The Foundation Model refers to a model that has already been trained and can handle specific tasks (such as text classification, image recognition, etc.). Its parameters are typically obtained through unsupervised or supervised learning on large-scale datasets. For example, in the field of natural language processing, BERT (Bidirectional Encoder Representations from Transformers) is a widely used foundation model, which, through pre-training on vast amounts of text, gains strong language representation abilities.

<aside> 💡 The relationship between AGI and an AGI Foundation Model is like that of C++ and the C++ Standard Library.

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Fine-tuning is the process of making slight adjustments to a foundation model using a small amount of labeled data to adapt it for different tasks. Fine-tuning typically involves the following steps:

1. Select an appropriate foundation model;
2. Add new layers or modify existing layers according to the required task;
3. Train the entire model using a small amount of labeled data.

<aside> 💡 💡 A foundation model is like a child who has just finished their compulsory education (high school). Fine-tuning is like sending that child to university for further studies.

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All AGI Comes from the Foundation Model

Existence and Uniqueness