Hello everyone, I am Wei Xue AI. Today I will introduce to you the practical application of large models 10-detailed explanation of large model domain knowledge and parameter efficient fine-tuning (PEFT) technology, and use PEFT to train your own large models. Parameter Efficient Fine-tuning Technology (PEFT) in the field of large models refers to a method that improves model performance and efficiency through efficient parameter fine-tuning of large-scale neural network models. PEFT technology is usually suitable for large models that need to be efficiently fine-tuned to obtain better performance on specific vertical domain tasks, significantly reducing computing power expenditures. For beginners who have just come into contact with large models, they may not know much about some names. I will introduce this in detail:
1. Glossary of important terms in the field of large models
LLM model: A large language model or LLM is a machine learning model that can learn the underlying structure and semantics of text data for NLP tasks. They do this by learning a set of latent variables that represent high-level concepts and features of the text. Essentially, LLM models try to capture the content of a text rather than just focusing on the words used.
Pretrained model: A pretrained model is a machine learning model trained on large amounts of data to facilitate a specific task such as image classification, speech recognition, or natural language processing. These models have learned the optimal set of weights and parameters required to perform the task efficiently so that they can be used as a starting point for further training on new data or for use in other applications.
Parameters: Parameters are values/variables learned by the model during training to predict or classify new data. Parameters are often represented as weights and biases in neural networks, and they control how input data is converted into output predictions.
Transfer learning: Transfer learning is taking a pre-trained model developed for a specific task and reusing it as a starting point for a new related task. This involves using the learned feature representations of a pre-trained model as a starting point for a new model, which is then trained on a smaller dataset specific to the new task.
Fine-tuning: Fine-tuning is a specific type of transfer learning in which the weights of a pre-trained model are tuned on a new task-specific dataset