nvflare-convert-huggingface

name	nvflare-convert-huggingface
description	Convert existing Hugging Face Transformers Trainer or TRL SFTTrainer training code into an NVFLARE federated job using flare.patch(trainer), local validation, and job export; use when the user names Hugging Face or preliminary source inspection identifies one Hugging Face owner, and not for manual PyTorch loops, Lightning, inference-only pipelines, deployment, or experiment workflows.
license	Apache-2.0
metadata	{"version":"0.1.0","author":"NVIDIA FLARE Team <[e-mail]>","min-flare-version":"2.9.0","blast-radius":"runs_simulator","category":"Conversion","tags":"nvflare, federated-learning, huggingface, transformers, trl, peft, conversion","languages":"python","frameworks":"huggingface, transformers, trl, pytorch, nvflare","domain":"ml"}

NVFLARE Convert Hugging Face

Use When

Use when converting training code built around transformers.Trainer, Seq2SeqTrainer, TRL SFTTrainer, or another Trainer subclass. Support full-model and PEFT/LoRA fine-tuning, datasets/tokenizers, Trainer callbacks and metrics, checkpoint continuity, and replicated torch.distributed training.

Do Not Use When

Do not use for an AutoModel driven by a manual PyTorch loop without a Hugging Face Trainer (route to nvflare-convert-pytorch), PyTorch Lightning (route to nvflare-convert-lightning, including Lightning modules that contain Transformers models), inference-only pipelines, model serving, failed jobs (route to nvflare-diagnose-job), or federated statistics without training (route to nvflare-fed-stats). Route a project with active Lightning and Hugging Face Trainer entrypoints to nvflare-orient to select one training-loop owner or separate jobs. Route unresolved Trainer ownership, such as a Trainer factory without a bound owner call, to nvflare-orient; do not patch either Trainer. Out of scope: DeepSpeed, FSDP, production/POC deployment, controller rewrites, experiment search, and privacy-protection requests such as HE, encrypted aggregation, differential privacy, or privacy filters; never substitute an unprotected recipe or present a disclaimer as implementation. If a request combines federated statistics and model-training conversion, treat it as two independent jobs and workflows: do not merge or automatically chain them, do not route the combination to nvflare-orient, and ask which workflow to run first before generating or running either job. Recommend nvflare-fed-stats first only when the user's purpose is to understand data distribution; handle conversion later as a separate request.

Available Scripts

Script	Purpose	Arguments
scripts/resolve_model_snapshot.py	Resolve a local or Hub model/dataset snapshot and emit JSON evidence.	Identifier; required --source; optional source-root, download, revision, cache, and repository-type flags.

Use run_script() only during the validation phase after loading references/huggingface-validation.md. For example:

• run_script("scripts/resolve_model_snapshot.py", ["--source", "local", "--source-root", "<absolute-source-root>", "<configured-path>"])
• run_script("scripts/resolve_model_snapshot.py", ["--source", "hub", "<org/model>"])

Workflow

1. Load ../nvflare-shared/references/conversion-common.md and apply it for the whole conversion; this SKILL.md states only the framework-specific deltas. Load ../nvflare-shared/references/conversion-workflow.md only for a non-standard rerun, authorization, or missing-semantics case; it no longer holds the data-location or partitioning contracts, whose invariants conversion-common.md owns. Load ../nvflare-shared/references/site-data-and-paths.md for generated partitions, relative paths, or per-site data locations.
2. Inspect before editing with nvflare agent inspect source <path> --format json plus direct source reading. Load references/huggingface-detection.md during this phase. If inspect recommends nvflare-orient for unresolved Trainer ownership or active Lightning/Hugging Face owners, stop before editing. Extract the entrypoint, Trainer subclass, model constructor, tokenizer or processor, datasets and collator, Trainer arguments, compute_metrics, callbacks, checkpoint and PEFT settings, precision, local budget, distributed launcher, site/round counts, data location, and aggregation intent. Do not import or execute user training modules to discover them.
3. Apply the dependency-install ordering rule in ../nvflare-shared/references/conversion-common.md before any Python command imports user, framework, NVFLARE, or declared dependencies. Keep dependency inventory single-purpose: check NVFLARE separately, inventory non-product packages separately, and do not append Hugging Face cache or filesystem discovery. Defer model and dataset availability checks to the maintained resolver during validation. If an optional path must be inspected, run it separately and report a missing directory with exit code zero.
4. Select the recipe from FL intent. For explicit FedAvg, run nvflare recipe show fedavg-pt --format json, then immediately load ../nvflare-shared/references/pytorch-family-recipe-construction.md and use the returned module, class, and parameters with the required construction and execution shape in assets/job.py. Import FedAvgRecipe from nvflare.app_opt.pt.recipes.fedavg, never from nvflare.recipe. Treat class_path as the public recipe key and path as its normalized exported representation; do not inspect Recipe source or signatures to reconcile them. Do not guess adjacent symbols or add per-site recipe config unless sites genuinely differ. Load ../nvflare-shared/references/pytorch-family-recipe-selection.md only for ambiguous, evaluation-only, or non-FedAvg requests.

Requirements

• Must use flare.patch(trainer) as the sole model-exchange owner. receive() inside a patched loop may inspect task metadata only; it must not load a second copy of the global model.
• Must follow the Client API initialization and conditional rank contract in ../nvflare-shared/references/conversion-common.md. Keep the generated client rankless; nvflare.client.hf.init() owns distributed-rank resolution and rejection. Load references/huggingface-state-and-distributed.md only for a distributed multi-process source path.
• Must preserve source evaluation. When per-round global-model evaluation is required, call trainer.evaluate() before trainer.train() on every rank. Do not invent compute_metrics, label mappings, averaging denominators, or metric direction.
• Must follow the Best-Model Metric policy in ../nvflare-shared/references/pytorch-family-recipe-construction.md; the Hugging Face delta is only how the delivered key is named and produced. Must preserve source metric names when practical: if the generated trainer.evaluate() emits accuracy, set key_metric="accuracy"; if Trainer emits a prefixed key such as eval_accuracy, set the server to that exact key and report the source-to-server mapping. When best-model selection is requested, every lower-is-better metric, including Trainer-generated eval_loss, is delivered as an explicitly negated companion and selected by that key — never as raw loss. When selection is not requested, use key_metric=""; do not omit it and accidentally activate the recipe default.
• Must preserve PEFT configuration exactly and verify adapter key compatibility between the server model and patched Trainer. Do not infer LoRA target modules, silently switch adapter/full-model scope, or solve key mismatches with non-strict loading.
• Must verify that trainer.model owns all federated trainable state for Trainer subclasses with reference, reward, value-head, or other auxiliary models. Ask or fail closed when params_scope="auto" would omit trainable state required by the algorithm.
• Must preserve model constructor values needed on both server and clients per ../nvflare-shared/references/pytorch-model-exchange.md (State-Dict Compatibility). Ask one semantic question or fail closed when required values are not statically available.
• Must patch only one Trainer per Python process. Preserve a single Trainer lifecycle across rounds when restore_state=True.

Always read this converter SKILL.md together with ../nvflare-shared/references/conversion-common.md. Complete each workflow phase before loading the next phase's reference. Do not preload validation, state/DDP, broad workflow, dependency, or reporting references. The standard FedAvg path loads, in order: ../nvflare-shared/references/conversion-common.md, references/huggingface-detection.md, and ../nvflare-shared/references/site-data-and-paths.md only for its stated triggers; ../nvflare-shared/references/pytorch-family-recipe-construction.md, references/huggingface-conversion.md, and ../nvflare-shared/references/pytorch-model-exchange.md; then ../nvflare-shared/references/validation-evidence.md and references/huggingface-validation.md; load references/huggingface-state-and-distributed.md and other shared references only under the triggers above. Do not depend on repository examples.