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LoRA Fine-tuning refers to a technique for improving the performance of a pre-trained language model by fine-tuning it on a specific task or domain. LoRA stands for "Language-oriented Recurrent Attention", which is a neural architecture that has been used for pre-training language models.
Fine-tuning a pre-trained language model involves further training the model on a specific task or dataset to adapt it to that task or dataset. This is done by adding a task-specific layer on top of the pre-trained model and training the entire model end-to-end on the task-specific data.
Fine-tuning a language model on a specific task or domain can improve its performance on that task or domain. For example, if you have a pre-trained language model that has been trained on general text data, you can fine-tune it on a specific text classification task, such as sentiment analysis or named entity recognition, to improve its performance on that task.
LoRA Fine-tuning is a specific implementation of this technique that uses the LoRA architecture for pre-training language models and fine-tuning them on specific tasks or domains.
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LoRA Fine-tuning refers to a technique for improving the performance of a pre-trained language model by fine-tuning it on a specific task or domain. LoRA stands for "Language-oriented Recurrent Attention", which is a neural architecture that has been used for pre-training language models.
Fine-tuning a pre-trained language model involves further training the model on a specific task or dataset to adapt it to that task or dataset. This is done by adding a task-specific layer on top of the pre-trained model and training the entire model end-to-end on the task-specific data.
Fine-tuning a language model on a specific task or domain can improve its performance on that task or domain. For example, if you have a pre-trained language model that has been trained on general text data, you can fine-tune it on a specific text classification task, such as sentiment analysis or named entity recognition, to improve its performance on that task.
LoRA Fine-tuning is a specific implementation of this technique that uses the LoRA architecture for pre-training language models and fine-tuning them on specific tasks or domains.
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