Agent skill · NVIDIA
tao-finetune-huggingface-model
Fine-tune any HuggingFace CV / VLM / LLM model on local NVIDIA GPUs inside an NGC PyTorch container when no dedicated TAO model skill matches. Use when the user wants to fine-tune a HuggingFace model (full or LoRA), train a vision / VLM / LLM model end-to-end, generate a reproducible HF training pipeline, smoke-test a HuggingFace model locally before scale-up, push a fine-tuned model to the HF Hub with a model card, or emit a self-contained rerun skill for an existing HuggingFace finetune. Supports image classification, object detection, semantic / instance / panoptic segmentation, depth estimation, image-text-to-text VLM (SFT / LoRA), and LLM SFT / DPO / GRPO. Six-step workflow: inspect and qualify, hardware and NGC image, research, generate and smoke, train + eval + infer, push and emit rerun skill. Do not use for any Hugging Face model ID claimed by a dedicated `skills/models/*` skill; the model skill and its declared execution environment take precedence.
What it needs
About 10k tokens when loaded.
What this skill does
<!-- Copyright (c) 2026, NVIDIA CORPORATION. All rights reserved. Licensed under the Apache License, Version 2.0; see http://www.apache.org/licenses/LICENSE-2.0 --> tao-finetune-huggingface-model Standalone install? If this session was not initialized by the TAO skill bank plugin, run the tao-setup skill first (host preflight, credentials, cross-skill discovery). Local NVIDIA GPU fine-tuning for HuggingFace models, grounded in live-fetched documentation with curated references as a fallback safety net. One NGC container, a few focused scripts, one push to HF Hub. Follow the rules in this file; don't improvise. Dedicated-model routing gate Before Step 1 or any probe, image selection, package install, venv creation, or training-code generation, resolve modelid against the packaged model-owner registry. Use the absolute skill-bank root from which this file was loaded: The resolver matches model metadata, including huggingfacemodelids, networkarch, skill names, and legacy aliases. Routing is internal: a model ID and task are enough. Never require prompt boilerplate about skills, containers, or checkpoint formats. Exit 0: stop this workflow and follow the owning model skill's environment, action metadata, preflight, and checkpoint preparation. Exit 3: no packaged model skill owns the ID. This is the only result that permits Step 1 of the generic workflow. Any other nonzero exit: ownership discovery is broken or ambiguous. Stop and resolve that error; do not silently fall back to generic Hugging Face training. Hugging Face hosting never overrides ownership. Do not use this workflow to bypass a matched skill or ask the user to prescribe its internal preparation. For example, nvidia/Cosmos3-Nano routes to tao-finetune-cosmos-reason. Do not create a host training venv in this workflow. Its default execution path is the NGC container documented below; any venv-based training path requires an explicit user request. Order of authority (highest first): 1. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills NVIDIA/tao-finetune-huggingface-model