configure-model

Name: configure-model
Availability: InStock
Author: SURUS

Generate YAML schemas for AI model benchmarking targets in Benchy.

8|Updated Sep 12, 2025

Search Tags:#llm #benchmarking #configuration #api-integration #vllm #ai-evaluation

Ready to run

npx skills add https://github.com/surus-lat/benchy --skill configure-model

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: configure-model
Source: https://github.com/surus-lat/benchy/tree/main/.agent/skills/configure-model
Command: npx skills add https://github.com/surus-lat/benchy --skill configure-model

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill removes the friction of manually setting up evaluation targets by standardizing the configuration of AI models and API endpoints within the Benchy benchmarking suite.

Core Features & Use Cases

Multi-Interface Support: Seamlessly switch between custom HTTP APIs, cloud-hosted models like OpenAI or Anthropic, and local vLLM/Ollama servers.
Standardized Schema: Automatically generates the required target section for benchmark.yaml files to ensure consistency across evaluation runs.
Use Case: A developer needs to compare the performance of a local Llama-3 model against a hosted GPT-4o instance; this skill generates the necessary configuration blocks for both environments in seconds.

Quick Start

Use the configure-model skill to set up a local vLLM server at http://localhost:8000/v1 as the target for the current benchmark.

Frequently Asked Questions about configure-model

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

FAQPage Schema

How do I configure a local vLLM server for LLM benchmarking?▼

To configure a local vLLM server for LLM benchmarking, you generate a standardized YAML schema that maps the local inference server's API interface and request templates for automated evaluation workflows.

Can I benchmark cloud-based models and custom HTTP APIs in the same evaluation run?▼

Yes, you can benchmark cloud-based models and custom HTTP APIs together by generating the appropriate target configuration blocks for each provider within your benchmark.yaml file to ensure consistent evaluation.

What is the best way to set up an OpenAI model as a benchmarking target?▼

The best way to set up an OpenAI model as a benchmarking target is to automatically generate the required YAML schema section, ensuring correct mapping of the cloud-based provider's specific API interface for evaluation.

How does YAML schema generation work for AI evaluation workflows?▼

YAML schema generation for AI evaluation works by standardizing the configuration of AI models and API endpoints, automatically mapping provider-specific interfaces and request templates into a benchmark.yaml file.

Do I need to manually write request templates for API integration when setting up benchmarks?▼

No, you do not need to manually write request templates for API integration because the configuration process automatically ensures correct mapping of provider-specific interfaces and request templates for the benchmarking engine.

Why use a standardized configuration schema for comparing local and cloud AI models?▼

Using a standardized configuration schema for comparing local and cloud AI models removes manual setup friction and ensures consistency across automated evaluation runs by correctly mapping diverse API interfaces.