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12
ch02/01_main-chapter-code/ch02.ipynb
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@ -1880,10 +1880,18 @@
"id": "8b3293a6-45a5-47cd-aa00-b23e3ca0a73f",
"metadata": {},
"source": [
"**See the [./dataloader.ipynb](./dataloader.ipynb) code notebook**, which is a concise version of the data loader that we implemented in this chapter and will need for training the GPT model in upcoming chapters.\n",
"See the [./dataloader.ipynb](./dataloader.ipynb) code notebook, which is a concise version of the data loader that we implemented in this chapter and will need for training the GPT model in upcoming chapters.\n",
"\n",
"**See [./exercise-solutions.ipynb](./exercise-solutions.ipynb) for the exercise solutions.**"
"See [./exercise-solutions.ipynb](./exercise-solutions.ipynb) for the exercise solutions."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c6aa436b-c9ff-4f01-9a58-5c377b3700a5",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {

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ch03/01_main-chapter-code/ch03.ipynb
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@ -2007,7 +2007,8 @@
"id": "fa3e4113-ffca-432c-b3ec-7a50bd15da25",
"metadata": {},
"source": [
"- See the [./multihead-attention.ipynb](./multihead-attention.ipynb) code notebook, which is a concise version of the data loader (chapter 2) plus the multi-head attention class that we implemented in this chapter and will need for training the GPT model in upcoming chapters"
"- See the [./multihead-attention.ipynb](./multihead-attention.ipynb) code notebook, which is a concise version of the data loader (chapter 2) plus the multi-head attention class that we implemented in this chapter and will need for training the GPT model in upcoming chapters\n",
"- You can find the exercise solutions in [./exercise-solutions.ipynb](./exercise-solutions.ipynb)"
]
}
],

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ch04/01_main-chapter-code/ch04.ipynb
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@ -1480,6 +1480,17 @@
"- Note that the model is untrained; hence the random output texts above\n",
"- We will train the model in the next chapter"
]
},
{
"cell_type": "markdown",
"id": "a35278b6-9e5c-480f-83e5-011a1173648f",
"metadata": {},
"source": [
"## Summary and takeaways\n",
"\n",
"- See the [./gpt.py](./gpt.py) script, a self-contained script containing the GPT model we implement in this Jupyter notebook\n",
"- You can find the exercise solutions in [./exercise-solutions.ipynb](./exercise-solutions.ipynb)"
]
}
],
"metadata": {

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ch05/01_main-chapter-code/ch05.ipynb
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@ -2430,9 +2430,9 @@
"id": "fc7ed189-a633-458c-bf12-4f70b42684b8",
"metadata": {},
"source": [
"- See the [gpt_train.py](gpt_train.py) script containing a self-contained training script\n",
"- The [gpt_generate.py](gpt_generate.py) script loads pretrained weights from OpenAI and generates text based on a prompt\n",
"- You can find the exercise solutions in [exercise-solutions.ipynb](exercise-solutions.ipynb)"
"- See the [./gpt_train.py](./gpt_train.py) script, a self-contained script for training\n",
"- The [./gpt_generate.py](./gpt_generate.py) script loads pretrained weights from OpenAI and generates text based on a prompt\n",
"- You can find the exercise solutions in [./exercise-solutions.ipynb](./exercise-solutions.ipynb)"
]
}
],

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ch06/01_main-chapter-code/ch06.ipynb
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"id": "dafdc910-d616-47ab-aa85-f90c6e7ed80e",
"metadata": {},
"source": [
"- Interested readers can find an introduction to parameter-efficient training with low-rank adaptation (LoRA) in appendix E\n"
"- See the [./gpt_class_fintune.py](./gpt_class_fintune.py) script, a self-contained script for classification finetuning\n",
"- You can find the exercise solutions in [./exercise-solutions.ipynb](./exercise-solutions.ipynb)\n",
"- In addition, interested readers can find an introduction to parameter-efficient training with low-rank adaptation (LoRA) in [appendix E](../../appendix-E)"
]
}
],
@ -2370,7 +2372,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.11"
"version": "3.11.4"
}
},
"nbformat": 4,

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ch07/01_main-chapter-code/ch07.ipynb
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@ -2699,9 +2699,12 @@
"id": "f9853e7f-a81a-4806-9728-be1690807185"
},
"source": [
"## Summary\n",
"## Summary and takeaways\n",
"\n",
"- No code in this section"
"- See the [./gpt_class_fintune.py](./gpt_class_fintune.py) script, a self-contained script for classification finetuning\n",
"- [./ollama_evaluate.py](./ollama_evaluate.py) is a standalonw script based on section 7.8 that evaluates a JSON file containing \"output\" and \"response\" keys via Ollama and Llama 3\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)"
]
}
],
@ -2727,7 +2730,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.11"
"version": "3.11.4"
}
},
"nbformat": 4,