Prefer .safetensors weights loaded through an official library API, and treat every model repository as untrusted until you have reviewed it. Safetensors avoids the specific risk of executing code through pickle-based weight deserialization; it does not make a repository, its Python files, dependencies, or model behavior safe. Pause before loading pickle weights or enabling custom repository code.
Inspect the model repository before downloading
On the model page, verify that the owner and repository are the ones you intended to use. Read the model card (the repository’s README) and look for the model’s task, intended use, limitations, biases, license, training details, evaluation information, hardware requirements, and instructions. Hugging Face recommends that model cards explain these details in its model release checklist.
Then inspect the repository file list and recent changes. Pay particular attention to Python files such as modeling_*.py, custom pipeline or tokenizer code, setup scripts, and dependency declarations. Ask whether the model needs these files and whether you can understand and trust what they do. A model card or familiar-looking name is disclosure, not proof of safety.
Choose the safer weight format
Weight files are not all equivalent. Python pickle files can invoke code when deserialized. Safetensors is designed as a tensor format that avoids pickle deserialization for weights, so prefer the model’s .safetensors files when your library supports them. Hugging Face’s pickle scanning documentation explains the risk, and its serialization API documentation describes safe loading helpers that default to the safetensors loader.
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This protection is limited to the weight format. It does not inspect or neutralize repository Python code, dependencies, scripts, or the model’s intended behavior. Do not manually use unrestricted pickle loading for an untrusted checkpoint. The serialization documentation describes weights_only=True as a restricted-unpickler option for intentional pickle loading, but it does not make an untrusted file risk-free; it also has no effect on PyTorch versions below 1.13, which lack that restricted unpickler.
Transformers and Diffusers loading
Use the safe loading path documented for your installed library version. Transformers’ version 4.57.1 documentation describes loading models and recommends safetensors where available; consult the documentation matching your installed version before relying on a particular API default: Transformers model loading.
Diffusers documents that it loads safetensors automatically in the described case when the format is available and the library is installed; use_safetensors=True makes that preference explicit. If a repository only offers pickle weights, Diffusers points to the Hub conversion workflow so you can avoid downloading and locally deserializing the potentially unsafe pickle: Loading safetensors in Diffusers.
Interpret Hub scanning as a warning layer, not a safety verdict
Hugging Face describes scanning repositories with ClamAV and scanning pickle files for imports, which are surfaced for users to review. The documented pickle scan extracts imports without executing the pickle. However, Hugging Face says the scan is best-effort, does not actively audit Python packages, and is “not 100% foolproof.” Its documentation states that “it’s your responsibility as a user to check if something is safe or not.” See Hugging Face’s pickle scanning guidance.
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Make a deliberate decision about custom repository code
Some Transformers repositories supply Python code for model architectures or other functionality outside the library’s built-in classes. Loading that code requires trust_remote_code=True. The flag is an explicit decision to run code from the repository, not a security check or protective setting.
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- Read the relevant custom Python files and dependency declarations; investigate code you do not understand rather than assuming the model card explains it.
- Assess whether you trust the author and the repository’s history, and whether the custom code is genuinely necessary for the model.
- Identify the exact reviewed revision and use its full commit hash in the loading call’s
revisionargument. Transformers’ custom model documentation recommends pinning a commit hash when trusting remote code. - Only then enable
trust_remote_code=True. Re-review the code and pin if you change revisions.
Run unfamiliar code in a disposable, isolated environment with minimal permissions and no sensitive credentials. That is prudent security practice, not a guarantee that isolation will prevent every kind of harm.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Download only what you need and pin the version
Hugging Face provides hf_hub_download to download an individual file and snapshot_download to retrieve a repository snapshot. Both support selecting a revision; a revision can be a branch, tag, or commit, but a full commit hash is the choice for an exact, reproducible version. Where supported, use file allow/ignore patterns to avoid fetching unnecessary artifacts, including pickle files you do not need. See the Hub download guide.
Pinning makes it possible to use the version you reviewed rather than silently moving to a changed branch tip. It does not certify the pinned version as safe, and you should review a new revision before switching to it.
Understand gated-model access and protect credentials
Gating controls access; it is not a safety certification or endorsement. Depending on the model, requesting access may share your account username and email with the model author, and the author controls access. Consider the model’s terms and this disclosure before requesting access. After access is granted, scripts need authentication to download the gated files. Keep any access token private. Details are in Hugging Face’s gated models documentation.
Use this safety checklist before running a model
- Confirm the repository identity; read its model card, license, task, limitations, owner information, and hardware requirements.
- Prefer
.safetensorsweights and the library’s supported safe loading path; avoid unrestricted pickle loading. - Inspect custom Python files, setup scripts, and dependencies. Enable
trust_remote_code=Trueonly after reviewing and deciding to trust the code. - Pin a full commit hash for reviewed custom code and reproducible downloads; repeat the review when changing revisions.
- Download only required files where supported, and treat scan results as one signal rather than a guarantee.
- Use a disposable, minimally privileged environment for unfamiliar code, with no sensitive credentials present.
- For gated repositories, consider the terms and contact-data sharing before requesting access; protect download tokens.
These steps reduce identifiable risks; they do not certify a model as safe, accurate, unbiased, properly licensed for your use, or free of vulnerabilities.
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