> ## Documentation Index
> Fetch the complete documentation index at: https://docs.oumi.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# KEY TERMS

> Important Oumi definitions and concepts

## BUILDER

A visual interface in Oumi used to create and configure machine learning assets such as datasets, evaluators, evaluations, and training workflows.

## CONVERSATION

The standardized internal format used by Oumi to represent datasets, where data is structured as a sequence of messages with defined roles (e.g., user, assistant) and associated metadata.

## DATA EXPLORER

A tool in Oumi for inspecting, filtering, and analyzing datasets to better understand their structure, quality, and content.

## DATA PROVENANCE

Metadata that records the origin, transformations, and lineage of data within a dataset, helping ensure transparency, traceability, and reproducibility.

## DATA SYNTHESIS

The automated generation of new training or evaluation data using models or rules to expand, augment, or balance existing datasets.

## DATASET

A structured collection of prompts, responses, or conversations used for training, evaluating, or analyzing machine learning models.

## DENSE

A neural network architecture where every parameter participates in every forward pass, meaning all parts of the model are active for each input.

## EVALUATION

The process of running a model against a dataset and scoring its outputs using evaluators to measure performance.

## EVALUATOR

A scoring function or model that assesses the quality of model outputs according to specific criteria, such as accuracy, safety, or instruction adherence.

## FAILURE MODES

Recurring patterns where a model produces incorrect, unsafe, or undesired outputs, often used to guide dataset improvements and retraining.

## FULL-WEIGHT FINE-TUNING (FFT)

A training method where all parameters of a model are updated during fine-tuning.

## HYPERPARAMETER

A configurable setting that influences how a machine learning model trains or generates predictions. Examples include learning rate, temperature, batch size, and max tokens.

## INSTRUCTION FOLLOWING

An evaluation criterion that measures how well a model adheres to the instructions given in a prompt.

## JSON LINES (JSONL)

A file format where each line is a separate JSON object, commonly used for storing and streaming structured machine learning datasets.

## JUDGE

A model or evaluation system that scores or compares model outputs based on defined criteria.

## LLM-AS-A-JUDGE

An evaluation technique where a large language model is used to assess the quality or correctness of another model’s output.

## LOW-RANK ADAPTATION (LORA)

A parameter-efficient fine-tuning technique that updates a small set of additional parameters instead of modifying the entire model.

## MAX TOKENS

A parameter that limits the maximum number of tokens a model can generate in a single response.

## MIXTURE-OF-EXPERTS (MOE)

A model architecture where multiple specialized sub-networks (experts) are available, and only a subset is activated for each input.

## MODEL

A machine learning system that processes input data and generates predictions or outputs.

## OPEN-WEIGHT LLMS

Large language models whose trained weights are publicly available for download and fine-tuning.

## PARAMETER-EFFICIENT FINE-TUNING (PEFT)

Training techniques that adapt a model by updating a small number of parameters rather than the full model.

## PARQUET

A columnar storage file format optimized for large-scale data processing and analytics.

## RETRIEVAL-AUGMENTED GENERATION (RAG)

A technique that improves model responses by retrieving relevant external information and incorporating it into generation.

## RECIPE

A reusable configuration file that defines a workflow for tasks such as data synthesis, training, or evaluation.

## REQUESTS PER MINUTE (RPM)

A rate limit parameter that controls how many API requests can be sent within one minute.

## SAFETY

An evaluation criterion that measures whether model outputs avoid harmful, unsafe, or policy-violating content.

## SEED

A value used to initialize random processes so that results can be reproduced consistently.

## SUPERVISED FINE-TUNING (SFT)

A training process where a model learns from labeled prompt–response examples.

## TEMPERATURE

A parameter that controls randomness in model output generation; higher values increase diversity while lower values make outputs more deterministic.

## TOPIC ADHERENCE

An evaluation criterion that measures how well a model stays focused on the subject of the prompt.

## TRUTHFULNESS

An evaluation criterion that assesses whether a model’s output is factually accurate and not misleading.