👉 The trainer fluid, also known as the training memory or context window, is a crucial component in transformer-based models like GPT (Generative Pre-trained Transformer). It refers to the mechanism that allows the model to retain and utilize information from the input data throughout its processing, effectively creating a continuous context that spans the entire sequence of tokens it has been trained on. This fluid enables the model to maintain coherence and context as it generates responses, making it possible for the model to understand and generate relevant, contextually appropriate text based on the input provided. The size of the trainer fluid can significantly impact the model's performance, as a larger window allows for better context retention but also increases computational demands.
