To prevent Transformers from loading custom Python code from a model repository, leave trust_remote_code unset or set it to False. That does not control how checkpoint weights are deserialized: prefer .safetensors files and avoid unsafe pickle loading for untrusted checkpoints. These controls reduce specific loading-time risks; they do not guarantee that a model or its runtime is safe.
Disable custom repository code in Transformers
Transformers AutoClass loaders such as AutoModel and AutoTokenizer can load custom model code when you explicitly opt in with trust_remote_code=True. The Transformers guide says, “Set trust_remote_code=True in from_pretrained() to load a custom model.” If you do not need that code, do not pass the setting; if a shared configuration or wrapper supplies it, set it to False and check that no wrapper overrides it. See the Transformers model-loading guide.
from transformers import AutoModel, AutoTokenizer
model_id = "organization/model-name"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=False)
model = AutoModel.from_pretrained(model_id, trust_remote_code=False)Some architectures require custom repository code and may not load when this option is disabled. In that case, enabling custom code is a separate trust decision—not a requirement to accept pickle-based weights.
Use a safer checkpoint format
trust_remote_code governs custom Python code for Transformers AutoClass loading. It does not disable code execution that can occur during unsafe checkpoint deserialization. Transformers describes pickle as insecure and loads safetensors weights when they are available. Prefer model repositories that provide .safetensors weights; availability depends on the model and repository. See Transformers’ loading-models documentation.
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When using Hugging Face Hub serialization helpers such as load_state_dict_from_file or load_torch_model, retain the documented safe=True default. Safe mode rejects a pickle file rather than falling back to it; safe=False permits that fallback. Do not use it for an untrusted checkpoint. The Hub documentation also describes weights_only=True for pickle loading, but its restricted-unpickler protection depends on PyTorch: the documentation says it has no effect before PyTorch 1.13, which lacks that restricted unpickler. Check the runtime version rather than assuming the option provides protection. See Hugging Face Hub serialization documentation.
Keep the controls separate
| Control | What it addresses | Trade-off or qualification |
|---|---|---|
trust_remote_code=False |
Loading custom Python code from a model repository through Transformers AutoClass loading. | Models that require custom code may not load. |
Safetensors or Hub safe=True |
Checkpoint deserialization risks associated with pickle; safe mode rejects pickle rather than allowing fallback. | A repository may not provide safetensors, and safe mode can reject a pickle-only checkpoint. |
weights_only=True |
Uses PyTorch’s restricted unpickler where supported. | According to the Hub documentation, it has no effect on PyTorch versions earlier than 1.13. |
Pinning revision |
Reduces the chance that code changes between runs and makes the selected revision reproducible. | Pinning does not establish that the reviewed code is benign. |
If custom code is necessary, review and pin it
- Inspect the model repository’s custom code and record where the reviewed files came from.
- Choose the specific reviewed commit, then pass its commit hash using
revisioninfrom_pretrained(), rather than relying on a moving branch. - Keep checkpoint-format precautions in place independently; enabling custom code does not make pickle deserialization safe.
Transformers documents revision pinning as an additional security measure because repository code can change. A pinned revision improves reproducibility and limits drift, but it is not a substitute for reviewing code. See the Transformers guide to custom models and revisions.
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Understand what these settings do not cover
These measures target particular loading-time execution paths. They do not prove that a repository, weights, dependencies, or runtime are safe, and they do not prevent every form of harmful model behavior. Hugging Face Text Generation Inference has separate security guidance tied to that serving product and its TGI 2.0 behavior; do not transfer its command-line or environment settings to Transformers Python code. See TGI model-safety guidance.
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