---

name: model-loading description: "Load HFL Chinese BERT-wwm family models with Transformers or PaddleHub while avoiding RoBERTa-class and offline-cache mistakes." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

model-loading

Use this sub-skill when a task is about loading or validating HFL Chinese BERT-wwm family model identifiers, choosing the correct MODEL_NAME or PaddleHub MODULE_NAME, checking whether a model is available in an offline Hugging Face cache, or fixing errors caused by using RoBERTa classes for RoBERTa-wwm-named checkpoints.

Route here for

• hfl/chinese-bert-wwm, hfl/chinese-bert-wwm-ext, hfl/chinese-roberta-wwm-ext, hfl/chinese-roberta-wwm-ext-large, hfl/rbt3, hfl/rbt4, hfl/rbt6, or hfl/rbtl3 loading.
• Deciding between BertTokenizer/BertModel and AutoTokenizer/AutoModel for these identifiers.
• Explaining why RobertaTokenizer or RobertaModel is wrong for these models despite names containing RoBERTa.
• Distinguishing TensorFlow checkpoint zips from Hugging Face/PyTorch files.
• PaddleHub module-name lookup and optional Paddle/PaddleHub dependency caveats.
• Offline-safe model-id validation or cache checks with the bundled helper.

Route elsewhere

• Fine-tuning hyperparameters, task/model selection, WWM interpretation, and benchmark-driven model choice: ../task-selection-and-finetuning/SKILL.md.
• Dataset schemas, dataset availability, and benchmark table interpretation: ../data-and-benchmarks/SKILL.md.
• Cross-cutting environment, cache, proxy, and framework issues that are not specific to these loading workflows: ../../references/troubleshooting.md.

Operating pattern

1. Identify whether the user is using Hugging Face Transformers, PaddleHub, or a downloaded checkpoint zip.
2. Normalize the requested model to a supported HFL id or PaddleHub module name using references/loading-workflows.md.
3. For Transformers, prefer BertTokenizer plus BertModel, or AutoTokenizer plus AutoModel. Do not use RobertaTokenizer or RobertaModel for this repository's Chinese RoBERTa-wwm family.
4. Decide the loading mode before calling from_pretrained:
   • offline/cache validation: use local_files_only=True or the bundled checker's default offline mode;
   • online download: make the network action explicit and ensure the user expects checkpoint downloads;
   • local checkpoint directory: verify the directory contains files matching the framework path being used.
5. If the goal is only to validate a model id or check local cache state, run scripts/check_transformers_model.py instead of writing ad hoc loading code.

Bundled helper

Run the checker from this sub-skill directory or provide its path explicitly:

python scripts/check_transformers_model.py --help
python scripts/check_transformers_model.py hfl/rbt3 --try-load-tokenizer --offline-only
python scripts/check_transformers_model.py --model-id hfl/chinese-roberta-wwm-ext --try-load-config --allow-download

The helper validates the bundled model-id map and imports the expected Transformers classes. It does not download by default. It exits nonzero for unknown ids, missing imports/backends, or any requested config/tokenizer/model load that fails.

References

• references/loading-workflows.md: model-id and PaddleHub mappings, offline/online/cache workflows, and checkpoint-format notes.
• references/api-reference.md: verified public APIs and signatures for the minimum Transformers workflow.
• references/troubleshooting.md: workflow-specific failure diagnosis and fixes.