together-evaluations

To evaluate and compare LLM outputs, you can use Together AI's Evals framework to classify, score, and compare model responses. Upload a JSONL or CSV dataset containing responses, specify a judge, and create an evaluation to benchmark quality across serverless, dedicated, or external targets.

Benchmarking model outputs requires a dataset uploaded as JSONL or CSV containing model responses. You also need a judge specification and a model_to_evaluate column to run classification, scoring, or comparison evaluations across your chosen targets.

Yes, you can perform A/B model comparisons to identify preferred outputs using the Evals framework. By uploading a dataset with responses and a judge specification, the framework compares model responses to determine which output is preferred.

Yes, the Together AI Evals framework supports benchmarking across serverless, dedicated, and external targets. You can evaluate and compare outputs from external models by configuring your evaluation targets when setting up the benchmarking pipeline.

The best way to run bias and guardrail checks is to use the Evals framework to classify responses into predefined labels. By uploading a JSONL or CSV dataset with a judge specification, you can quantify quality, safety, and preference automatically.

LLM evaluation scoring works by assigning numeric scores to model responses for quantitative benchmarking. You upload a JSONL or CSV dataset with responses, define a judge specification, and the framework generates numeric scores to measure output quality.