Prefer safetensors weights loaded through an official library, inspect the model’s files and documentation, and pin the exact revision you reviewed. Pause if a repository requires pickle weights or custom Python code: those add risks that a clean Hub scan cannot rule out. Safetensors reduces the specific risk of unsafe pickle deserialization; it does not certify the repository or model as safe.
What to check before downloading a model
Start on the model’s Hub page, not with a download command. Confirm that the repository is the one you intended to use and examine its owner, model card, files, and revision history. Hugging Face recommends that model cards explain intended use, training details and hardware requirements, evaluations, limitations, and biases; see the model release checklist.
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- Task and intended use: Check that the model is meant for your task and note restrictions or warnings about how it should be used.
- License: Read the repository’s license and terms before using or redistributing the model. A download does not grant rights beyond those terms.
- Files: Identify weight files, Python modules, setup scripts, and dependency declarations. Look for
.safetensorsweights and note whether the repository asks you to run custom code. - Maintenance and requirements: Review changes to the repository and whether your installed library versions, hardware, and dependencies match its stated requirements.
A model card is useful disclosure, not a guarantee that claims are complete or that the model is secure, accurate, or suitable for your use.
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Some model weight files use Python’s pickle serialization. Deserializing an untrusted pickle can execute code, so loading the file is not merely reading numerical data. Safetensors is designed as a tensor format that avoids pickle deserialization for weights. When a model and library support it, prefer that format and use the library’s safe loading path. Hugging Face’s serialization documentation describes safe loading helpers that default to safe=True, which uses the safetensors loader.
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This protection is limited to the weight file. It does not inspect or neutralize Python code in the repository, unsafe dependencies, setup scripts, or the model’s behavior when run.
If a pickle checkpoint is unavoidable, do not manually load an untrusted file with unrestricted pickle deserialization. The serialization documentation describes weights_only=True as a restricted-unpickler option for intentional pickle loading, but it does not make an untrusted checkpoint risk-free. That option has no effect on PyTorch versions below 1.13, which do not include that restricted unpickler. Confirm the behavior for your installed versions before relying on it.
Transformers and Diffusers loading
Transformers’ version 4.57.1 documentation describes loading models through the library and recommends safetensors where available. Check the documentation for your installed version before relying on version-specific behavior: Loading models in Transformers 4.57.1.
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Diffusers documents that it loads safetensors automatically in the described case when the file is available and the library is installed; setting use_safetensors=True makes the preference explicit. Its safetensors guidance also describes using the Hub conversion workflow for pickle files, avoiding the need to download and locally deserialize a potentially unsafe pickle just to convert it.
What Hugging Face scans do—and do not—tell you
Hugging Face documents ClamAV scanning and pickle-import scanning. The pickle scanner extracts imports for review without executing the pickle. A scan result can help identify concerns, but it is not a security audit: Hugging Face says the scanning is best-effort and “not 100% foolproof,” and notes that it does not actively audit Python packages. The platform’s pickle-scanning documentation says that “it’s your responsibility as a user to check if something is safe or not.”
Do not treat a clean scan or the absence of a warning as proof that a repository is safe. Review flagged imports and files yourself, and consider repository changes as well as the scan result.
When custom repository code is involved
Some Transformers repositories provide Python code for model architectures or related components that are not included in the library’s built-in classes. Loading that code requires trust_remote_code=True. The flag is an explicit decision to run repository code; it is not a safety check or sandbox.
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- Find out which custom files the model needs, such as
modeling_*.py, custom tokenizer or pipeline code, and any setup or dependency files. - Read the relevant source and assess whether you trust its author and maintenance history. If you cannot review the code or establish a reason to trust it, do not enable remote code.
- Identify the full commit hash for the exact repository version you reviewed. The Transformers documentation recommends pinning a commit when enabling custom code. Re-review the code if you change the pinned revision.
- If you proceed, run unfamiliar code in a disposable, isolated environment with minimal permissions and no sensitive credentials. Isolation is prudent security practice, not a guarantee that code is harmless.
See the Transformers custom-model guidance for version 4.57.1, and verify instructions against your installed version.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Download only what you need and pin the revision
The Hub provides hf_hub_download for an individual file and snapshot_download for a repository snapshot. Both can use a revision; the download guide explains that a revision can be a branch, tag, or commit. For a reproducible download, use the full commit hash rather than a branch name that can move. The guide also documents file allow and ignore patterns to include or exclude artifacts: Download files from the Hub.
For example, adapt this pattern to the repository, commit hash, and files you actually need:
from huggingface_hub import hf_hub_download
path = hf_hub_download(
repo_id="organization/model",
filename="model.safetensors",
revision="FULL_COMMIT_HASH",
)Replace the sample repository ID, filename, and revision with verified values; the placeholder hash is not a usable revision. Downloading a file does not make it safe to load. If you need custom code or a full snapshot, apply the same review and revision-pinning checks to those files.
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Requesting access to a gated model
Gating is an access-control mechanism, not an endorsement or a safety certification. Access is controlled by the model author, and an access request may share your account username and email with that author. Check the model’s terms and decide whether that disclosure is acceptable before requesting access. After approval, scripts need authentication to download gated files. Keep any Hub access token private and out of source code, notebooks, and logs. Hugging Face explains the process in its gated models documentation.
A practical safety checklist
- Verify the intended repository and review its owner, model card, license, task, limitations, and hardware requirements.
- Prefer safetensors weights with a supported library loading path; avoid unrestricted pickle loading for untrusted checkpoints.
- Inspect Python files, scripts, and dependency declarations. Do not enable
trust_remote_code=Truewithout deciding to trust the code. - Use a full commit hash for the version you reviewed, particularly when custom code is required; review again if the revision changes.
- Download only necessary files where the Hub tools allow it, and treat scan results as one signal rather than a verdict.
- For unfamiliar code, use an isolated environment with minimal permissions and no sensitive credentials.
- For gated repositories, consider the access-request disclosure and terms, then protect the token used to authenticate.
These steps reduce identifiable risks; they cannot certify that a model is safe, accurate, unbiased, properly licensed for a particular use, or free of vulnerabilities.
Quick Recap
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