methodology

Fine-Tuning LLMs

Fine-tuning LLMs is a machine learning technique where a pre-trained large language model (LLM) is further trained on a smaller, domain-specific dataset to adapt it for specialized tasks or improve its performance in particular contexts. This process adjusts the model's parameters to better align with the target data distribution, enabling capabilities like custom text generation, classification, or question-answering. It leverages transfer learning to reduce training time and data requirements compared to training a model from scratch.

Also known as: LLM Fine-Tuning, Fine-Tuning Large Language Models, Model Fine-Tuning, Transfer Learning for LLMs, Adapting LLMs
🧊Why learn Fine-Tuning LLMs?

Developers should learn fine-tuning LLMs when they need to customize general-purpose models for specific applications, such as creating chatbots for customer support, generating industry-specific content, or improving accuracy in niche domains like legal or medical text analysis. It is particularly useful in scenarios where labeled data is limited but high performance is required, as it builds on the broad knowledge of pre-trained models while tailoring outputs to meet precise business or technical needs.

Compare Fine-Tuning LLMs

Fine-Tuning LLMs vs Prompt Engineering→Fine-Tuning LLMs vs Retrieval Augmented Generation→Fine-Tuning LLMs vs Few Shot Learning→

Learning Resources

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Hugging Face Fine-Tuning Tutorial
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Fine-Tuning LLMs with PyTorch
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Coursera: Fine-Tuning Large Language Models
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YouTube: Fine-Tuning Explained by Andrej Karpathy
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Related Tools

Transfer LearningNatural Language ProcessingMachine LearningHugging Face TransformersPrompt Engineering

Alternatives to Fine-Tuning LLMs

Prompt EngineeringRetrieval Augmented GenerationFew Shot Learning