Definition ∞ LLM fine-tuning is the process of adapting a pre-trained large language model to perform specific tasks or generate outputs aligned with a particular domain or style. This involves further training the model on a smaller, specialized dataset, allowing it to learn nuanced patterns relevant to the target application. Fine-tuning enhances the model’s performance and relevance for specialized uses. It customizes general AI capabilities for precise requirements.
Context ∞ News about LLM fine-tuning is increasingly relevant in the digital asset space, especially concerning AI applications for market analysis, content generation, or customer support within crypto platforms. This technique allows AI models to better understand and generate text specific to blockchain terminology or market events. It represents a key development in integrating advanced AI with digital asset ecosystems.
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