Pieces Technical Glossary
This comprehensive glossary serves as a one-stop resource for understanding the key terms and concepts associated with our SDK. Mastering these definitions will empower you to effectively utilize the SDK in your projects. Click on any of the terms to see more details about the particular term.
Glossary Terms
AIMLAssetsCS FundamentalsFrontendPieces SpecificSearch
AI Code Refactoring
AI Code Refactoring refers to the systematic modification of software code to enhance its design, readability, and maintainability without altering its external behavior — just with the added ingredient of AI code.AI Code Review
AI Code Review refers to the use of artificial intelligence technologies to analyze and improve code quality in software development.Causal Language Model
A Causal Language Model (CLM) is a type of language model designed to predict the next token in a sequence based on the previous tokens, without access to future context.Classification
Classification is a machine-learning task that involves assigning a label to an input data point.Conditional Variational Autoencoder
A Conditional Variational Autoencoder (CVAE) is an extension of the Variational Autoencoder (VAE), a type of neural network that aims to generate data similar to its training set.Context Window
The frame of reference that a language model uses to understand or generate language based on a fixed span of words or tokens surrounding a specific point.Copilot
An AI copilot is a development assistant that can help generate, answer questions about, troubleshoot, and debug code.Data Tokenization
Data tokenization in the context of Artificial Intelligence (AI), specifically in Natural Language Processing (NLP), refers to the process of breaking down text into smaller units called tokens.Debugging AI
Debugging AI refers to the use of artificial intelligence (AI) tools to enhance the process of identifying and fixing bugs in software code. These tools leverage AI technologies to automate and optimize the debugging process, making it more efficient and less prone to human error.Diffusion Models
Diffusion models are a class of generative models that have significantly impacted fields such as image generation, audio synthesis, and more, by effectively learning to reverse a process that gradually adds noise to data.Embedding
Embedding is a technique that converts discrete data into a continuous vector space.Hyper Parameter Tuning
Hyperparameter tuning is a crucial step in the process of building and optimizing machine learning models. Hyperparameters are the configuration variables that govern the training process and structure of a machine-learning model. These could include the learning rate in a neural network, depth in a decision tree, or number of clusters in K-means clustering.LLM Fine-Tuning
The process of adjusting a pre-trained AI model's parameters so it can perform better on specific tasks or datasets. Fine-tuning large language models is particularly beneficial when the model needs to adapt to specific or niche tasks where general models might not perform well without adjustments.LangChain
An open-source framework designed to streamline the development of applications powered by large language models (LLMs).Language Model Hallucination
Language model hallucination occurs when a large language model (LLM) generates text that is not supported by its input data or training content. This phenomenon can manifest as minor inaccuracies or complete fabrications, affecting the reliability and trustworthiness of the model's outputs.Large Language Models (LLMs)
Large Language Models (LLMs) are a type of neural network that has been trained on diverse datasets, including vast amounts of text and, increasingly, multimodal data such as images, audio, and video.LoRA (Low-Rank Adaptation)
LoRA (Low-Rank Adaptation) is a method of fine-tuning large language models (LLMs) efficiently by adapting only a small subset of model parameters, specifically within the Transformer's attention mechanismMasked Language Modeling
Masked Language Modeling (MLM) is a pre-training technique in natural language processing (NLP) that enables AI models to predict masked tokens within an input sequence, enhancing their understanding of language context and structure.Neural Machine Translation
Neural Machine Translation (NMT) is an innovative technology that allows computers to translate languages with human-level fluency. Unlike previous approaches, NMT uses artificial intelligence to analyze and create translations that match the natural flow of language. It analyzes context, idioms, and subtleties to provide more accurate and contextually appropriate translations. This transformational technology uses neural networks to comprehend complicated linguistic patterns, allowing seamless communication across several languages. NMT's capacity to adapt and learn from varied linguistic data sets is a huge step forward in breaking down language barriers, boosting global connectedness, and improving cross-cultural communication.Quantized Generative Pre-trained Transformer (qGPT)
qGPT is a quantized version of the Generative Pre-trained Transformer (GPT) language model.Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation (RAG) combines retrieval-based and generative-based approaches to generate responses by retrieving relevant information from a large corpus.Small Language Models
Small language models are a type of AI that can understand and generate human language.Transformers
A transformer is an advanced model architecture in machine learning that uses an attention mechanism to dynamically weigh the significance of different words in a sentence.Vector Quantization
Vector Quantization (VQ) is a pivotal data compression technique predominantly utilized in digital signal processing.Vector Search
Vector search is a sophisticated data retrieval method that utilizes mathematical vectors to analyze and process complex, unstructured data. Learn more about vector search, its benefits, drawbacks, and key takeaways.Vector Store
A vector store, also known as a vector database, is a type of database designed to handle high-dimensional vector data. Vectors are mathematical representations of data, each dimension representing different features of the data.Vision Transformers
Vision Transformers (ViT) are a type of neural network architecture primarily used for image recognition tasks.Zero-Shot Learning
Zero-shot learning (ZSL) is a machine learning paradigm that addresses the challenge of classifying objects from unseen classes—those for which no training data is availableqGPTseeds
qGPTseeds are a set of pre-trained models for the qGPT language model.Additional Resources:
For a deeper understanding of the SDK, consider exploring these supplementary resources:
- SDK Documentation: Comprehensive guides and tutorials for the SDK.
- API Reference: Detailed information on available functions and methods.
- Samples and Examples: Practical code snippets demonstrating SDK usage.
Contributing:
We value your feedback! If you encounter any missing or unclear terms, or have suggestions for improvement, feel free to contribute by reporting issues or submitting pull requests to our documentation repository (if applicable).
By effectively using this glossary, you'll enhance your understanding of the SDK and streamline your development process. Happy coding!