nemo-customizer

Name: nemo-customizer
Availability: InStock
Author: NVIDIA-NeMo

Orchestrate SFT and LoRA fine-tuning workflows for LLMs on the NeMo Platform.

58|16|Updated May 14, 2026

Search Tags:#llm #fine-tuning #lora #sft #unsloth #nemo #automodel

Ready to run

npx skills add https://github.com/NVIDIA-NeMo/nemo-platform --skill nemo-customizer

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: nemo-customizer
Source: https://github.com/NVIDIA-NeMo/nemo-platform/tree/main/plugins/nemo-customizer/src/nemo_customizer/skills/nemo-customizer
Command: npx skills add https://github.com/NVIDIA-NeMo/nemo-platform --skill nemo-customizer

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill simplifies the complex process of fine-tuning LLMs by automating the orchestration of SFT and LoRA training jobs on the NeMo Platform, removing the need for manual infrastructure management.

Core Features & Use Cases

Automated Training Pipelines: Supports both Automodel and Unsloth backends for efficient SFT and LoRA fine-tuning.
Platform Integration: Handles dataset conversion, fileset management, and job polling directly through the NeMo CLI.
Use Case: A developer needs to fine-tune a Qwen model on a custom instruction dataset; this Skill manages the entire lifecycle from dataset upload and job submission to monitoring and adapter registration.

Quick Start

Use the nemo-customizer skill to submit an automodel SFT LoRA job for the qwen3-1.7b model using the commonsense_qa dataset fileset.

Frequently Asked Questions about nemo-customizer

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

FAQPage Schema

How do I automate SFT and LoRA fine-tuning workflows for LLMs on the NeMo Platform?▼

You can automate SFT and LoRA fine-tuning by orchestrating dataset preparation, model registration, and GPU-accelerated Docker job submission through the NeMo CLI. This removes manual infrastructure management and streamlines the entire training lifecycle.

Can I use the Unsloth backend for LoRA fine-tuning with NeMo?▼

Yes, NeMo customizer supports both Automodel and Unsloth backends for efficient SFT and LoRA fine-tuning. It handles the integration natively, allowing you to submit training jobs directly through the platform's execution profiles.

What is the best way to manage datasets and model registration for LLM training jobs?▼

The best way to manage datasets and model registration is using the NeMo CLI for dataset conversion, fileset management, and model entity registration. This ensures your custom datasets are correctly prepared before submitting GPU-accelerated training jobs.

Does NeMo customizer handle job monitoring and adapter deployment automatically?▼

Yes, NeMo customizer handles job polling to monitor training progress and manages the lifecycle through to adapter registration. It ensures reliable training and seamless deployment of LoRA adapters directly on the NeMo Platform.

Do I need specific execution profiles to run GPU-accelerated Docker jobs for SFT?▼

Yes, you need platform-specific execution profiles to ensure reliable GPU-accelerated Docker job submission for SFT and LoRA. These profiles integrate with the NeMo CLI to manage the underlying training infrastructure automatically.