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ch07/01_main-chapter-code/ch07.ipynb
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@ -16,7 +16,7 @@
"</font>\n",
"</td>\n",
"<td style=\"vertical-align:middle; text-align:left;\">\n",
"<a href=\"http://mng.bz/orYv\"><img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/cover-small.webp\" width=\"100px\"></a>\n",
"<a href=\"http://mng.bz/orYv\"><img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/cover-small.webp?1\" width=\"100px\"></a>\n",
"</td>\n",
"</tr>\n",
"</table>"
@ -77,7 +77,7 @@
"id": "264fca98-2f9a-4193-b435-2abfa3b4142f"
},
"source": [
"<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/ch07_compressed/overview.webp\" width=500px>"
"<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/ch07_compressed/overview.webp?1\" width=500px>"
]
},
{
@ -121,7 +121,7 @@
"source": [
"- The topics covered in this chapter are summarized in the figure below\n",
"\n",
"<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/ch07_compressed/chapter-overview-1.webp?123\" width=500px>"
"<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/ch07_compressed/chapter-overview-1.webp?1\" width=500px>"
]
},
{
@ -285,7 +285,7 @@
"id": "dffa4f70-44d4-4be4-89a9-2159f4885b10"
},
"source": [
"<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/ch07_compressed/prompt-style.webp\" width=500px>"
"<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/ch07_compressed/prompt-style.webp?1\" width=500px>"
]
},
{
@ -482,7 +482,7 @@
"id": "233f63bd-9755-4d07-8884-5e2e5345cf27"
},
"source": [
"<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/ch07_compressed/chapter-overview-2.webp?1234\" width=500px>"
"<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/ch07_compressed/chapter-overview-2.webp?1\" width=500px>"
]
},
{
@ -709,7 +709,7 @@
"id": "0386b6fe-3455-4e70-becd-a5a4681ba2ef"
},
"source": [
"<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/ch07_compressed/inputs-targets.webp\" width=400px>"
"<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/ch07_compressed/inputs-targets.webp?1\" width=400px>"
]
},
{
@ -804,7 +804,7 @@
"id": "bd4bed33-956e-4b3f-a09c-586d8203109a"
},
"source": [
"<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/ch07_compressed/ignore-index.webp\" width=500px>"
"<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/ch07_compressed/ignore-index.webp?1\" width=500px>"
]
},
{
@ -1058,7 +1058,7 @@
"id": "fab8f0ed-80e8-4fd9-bf84-e5d0e0bc0a39"
},
"source": [
"<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/ch07_compressed/mask-instructions.webp\" width=600px>"
"<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/ch07_compressed/mask-instructions.webp?1\" width=600px>"
]
},
{
@ -1088,7 +1088,7 @@
"id": "9fffe390-b226-4d5c-983f-9f4da773cb82"
},
"source": [
"<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/ch07_compressed/chapter-overview-3.webp\" width=500px>"
"<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/ch07_compressed/chapter-overview-3.webp?1\" width=500px>"
]
},
{
@ -1488,7 +1488,7 @@
"id": "8d1b438f-88af-413f-96a9-f059c6c55fc4"
},
"source": [
"<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/ch07_compressed/chapter-overview-4.webp\" width=500px>"
"<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/ch07_compressed/chapter-overview-4.webp?1\" width=500px>"
]
},
{
@ -1706,7 +1706,7 @@
"source": [
"- In this section, we finetune the model\n",
"\n",
"<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/ch07_compressed/chapter-overview-5.webp\" width=500px>\n",
"<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/ch07_compressed/chapter-overview-5.webp?1\" width=500px>\n",
"\n",
"- Note that we can reuse all the loss calculation and training functions that we used in previous chapters"
]
@ -1968,7 +1968,7 @@
"id": "5a25cc88-1758-4dd0-b8bf-c044cbf2dd49"
},
"source": [
"<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/ch07_compressed/chapter-overview-6.webp\" width=500px>"
"<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/ch07_compressed/chapter-overview-6.webp?1\" width=500px>"
]
},
{
@ -2224,7 +2224,7 @@
"id": "805b9d30-7336-499f-abb5-4a21be3129f5"
},
"source": [
"<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/ch07_compressed/chapter-overview-7.webp\" width=500px>"
"<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/ch07_compressed/chapter-overview-7.webp?1\" width=500px>"
]
},
{
@ -2264,7 +2264,7 @@
"\n",
"- In general, before we can use ollama from the command line, we have to either start the ollama application or run `ollama serve` in a separate terminal\n",
"\n",
"<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/ch07_compressed/ollama-run.webp\" width=700px>\n",
"<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/ch07_compressed/ollama-run.webp?1\" width=700px>\n",
"\n",
"\n",
"- With the ollama application or `ollama serve` running in a different terminal, on the command line, execute the following command to try out the 8-billion-parameter Llama 3 model (the model, which takes up 4.7 GB of storage space, will be automatically downloaded the first time you execute this command)\n",
@ -2710,7 +2710,7 @@
"- This marks the final chapter of this book\n",
"- We covered the major steps of the LLM development cycle: implementing an LLM architecture, pretraining an LLM, and finetuning it\n",
"\n",
"<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/ch07_compressed/final-overview.webp\" width=500px>\n",
"<img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/ch07_compressed/final-overview.webp?1\" width=500px>\n",
"\n",
"- An optional step that is sometimes followed after instruction finetuning, as described in this chapter, is preference finetuning\n",
"- Preference finetuning process can be particularly useful for customizing a model to better align with specific user preferences; see the [../04_preference-tuning-with-dpo](../04_preference-tuning-with-dpo) folder if you are interested in this\n",