HuggingFace offers shared models, datasets, and hosted spaces, giving AI researchers and developers a complete ecosystem. This article explains how to work with HuggingFace models and datasets. 1. Model Operations and Usage 1.1 Custom Storage Directory 1 export HF_HOME=/Volumes/Data/HuggingFace Otherwise the default is the ~/.cache/huggingface directory. 1.2 Downloading Models The

1. Why Transformer Fully connected self-attention In earlier RNN models, each word could only relate to its neighboring words, whereas with the Attention mechanism in the Transformer model, a word can relate to a word at any position, so it can capture global context information. No vanishing gradient problem An RNN applies repeatedly to the same weight matrix, so when its largest eigenvalue is less than 1 the vanishing gradient problem appears.

1. What a Large Model Actually Is First, let us ask two large models this question and see what they answer. Claude says that a large model is essentially a probabilistic expression of linguistic knowledge: through statistical learning it models the regularities at every level of language and represents the prior distribution of language generation, thereby gaining the ability to predict and generate language.

1. Keywords Machine Learning (ML) The technology of automatically acquiring knowledge from data. Neural Network (NN) A model that imitates the structure and learning mechanism of biological neural networks; one of the branches of machine learning. The structure of a neural network consists of an input layer, hidden layers, and an output layer.

1. LLMs and Langchain Many people may never get the chance to train, or even fine-tune, a large model, but using large models is the wave of the future. So how should we embrace this change? The answer is Langchain. A large model provides a broad, general-purpose foundation. So far