Finetuning Basics
Contents
Finetuning Basicsยถ
Summaryยถ
Finetuning in Large Language Models (LLMs) is a process that adapts pre-trained models to specific tasks or domains by updating their parameters on a new dataset. This process enhances the modelโs performance on targeted applications, making it crucial for domain-specific tasks where pre-trained models lack specialized knowledge. Finetuning involves various techniques, including unsupervised, supervised, and instruction-based methods, each with its own advantages and limitations. The process typically includes preparing a high-quality dataset that is representative of the task and updating the model weights to better capture the underlying patterns and complexities in the data.
Key Conceptsยถ
Finetuning: A process that adapts pre-trained LLMs to specific tasks or domains by updating their parameters on a new dataset.
Unsupervised Finetuning: Involves exposing the LLM to a large corpus of unlabelled text from the target domain to refine its understanding of language.
Supervised Finetuning: Requires labelled data tailored to the target task, such as text classification or sentiment analysis.
Instruction-Based Finetuning: Uses natural language instructions to guide the LLM, useful for creating specialized assistants.
Data Requirements: High-quality, representative, and sufficiently specified datasets are essential for effective finetuning.
Model Selection: Choosing the most suitable pre-trained model for finetuning is crucial, considering factors such as model size, complexity, and original training data.
Referencesยถ
URL Name |
URL |
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Finetuning in Large Language Models - Oracle Blogs |
https://blogs.oracle.com/ai-and-datascience/post/finetuning-in-large-language-models |
Getting started with LLM fine-tuning - Microsoft Learn |
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The Ultimate Guide to LLM Fine Tuning: Best Practices & Tools - Lakera AI |
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The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs - arXiv |
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My experience on starting with fine tuning LLMs with custom data - Reddit |