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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11For Transformers’ AutoClass loaders, keep trust_remote_code unset or set it to False. That prevents Transformers from loading custom Python code from a model repository through this option—but it does not control how checkpoint weights are deserialized. Prefer safetensors, and treat any pickle-based checkpoint as a separate trust decision.
Disable custom repository code in Transformers
Transformers requires an explicit opt-in to load custom model code that is not implemented in the library. Its documentation states: “Set trust_remote_code=True in from_pretrained() to load a custom model.” Hugging Face’s model-loading guide covers this setting and the custom-model loading path.
When using AutoModel, AutoTokenizer, or another AutoClass, omit trust_remote_code=True. The default is not to trust remote code. If a wrapper or shared configuration passes the setting through, explicitly set it to False and check that no later layer overrides it:
from transformers import AutoModel, AutoTokenizer
model_id = "organization/model"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=False)
model = AutoModel.from_pretrained(model_id, trust_remote_code=False)If the architecture depends on custom repository code, loading it this way may fail or may not support that model. Do not turn the option on merely to get past an error without first assessing the code.
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Control checkpoint deserialization separately
Disabling trust_remote_code is not a general switch that disables all code execution during loading. Checkpoint formats and serialization helpers have separate security behavior. Transformers describes pickle deserialization as insecure because it can execute arbitrary code, and recommends safetensors when available. Transformers loads safetensors when they are available; whether a particular repository provides them depends on that model.
For the lower-level Hugging Face Hub helpers load_state_dict_from_file and load_torch_model, retain the documented safe=True setting. Safe mode rejects a pickle file rather than falling back to it; safe=False permits pickle fallback. The Hub serialization reference also documents weights_only=True for restricted pickle loading.
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weights_only=True is not equally protective on every PyTorch version: the Hub reference says it has no effect before PyTorch 1.13, because those versions lack the restricted unpickler. Check the version actually used by your runtime. For an untrusted checkpoint, do not rely on this option on an older version; prefer safetensors or reject the checkpoint.
When custom code is genuinely required
If a model cannot be used without its custom code, review the repository code and record which revision you assessed. Then pin the load to that specific commit hash with revision, rather than following a branch or other moving reference:
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model = AutoModel.from_pretrained(
"organization/model",
trust_remote_code=True,
revision="<reviewed-commit-hash>",
)The Transformers guide to custom models and revisions presents revision pinning as an additional security measure because repository code can change. Pinning makes the loaded revision more reproducible and reduces drift; it does not prove that the reviewed code is benign. It also does not change the separate risks of the weight format.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Keep the controls in their proper scope
trust_remote_code=Falseaddresses Transformers’ custom repository Python loading path; it is not a blanket sandbox for the model-loading process.- Safetensors and the Hub serialization options address checkpoint deserialization, not whether custom model code is enabled.
- Enabling one path does not automatically enable or secure the other. Make each choice deliberately.
- These measures reduce specific loading-time risks. They do not establish that a repository, its weights, dependencies, or runtime are safe, nor do they prevent every harmful behavior a model could produce.
Hugging Face Text Generation Inference has separate security guidance, including behavior specific to TGI 2.0. Its command-line and environment settings apply to that serving product and should not be copied as substitutes for the Transformers Python controls described here. See the TGI model-safety guidance.
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