Guide AI teams through generating NeMo Evaluator Launcher configurations.
npx skills add https://github.com/NVIDIA/skills --skill nel-assistant
Please help me install this Agent Skill. Skill: nel-assistant Source: https://github.com/NVIDIA/skills/tree/main/skills/NeMo-Evaluator-Launcher/nel-assistant Command: npx skills add https://github.com/NVIDIA/skills --skill nel-assistant
Guides users through creating production-ready NeMo Evaluator Launcher configurations via an interactive wizard that reduces setup complexity and errors.
To begin, run the interactive wizard to generate a production-ready nel-assistant configuration and apply it to your NeMo Evaluator Launcher deployment.
High-intent search queries and answers about installing and using this skill.
To generate a NeMo Evaluator Launcher configuration, you can use an interactive wizard that guides you through model discovery, sampling settings, and task-specific overrides to produce a YAML file ready for multi-node deployment.
The best way to set up evaluation hooks and logging interceptors is by using a guided configuration wizard that automatically applies these task-specific overrides to your NeMo Evaluator Launcher deployment settings.
Yes, you can customize tool-calling and model deployment settings by progressing through an interactive configuration process that reads available model cards and generates customized nemo_evaluator_config overrides for your workflows.
You can monitor evaluation workflows and apply per-task overrides by generating a production-grade configuration that includes built-in hooks and interceptors specific to your NeMo Evaluator Launcher tasks.
Yes, the NeMo Evaluator Launcher configuration wizard supports model discovery by automatically reading available model cards to accurately configure deployment and sampling settings during the generation process.
NeMo Evaluator configuration validation often fails due to missing task-specific overrides or incorrect deployment settings; using an interactive wizard ensures step-by-step progression and validates outputs before multi-node deployment.
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