Pre-training and Fine-tuning comparison in code implementation #MLtraining

The article discusses the difference between pre-training and fine-tuning large language models (LLMs) to enhance their performance in specific tasks. Pre-training involves training the model on a large dataset to learn general language patterns, while fine-tuning involves further training the model on a smaller dataset to adapt it to a specific task or domain. Fine-tuning is necessary in certain circumstances to improve the model’s performance in real-world applications. The article provides code implementation examples to illustrate the concepts of pre-training and fine-tuning. Readers can find more details on this topic by visiting the Level Up Coding website.

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Source link: https://levelup.gitconnected.com/pre-training-vs-fine-tuning-with-code-implementation-505650e2c945?source=rss——llm-5