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By interacting with Oumi’s Agent, you can quickly create, evaluate, and train models with precision, regardless of your level of expertise or where you are in the machine learning lifecycle. The following are just a few examples that highlight some of the tasks you can accomplish using simple natural language prompts.

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Build & analyze datasets

Create a structured dataset of customer support tickets labeled by both urgency and topic to support classification tasks.
Create a labeled dataset of customer support tickets categorized by urgency and topic
Create 500 question-answer pairs from a product documentation PDF for training or evaluation use.
Generate 500 question-answer pairs from a product documentation PDF
Increase dataset size and diversity by generating new samples that follow the same style and format.
Expand my dataset by generating new samples that match the style and format of my existing examples
Improve evaluation robustness by generating additional samples consistent with your existing dataset.
Expand my dataset by generating additional samples consistent with my existing dataset to improve my evaluation
Analyze your dataset and generate new tasks with detailed solutions for addressing missing coverage.
Analyze my coding dataset and generate additional tasks with step-by-step solution breakdowns to fill gaps

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Evaluate models

Define evaluators that score model outputs based on key criteria like helpfulness and accuracy.
Create evaluators to score my model's responses for helpfulness and accuracy
Build a targeted evaluator to assess how well your model performs on customer support interactions.
Create an evaluator to measure my model's response quality on customer support tickets
Run evaluations to uncover common weaknesses and failure modes in your model’s responses.
Evaluate Qwen on a customer support dataset and surface the most common failure patterns
Evaluate model performance on sentiment classification using real-world product review data.
Test Qwen on a sentiment analysis task using customer food product reviews

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Train Models

Fine-tune a model to automatically categorize customer support questions by topic and urgency.
Fine-tune a model to classify customer support questions by topic and urgency
Fine-tune Qwen to identify the intent behind incoming customer support messages.
Fine-tune Qwen to detect the intent behind incoming customer support messages
Train a compact model for fraud detection using on-policy distillation techniques.
Train a compact model by distilling a large fraud detection model’s responses into a smaller model using on policy distillation.
Check out the Prompt Library for more examples of what you can do with the Oumi Agent.

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What’s next

Build your first model by diving into the Quickstart and building your first custom machine learning model in Oumi.