tao-finetune-huggingface-model

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.