LoRA, full training, and RL paths plus Nemotron open models
Confirm GPU access on your hardware platform before you continue with any fine-tuning workflow.
nvidia-smi
Expected output should show GPU information for your hardware platform.
NOTE
Concrete Unsloth install, launch, and training commands are not included in this playbook. This page is a methods overview (LoRA / QLoRA, full fine-tuning, and reinforcement learning) plus pointers to Unsloth and Nemotron resources. For Blackwell install and fine-tuning guidance from Unsloth, see Fine-tuning LLMs with Blackwell, RTX 50 series & Unsloth.
Match the method to your goal and data size:
This playbook does not ship hyperparameters, training scripts, or a dependency list for those runs.
When you are ready to train, choose an open model that fits your VRAM and task. Nemotron 3 Nano 30B-A3B is one option optimized for agentic fine-tuning workloads; download it from Hugging Face when you follow an upstream Unsloth or training guide.
Confirm license terms and any gated-model access before downloading.
When you move from this overview to hands-on training:
nvidia-smi during trainingDo not invent local package pins or container tags from this playbook — use the Unsloth docs for current install steps.
If you created local environments, checkpoints, or downloaded models while following linked Unsloth or Nemotron guidance:
Cleanup is optional.