There are several types of large language models (LLMs) that differ in their architecture and the type of text data they are trained on. Here are a few examples:

1. Transformer-based models : Transformer-based models, such as GPT-3, use a self-attention mechanism to learn patterns in text data. These models have achieved state-of-the-art performance on a wide range of NLP tasks and are often used for text generation and language understanding.

2. Encoder-decoder models : Encoder-decoder models, such as BERT, use a transformer-based architecture to learn representations of text. These models are often used for text classification and language understanding.

3. RNN-based models : Recurrent neural network (RNN) based models, such as LSTM, use a different type of neural network architecture that is better suited to sequential data. These models are often used for text generation and language understanding.

4. Hybrid models : Some LLMs combine different types of architectures, such as transformers and RNNs, to achieve better performance on specific tasks. For example, the GPT-2 model uses a combination of transformers and RNNs for text generation.

5. Task-specific models : Some LLMs are designed for specific NLP tasks, such as machine translation or question answering. These models are often trained on large datasets that are specific to the task they are designed to perform.

6. Multilingual models : Multilingual LLMs are trained on text data from multiple languages and can perform well on tasks that involve multiple languages.