tao-finetune-huggingface-model

name	tao-finetune-huggingface-model
description	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.
license	Apache-2.0
tags	["finetuning","huggingface","nvidia-tao","computer-vision","training"]
compatibility	Requires docker + nvidia-container-toolkit, NVIDIA GPU (driver ≥ 545, ≥ 24 GB VRAM for ≤3B models), ~40 GB free disk. Optional credentials (read from the session environment) — HF_TOKEN is read only when the model/dataset is gated or `push_to_hub` is on; WANDB_API_KEY and WANDB_PROJECT only when WandB logging is enabled.
metadata	{"author":"NVIDIA Corporation","version":"0.1.0"}
allowed-tools	Read Bash Write

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 model_id against the packaged model-owner registry. Use the absolute skill-bank root from which this file was loaded:

python <bank-root>/scripts/resolve_tao_model.py \
  --skill-bank <bank-root> \
  --model "$MODEL_ID" \
  --format json

The resolver matches model metadata, including huggingface_model_ids, network_arch, 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. User input — explicit model_id, dataset_id, training_method, config.yaml overrides.
2. Live research — model card, HF repo example, author finetune script, HF task docs, paper; always fetched (Step 3 + references/research-priorities.md).