nemotron-customize

Routes Nemotron customization requests to the correct repo-native workflow.

Updated Jul 30, 2026
One-click install
npx skills add https://github.com/Lhhiep-maxcode/Nemotron --skill nemotron-customize-lhhiep-maxcode
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: nemotron-customize
Source: https://github.com/Lhhiep-maxcode/Nemotron/tree/main/skills/nemotron-customize
Command: npx skills add https://github.com/Lhhiep-maxcode/Nemotron --skill nemotron-customize-lhhiep-maxcode

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill turns Nemotron model customization requests into the right repo-native workflow, so you can avoid guessing which training, data prep, conversion, evaluation, or optimization step to use.

Core Features & Use Cases

  • Workflow routing: Selects the correct Nemotron step for curation, translation, SFT, PEFT, pretraining, RL alignment, conversion, optimization, benchmarking, or evaluation.
  • Pipeline composition: Chains multiple steps by matching artifacts such as JSONL, packed Parquet, bin/idx shards, and checkpoints.
  • Guardrailed execution: Applies repo rules for tokenizer alignment, checkpoint conversion boundaries, hosted service requirements, and safe remote execution.
  • Use Case: A developer wants to translate a dataset, fine-tune a model, then evaluate the result; this Skill identifies the required steps and how their outputs connect.

Quick Start

Ask for the exact Nemotron step or pipeline you need, along with your input paths, model or checkpoint, output location, and whether you want a local dry run or remote execution.

Frequently Asked Questions about nemotron-customize

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

FAQPage Schema
How do I build a Nemotron pipeline for fine-tuning and evaluation?

Building a Nemotron pipeline involves routing requests through repo-native steps like data curation, SFT, PEFT, and evaluation, matching artifacts like JSONL, packed Parquet, and checkpoints to connect outputs.

What is the correct workflow for Nemotron checkpoint conversion?

Nemotron checkpoint conversion requires enforcing specific boundary rules and artifact compatibility to ensure safe remote execution and proper tokenizer alignment across different training stages.

Can I chain dataset translation and SFT training for Nemotron models?

Yes, you can chain dataset translation and SFT training. This Skill identifies the required Nemotron pipeline steps and connects their outputs by matching data formats like JSONL to ensure proper artifact flow.

Does Nemotron fine-tuning require specific tokenizer alignment?

Yes, Nemotron fine-tuning applies guardrailed execution rules that enforce strict tokenizer alignment and checkpoint conversion boundaries to maintain artifact compatibility across pretraining, RL alignment, and evaluation.

What are the limitations when running remote Nemotron training pipelines?

Remote Nemotron training pipelines require safe remote execution prerequisites and enforce strict guardrails for hosted service requirements, checkpoint boundaries, and artifact compatibility to prevent workflow failures.