Tag Archives: parameter-efficient fine-tuning

Customizing LLMs: Parameter-Efficient Fine-Tuning

Parameter-Efficient Fine-Tuning or PEFT is a more efficient approach to adapting large language models (LLMs) compared to traditional full fine-tuning. Instead of modifying the entire model, PEFT focuses on fine-tuning only a small subset of the model’s parameters, making it less resource-intensive. This allows for faster adaptation to specific tasks while maintaining most of the pre-trained knowledge of the model, offering a cost-effective solution for improving performance on specialized tasks.

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LLM Customization

A large language model or LLM is a type of machine learning model designed for natural language processing or NLP. These models have an extremely high number of parameters (trillions as of this writing) and are trained on vast amounts of human-generated and human-consumed data. Due to their extensive training on this data, LLMs develop predictive capabilities in syntax, semantics, and knowledge within human language. This enables them to generate coherent and contextually relevant responses, giving the impression of intelligence.

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