大模型微调

是

调整已训练好的LLM，适应特定领域的知识和任务。
与RAG对照，是LLM在私域使用的一个重要技术路线。

why

how

a. Data preparation

Data preparation involves curating and preprocessing the dataset to ensure its relevance and quality for the specific task. This may include tasks such as cleaning the data, handling missing values, and formatting the text to align with the model’s input requirements.

Additionally, data augmentation techniques can be employed to expand the training dataset and improve the model’s robustness. Proper data preparation is essential for fine-tuning as it directly impacts the model’s ability to learn and generalize effectively, ultimately leading to improved performance and accuracy in generating task-specific outputs.

b. Choosing the right pre-trained model

It’s crucial to select a pre-trained model that aligns with the specific requirements of the target task or domain. Understanding the architecture, input/output specifications, and layers of the pre-trained model is essential for seamless integration into the fine-tuning workflow.

Factors such as the model size, training data, and performance on relevan