diff --git a/ch07/01_main-chapter-code/ch07.ipynb b/ch07/01_main-chapter-code/ch07.ipynb index a4652a25..869f12c6 100644 --- a/ch07/01_main-chapter-code/ch07.ipynb +++ b/ch07/01_main-chapter-code/ch07.ipynb @@ -2743,6 +2743,23 @@ "- The [./load-finetuned-model.ipynb](./load-finetuned-model.ipynb) notebook illustrates how to load the finetuned model in a new session\n", "- You can find the exercise solutions in [./exercise-solutions.ipynb](./exercise-solutions.ipynb)" ] + }, + { + "cell_type": "markdown", + "id": "b9cc51ec-e06c-4470-b626-48401a037851", + "metadata": {}, + "source": [ + "## What's next?\n", + "\n", + "- Congrats on completing the book; in case you are looking for additional resources, I added several bonus sections to this GitHub repository that you might find interesting\n", + "- The complete list of bonus materials can be viewed in the main README's [Bonus Material](https://github.com/rasbt/LLMs-from-scratch?tab=readme-ov-file#bonus-material) section\n", + "- To highlight a few of my favorites:\n", + " 1. [Direct Preference Optimization (DPO) for LLM Alignment (From Scratch)](../04_preference-tuning-with-dpo/dpo-from-scratch.ipynb) implements a popular preference tuning mechanism to align the model from this chapter more closely with human preferences\n", + " 2. [Llama 3.2 From Scratch (A Standalone Notebook)](../../ch05/07_gpt_to_llama/standalone-llama32.ipynb), a from-scratch implementation of Meta AI's popular Llama 3.2, including loading the official pretrained weights; if you are up to some additional experiments, you can replace the `GPTModel` model in each of the chapters with the `Llama3Model` class (it should work as a 1:1 replacement)\n", + " 3. [Converting GPT to Llama](../../ch05/07_gpt_to_llama) contains code with step-by-step guides that explain the differences between GPT-2 and the various Llama models\n", + " 4. [Understanding the Difference Between Embedding Layers and Linear Layers](../../ch02/03_bonus_embedding-vs-matmul/embeddings-and-linear-layers.ipynb) is a conceptual explanation illustrating that the `Embedding` layer in PyTorch, which we use at the input stage of an LLM, is mathematically equivalent to a linear layer applied to one-hot encoded data\n", + "- Happy further reading!" + ] } ], "metadata": {