Custom instruction tuning is a process where a language model is specifically trained or adjusted to follow particular instructions or behave in a certain way. This involves providing the model with examples of desired behaviours or responses, so it can learn how to interpret and act on user instructions more accurately. The aim is to…
Context Cascade Networks are computational models designed to process and distribute contextual information through multiple layers or stages. Each layer passes important details to the next, helping the system understand complex relationships and dependencies. These networks are especially useful in tasks where understanding the context of information is crucial for making accurate decisions or predictions.
ChatML pretraining methods refer to the techniques used to train language models using the Chat Markup Language (ChatML) format. ChatML is a structured way to represent conversations, where messages are tagged with roles such as user, assistant, or system. These methods help models learn how to understand, continue, and manage multi-turn dialogues by exposing them…
Sparse decoder design refers to creating decoder systems, often in artificial intelligence or communications, where only a small number of connections or pathways are used at any one time. This approach helps reduce complexity and resource use by focusing only on the most important or relevant features. Sparse decoders can improve efficiency and speed while…
Interleaved multimodal attention is a technique in artificial intelligence where a model processes and focuses on information from different types of data, such as text and images, in an alternating or intertwined way. Instead of handling each type of data separately, the model switches attention between them at various points during processing. This method helps…
A Voice Identity Tool is a type of software or technology that analyses a person’s voice to identify or verify who they are. It works by capturing unique features in how someone speaks, such as tone, pitch, and rhythm, and compares these to a stored voiceprint. This process helps confirm if the speaker is the…
Input shape refers to the specific dimensions or structure of data that a computer model, such as a neural network, expects to receive. This includes the number of features, rows, columns, or channels in the data. Correctly matching the input shape is essential for the model to process the information accurately and avoid errors. It…
Output depth refers to the number of bits used to represent each individual value in digital output, such as in images, audio, or video. It determines how many distinct values or shades can be displayed or recorded. For example, higher output depth in an image means more subtle colour differences can be shown, resulting in…
Epoch reduction is a technique used in machine learning and artificial intelligence where the number of times a model passes through the entire training dataset, called epochs, is decreased. This approach is often used to speed up the training process or to prevent the model from overfitting, which can happen if the model learns the…
Loss decay is a technique used in machine learning where the influence of the loss function is gradually reduced during training. This helps the model make larger adjustments in the beginning and smaller, more precise tweaks as it improves. The approach can help prevent overfitting and guide the training process to a more stable final…