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workshop-benchmark-together-model

Integrate and benchmark Together AI models in Benchy with structured extraction tasks.

8|Updated Sep 12, 2025
Search Tags:#automation#llm#benchmarking#evaluation#together-ai#structured-extraction
One-click install
npx skills add https://github.com/surus-lat/benchy --skill workshop-benchmark-together-model
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: workshop-benchmark-together-model
Source: https://github.com/surus-lat/benchy/tree/main/.workshop/skills/workshop-benchmark-together-model
Command: npx skills add https://github.com/surus-lat/benchy --skill workshop-benchmark-together-model

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires jq, curl, and includes assets (resource) components.

What problem does it solve?

This skill streamlines the process of integrating and evaluating new Together AI models within the Benchy framework, removing the manual overhead of configuring and running performance tests.

Core Features & Use Cases

  • Zero-Code Integration: Add new models by simply deploying YAML configuration templates.
  • Smoke-to-Full Workflow: Validate model performance with rapid smoke tests before committing to full-scale benchmark runs.
  • Use Case: Use this during the Benchy workshop to quickly verify if a new model correctly handles structured extraction tasks without needing to rewrite core evaluation logic.

Quick Start

Execute the workshop benchmark process by running the provided shell commands to install model configs and initiate the smoke test loop for your target Together AI models.

Frequently Asked Questions about workshop-benchmark-together-model

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I benchmark Together AI models for structured extraction tasks?▼

You can benchmark Together AI models for structured extraction by deploying YAML configuration templates within the Benchy evaluation suite, executing automated smoke tests before running full-scale performance validation.

Can I validate Together AI model performance without writing evaluation code?▼

Yes, you can validate Together AI model performance without writing code by deploying YAML configuration templates to integrate new models into the Benchy framework for automated structured extraction testing.

What is the difference between a smoke test and a full benchmark run in Benchy?▼

A smoke test in Benchy provides rapid validation of whether a Together AI model correctly handles structured extraction, while a full benchmark run commits to comprehensive, large-scale performance validation.

Do I need API credentials to evaluate Together AI models with Benchy?▼

Yes, evaluating Together AI models with Benchy requires valid Together API credentials and a configured Benchy environment to execute the model-specific structured extraction workflows.

What dependencies are required to run Together AI benchmarking workflows?▼

Running Together AI benchmarking workflows requires the Benchy environment, valid Together API credentials, and the command-line utilities jq and curl to execute the evaluation shell commands.

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