7 Fine-Tuning Techniques for Large Models to Boost Your AI Projects
Discover 7 fine-tuning techniques to optimize large AI models, including LoRA, Adapter Tuning, and Prefix Tuning. Learn their principles, benefits, and applications.
This article deeply analyzes the basic concepts and diverse techniques of fine-tuning large models. It introduces various fine-tuning methods such as LoRA, Adapter Tuning, and Prefix Tuning in detail.
Each strategy's basic principles, main advantages, and suitable application scenarios are discussed thoroughly, enabling readers to choose the most appropriate fine-tuning method based on specific application requirements and computational resource constraints.
Basic Theory of Fine-Tuning Large Models
The training process of Large Language Models (LLMs) typically consists of two main stages:
Stage 1: Pre-training Phase
In this phase, the large model is trained on a vast, unlabeled dataset to learn the statistical features and basic knowledge of the language. The model acquires an understanding of vocabulary meanings, sentence structures, and basic information and context of texts.
Pre-training is essentially an unsupervised learning process. The pre-trained model, known as the base model, possesses general predictive abilities. Examples of base models include the GLM-130B model and OpenAI's main models.
Stage 2: Fine-Tuning Phase
After pre-training, the model undergoes further training on task-specific datasets. This stage involves fine-tuning the model's weights to better adapt to specific tasks. The resulting model gains various capabilities, such as the GPT code series, GPT text series, and ChatGLM-6B.
What is Fine-Tuning for Large Models?
In simple terms, fine-tuning a large model means inputting more information into the model to optimize specific functions. By providing domain-specific datasets, the model learns the knowledge of that domain, enhancing its performance in specific NLP tasks such as sentiment analysis, entity recognition, text classification, and dialogue generation.
Why is Fine-Tuning Critical?
The core reason is that fine-tuning equips large models with more precise functions. For example, integrating a local knowledge base for search or building a Q&A system for specific domain questions.
Take VisualGLM as an example. As a general multimodal model, it needs to be fine-tuned with medical imaging datasets to improve its performance in medical image recognition.
This is similar to hyperparameter optimization in machine learning models. Only after adjusting hyperparameters can the model better fit the current dataset. Large models can undergo multiple rounds of fine-tuning, each round optimizing the model's capabilities further.