together-evaluations

What problem does it solve?

Evaluate and benchmark LLM outputs using Together AI's Evals framework to classify, score, and compare model responses across serverless, dedicated, and external targets.

Core Features & Use Cases

• Classification: categorize responses into predefined labels to quantify quality.
• Scoring: assign numeric scores to responses for quantitative benchmarking.
• Comparison: perform A/B model comparisons to identify preferred outputs.
• Typical use cases include model QA, bias/guardrail checks, and rapid model benchmarking in research or product workflows.

Quick Start

Upload a dataset in JSONL/CSV and create an evaluation to start benchmarking model outputs with classify, score, and compare.