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Chat Models

Chat models are specialized versions of large language models designed to handle conversational contexts. They excel at generating human-like responses in interactive dialogue and maintaining context throughout a conversation, making them ideal for tasks like customer support, virtual assistance, and interactive applications. In contrast, Completion models are more suited for single-turn tasks that don't require maintaining a conversational history, such as text summarization, translation, or code generation. While both types of models can generate text based on the input they receive, Chat models are optimized for multi-turn dialogue and often exhibit a better understanding of nuanced conversational cues compared to their Completion counterparts.

Completion Models

Completion models are designed for single-turn tasks that require generating text based on a given prompt but don't necessitate maintaining a conversational history. These models excel in applications like text summarization, code generation, and translation, where the focus is on generating accurate and relevant content in one go, rather than engaging in back-and-forth dialogue. In contrast, Chat models are optimized for interactive, multi-turn conversations, and they are better at understanding and generating nuanced responses within a conversational context. While both types of models are capable of generating text, Completion models are generally more suited for tasks that don't require the complexities of conversational state and context.

Embedding Models

Embedding models are specialized machine learning models designed to map text into fixed-size vectors in a high-dimensional space. These vectors capture semantic meaning and relationships between words, allowing for various natural language processing tasks like text classification, clustering, and similarity analysis. Unlike Chat or Completion models, which generate text as output, embedding models primarily serve as a feature extraction mechanism to represent text in a format that other machine learning models can understand and process. They are commonly used for retrieval-augmented generation (RAG), in order to choose which bits of a large dataset should be included in the context for a completion or chat model.