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AI Engineering / Working with LLMsFine-tuning & Model Customization

12 items

1

Dataset Size and Quality

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2

Train/Validation/Test Splits

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3

Instruction Fine-Tuning (SFT)

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4

Adapters and Prefix/Prompt Tuning

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5

LoRA (Low-Rank Adaptation)

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6

Full vs Parameter-Efficient Fine-Tuning

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7

What is Fine-Tuning

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8

Preparing Training Datasets

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9

RLHF

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10

DPO and Preference Alignment

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11

Fine-Tune vs Prompting vs RAG

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12

QLoRA and Quantized Fine-Tuning

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Fine-Tune vs Prompting vs RAG

Choose fine-tuning, prompt engineering, or RAG based on whether you need behavior, guidance, or fresh knowledge.

Question

When should you fine-tune vs use prompt engineering or RAG?

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