FLOPs Analysis](ch04/02_performance-analysis/flops-analysis.ipynb)
- [KV Cache](ch04/03_kv-cache) - [KV Cache](ch04/03_kv-cache)
- [Attention alternatives](ch04/#attention-alternatives) - [Attention Alternatives](ch04/#attention-alternatives)
- [Grouped-Query Attention](ch04/04_gqa) - [Grouped-Query Attention](ch04/04_gqa)
- [Multi-Head Latent Attention](ch04/05_mla) - [Multi-Head Latent Attention](ch04/05_mla)
- [Sliding Window Attention](ch04/06_swa) - [Sliding Window Attention](ch04/06_swa)
- [Gated DeltaNet](ch04/08_deltanet) - [Gated DeltaNet](ch04/08_deltanet)
- [Mixture-of-Experts (MoE)](ch04/07_moe) - [Mixture-of-Experts (MoE)](ch04/07_moe)
- **Chapter 5: Pretraining on unlabeled data:**
- **Chapter 5: Pretraining on Unlabeled Data**
- [Alternative Weight Loading Methods](ch05/02_alternative_weight_loading/) - [Alternative Weight Loading Methods](ch05/02_alternative_weight_loading/)
- [Pretraining GPT on the Project Gutenberg Dataset](ch05/03_bonus_pretraining_on_gutenberg) - [Pretraining GPT on the Project Gutenberg Dataset](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](ch05/04_learning_rate_schedulers)
@@ -184,32 +185,35 @@ Several folders contain optional materials as a bonus for interested readers:
- [Llama 3.2 From Scratch](ch05/07_gpt_to_llama/standalone-llama32.ipynb) - [Llama 3.2 From Scratch](ch05/07_gpt_to_llama/standalone-llama32.ipynb)
- [Qwen3 Dense and Mixture-of-Experts (MoE) From Scratch](ch05/11_qwen3/) - [Qwen3 Dense and Mixture-of-Experts (MoE) From Scratch](ch05/11_qwen3/)
- [Gemma 3 From Scratch](ch05/12_gemma3/) - [Gemma 3 From Scratch](ch05/12_gemma3/)
- [Memory-efficient Model Weight Loading](ch05/08_memory_efficient_weight_loading/memory-efficient-state-dict.ipynb) - [Memory-Efficient Model Weight Loading](ch05/08_memory_efficient_weight_loading/memory-efficient-state-dict.ipynb)
- [Extending the Tiktoken BPE Tokenizer with New Tokens](ch05/09_extending-tokenizers/extend-tiktoken.ipynb) - [Extending the Tiktoken BPE Tokenizer With New Tokens](ch05/09_extending-tokenizers/extend-tiktoken.ipynb)
- [PyTorch Performance Tips for Faster LLM Training](ch05/10_llm-training-speed) - [PyTorch Performance Tips for Faster LLM Training](ch05/10_llm-training-speed)
- **Chapter 6: Finetuning for classification**
- [Additional experiments finetuning different layers and using larger models](ch06/02_bonus_additional-experiments) - **Chapter 6: Finetuning for Classification**
- [Finetuning different models on 50k IMDb movie review dataset](ch06/03_bonus_imdb-classification) - [Additional Experiments Finetuning Different Layers and Using Larger Models](ch06/02_bonus_additional-experiments)
- [Building a User Interface to Interact With the GPT-based Spam Classifier](ch06/04_user_interface) - [Finetuning Different Models on 50k IMDb Movie Review Dataset](ch06/03_bonus_imdb-classification)
- **Chapter 7: Finetuning to follow instructions** - [Building a User Interface to Interact With the GPT-Based Spam Classifier](ch06/04_user_interface)
- **Chapter 7: Finetuning to Follow Instructions**
- [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](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](ch07/03_model-evaluation)
- [Generating a Dataset for Instruction Finetuning](ch07/05_dataset-generation/llama3-ollama.ipynb) - [Generating a Dataset for Instruction Finetuning](ch07/05_dataset-generation/llama3-ollama.ipynb)
- [Improving a Dataset for Instruction Finetuning](ch07/05_dataset-generation/reflection-gpt4.ipynb) - [Improving a Dataset for Instruction Finetuning](ch07/05_dataset-generation/reflection-gpt4.ipynb)
- [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](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](ch07/04_preference-tuning-with-dpo/dpo-from-scratch.ipynb)
- [Building a User Interface to Interact With the Instruction Finetuned GPT Model](ch07/06_user_interface) - [Building a User Interface to Interact With the Instruction-Finetuned GPT Model](ch07/06_user_interface)
More bonus material from the [reasoning from scratch](https://github.com/rasbt/reasoning-from-scratch) repository: More bonus material from the [Reasoning From Scratch](https://github.com/rasbt/reasoning-from-scratch) repository:
- **Qwen3 (from scratch) basics** - **Qwen3 (From Scratch) Basics**
- [Qwen3 source code walkthrough](https://github.com/rasbt/reasoning-from-scratch/blob/main/chC/01_main-chapter-code/chC_main.ipynb) - [Qwen3 Source Code Walkthrough](https://github.com/rasbt/reasoning-from-scratch/blob/main/chC/01_main-chapter-code/chC_main.ipynb)
- [Optimized Qwen3](https://github.com/rasbt/reasoning-from-scratch/tree/main/ch02/03_optimized-LLM) - [Optimized Qwen3](https://github.com/rasbt/reasoning-from-scratch/tree/main/ch02/03_optimized-LLM)
- **Evaluation** - **Evaluation**
- [Verifier-based evaluation (MATH-500)](https://github.com/rasbt/reasoning-from-scratch/tree/main/ch03) - [Verifier-Based Evaluation (MATH-500)](https://github.com/rasbt/reasoning-from-scratch/tree/main/ch03)
- [Multiple-choice evaluation (MMLU)](https://github.com/rasbt/reasoning-from-scratch/blob/main/chF/02_mmlu) - [Multiple-Choice Evaluation (MMLU)](https://github.com/rasbt/reasoning-from-scratch/blob/main/chF/02_mmlu)
- [LLM leaderboard evaluation](https://github.com/rasbt/reasoning-from-scratch/blob/main/chF/03_leaderboards) - [LLM Leaderboard Evaluation](https://github.com/rasbt/reasoning-from-scratch/blob/main/chF/03_leaderboards)
- [LLM-as-a-judge evaluation](https://github.com/rasbt/reasoning-from-scratch/blob/main/chF/04_llm-judge) - [LLM-as-a-Judge Evaluation](https://github.com/rasbt/reasoning-from-scratch/blob/main/chF/04_llm-judge)
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