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On-premise LLMs
Parameter-Efficient Fine-Tuning (PEFT): The Basics and a Quick Tutorial
Parameter-Efficient Fine-Tuning (PEFT): The Basics and a Quick Tutorial Parameter-efficient fine-tuning (PEFT) modifies a subset of parameters in pre-trained neural networks, rather than updating all model parameters. Traditional fine-tuning methods can be computationally intensive, requiring significant resources and storage. PEFT aims to mitigate these challenges, focusing on adjusting a limited number of parameters to achieve […]
Fine-Tuning Llama 2 with Hugging Face PEFT Library
Fine-Tuning Llama 2 with Hugging Face PEFT Library What Is LLaMA 2? LLaMA2, introduced by Meta in 2023, is a family of open-source large language model (LLM). It includes models with either 7 billion or 70 billion parameters. The number of parameters in an LLM determines the model’s ability to learn from data and generate […]
Fine-Tuning LLMs: Top 6 Methods, Challenges and Best Practices
What Does It Mean to Fine-Tune LLMs? Fine-tuning Large Language Models (LLMs) involves adjusting pre-trained models on specific datasets to enhance performance for particular tasks. This process begins after general training ends. Users provide the model with a more focused dataset, which may include industry-specific terminology or task-focused interactions, with the objective of helping the […]