A language model is a probability distribution over sequence of words. Let us say there is a sequence of words of the length m. A probability model assigns a probability to the whole sequence. Language models generate probabilities by training on text in one or many languages.

Thus it is a statistical method that predicts the next word in the sequence of words. It learns from massive text the probability of each word appearing after a given sequence.

A translation model is a type of language model that gives conditional probability of the next token, given your source sequence and partial-target sentence.

A large language model (LLM) is a neural network-based language model that has a large number of parameters. These models are trained on massive data and can generate text that equals human writing.

What is a small language model? It all depends on the number of parameters. LLMs have more parameters. The more parameters a model has, the more data it can learn. The better it performs.

Large language models are being used today for text generation, translation and question answering. They can be used to automate tasks that are currently being done by humans.

The model is a mathematical representation of a system or process. Its job is to make predictions of the text that should follow a particular sequence. The parameters of the model are the values that define the skill of the model. These parameters help a model to make predictions. They transform input data into the desired output.

In a neural network model, the weights and biases are the parameters of the model. In a clustering model, the centroids of the clusters are the parameters of the model. In a linear regression model variables are the parameters of the model.

The value of parameters is estimated by the system during training. The value of hyper-parameters are pre-set and independent of the dataset. These values do not change during training. Hyper-parameter is not a part of the trained or the final model. Hyper-parameters specify the model family. They may control the training algorithm used to set the parameters.