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Sebastian Raschka 2024-11-07 20:12:59 -06:00 committed by GitHub
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"- The [./load-finetuned-model.ipynb](./load-finetuned-model.ipynb) notebook illustrates how to load the finetuned model in a new session\n", "- 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)" "- 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!"
]
} }
], ],
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