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ocr_text: Fine-tuning is a crucial process in machine learning that involves adapting a pre-trained model, such as a large language model (LLM), to specific tasks or use cases\n. Unlike transfer learning, which focuses on reusing knowledge from one task to another, fine-tuning specifically tailors the model for a particular goal by adjustin\n; its parameters during training.\n184 Key Aspects of Fine-Tuning in LLMs:\nL. **Adaptation Process**:\n- Fine-tuning involves modifying the pre-trained model's architecture and weights based on specific tasks or prompts.\n- During fine-tuning, models are typically trained using datasets that include relevant examples with clear task-specific guidance.\n2. **Task-Specific Guidance®:\n- Fine-tuning allows models to understand user intent by providing prompts that guide them towards the desired output.\n- For example, training an LLM to answer questions requires specific prompts that instruct it on how to respond.\n- Fine-tuning leverages existing knowledge, making it more efficient than retraining from scratch.\n- It improves customer satisfaction by providing relevant answers and enhances operational efficiency.\n1. **Balancing Generalization®*:\n- Fine-tuning includes techniques like dropout and weight decay to prevent overfitting, ensuring the model generalizes better to unseen tasks.\n5. **Handling Multiple Tasks™:\n- Fine-tuning can be applied across various domains, including question-answering systems, language translation, and text generation.\n- It supports adaptability by allowing models to handle multiple languages or dialects through contextual adaptation.\n5. **Computational Considerations®*:\n- While computationally intensive due to large model parameters, fine-tuning is optimized with efficient hardware and techniques.
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