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Pass a fixed revision to the API that loads or downloads the model. In Transformers, add revision="<commit-hash-or-fixed-tag>" to from_pretrained(); in huggingface_hub, pass the same kind of value to hf_hub_download() or snapshot_download(). A full commit ID gives the most durable repeatability. A tag can also work when the project treats it as immutable. Leaving the argument out uses the Transformers default, main, whose tip can change.
This guide shows how to select the revision, load complete models or individual files, keep several components on one commit, and reduce the risk of changing remote code.
What a Hugging Face revision is
A model repository on the Hugging Face Hub has a version history. A revision identifies one point in that history. Hugging Face APIs accept:
- Branches, such as
main. These are convenient but can move when new commits are pushed. - Tags, which usually express a release. A tag is suitable for reproducible work only if the project treats it as a fixed reference.
- Commit IDs, which identify one repository commit. A full commit ID is the clearest choice when you need the exact files and code used by a run.
Hugging Face’s model-sharing documentation recommends passing an explicit commit hash or a tag treated as fixed for reproducibility across runs (Hugging Face Transformers model-sharing documentation). The reference is repository-specific: a commit from one model repository is not a valid revision for a different base model, tokenizer, or adapter repository.
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Choose the right kind of revision
| Reference | Repeatability | Best use | Risk or limitation |
|---|---|---|---|
main or another branch |
Low over time | Experiments that intentionally follow the latest development state | The branch tip may advance between runs |
| Maintained release tag | Good if the tag is not moved | Readable release-based deployments | A mutable tag can point to different content later |
| Full commit ID | Highest for that repository | Production, audits, tests, and reproducible papers or pipelines | Less readable; you must copy the correct repository commit |
Pull-request ref such as refs/pr/3 |
Depends on the pull request’s lifecycle | Testing a proposed change | Not normally a permanent release reference |
For a release workflow, record both the human-readable tag (if one exists) and the resolved commit ID in your configuration or build metadata. The tag explains intent; the commit proves which repository state was used.
Find and record the revision before coding
Open the exact model repository, not a similarly named organization or fork, and inspect its commit history or release tags. Copy the revision associated with the configuration and weight files you intend to use. If the repository has multiple branches, verify that the commit is on the branch or release line your team selected. A model card may mention a recommended version, but the repository history is where you confirm the actual commit identifier.
Keep the model ID and revision together in one configuration record. For example, a deployment manifest might contain model_id, revision, the Transformers version, and the huggingface_hub version. Treat a revision change as a dependency upgrade: review it, test it, and make it visible in code review rather than silently replacing a value at runtime.
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Use the complete commit ID whenever possible. Interfaces may accept abbreviated hashes, but the official Hub download guidance recommends a full-length hash, and documentation does not define one minimum abbreviation that applies to every API. The complete value avoids ambiguity and remains portable between loading and download functions.
Pin a model with Transformers
1. Find the exact repository revision
Open the model repository’s version history and copy the commit ID associated with the files you intend to use. If the project publishes release tags, decide whether the tag is maintained as immutable. Do not copy a commit from a different repository, even when the repositories belong to the same project.
2. Pass revision to from_pretrained()
The Transformers model-loading API accepts a revision argument and defaults it to main (Transformers model API reference). Use your model’s actual ID and the revision you selected:
from transformers import AutoModel
model = AutoModel.from_pretrained(
"organization-or-user/model-name",
revision="<full-commit-hash>",
)The same argument is available on the corresponding auto classes for common model types. Pin the tokenizer, processor, or configuration as well when those objects are loaded from the Hub:
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from transformers import AutoConfig, AutoTokenizer, AutoModel
model_id = "organization-or-user/model-name"
revision = "<full-commit-hash>"
config = AutoConfig.from_pretrained(model_id, revision=revision)
tokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision)
model = AutoModel.from_pretrained(model_id, revision=revision, config=config)Using one explicit value makes the intent visible in code review. If you omit it, a later run can resolve the moving default branch to newer configuration, weights, tokenizer files, or custom code.
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3. Pin every separately loaded component
A model pipeline can involve more than the model weights. For example, a tokenizer, image processor, feature extractor, adapter, or generation configuration may be loaded in a separate call. Pass an explicit revision to each call that reads from the same repository. If a component lives in another repository, choose and record a revision for that repository independently.
base_id = "organization-or-user/base-model"
adapter_id = "organization-or-user/adapter"
base_revision = "<base-repository-commit>"
adapter_revision = "<adapter-repository-commit>"
base = AutoModel.from_pretrained(base_id, revision=base_revision)
adapter = AutoModel.from_pretrained(adapter_id, revision=adapter_revision)A commit identifier has meaning only inside the repository where it was resolved. Reusing base_revision for the adapter repository is incorrect unless that exact commit also exists there.
Download one file or a complete snapshot
Download an individual file with hf_hub_download()
Use hf_hub_download() when you need a specific file, such as a configuration or vocabulary file. Its revision parameter accepts a branch, tag, commit ID, or supported reference such as a pull-request ref. The Hub download guide recommends a full-length commit hash for this interface (Hugging Face Hub download guide):
from huggingface_hub import hf_hub_download
config_path = hf_hub_download(
repo_id="organization-or-user/model-name",
filename="config.json",
revision="<full-commit-hash>",
)
print(config_path)The returned path points to the cached file. The cache can contain files from several revisions, so the path and your recorded revision should travel with the build or experiment metadata.
Download a repository snapshot with snapshot_download()
For a complete repository state, use one snapshot request:
from huggingface_hub import snapshot_download
snapshot_path = snapshot_download(
repo_id="organization-or-user/model-name",
revision="<full-commit-hash>",
)
print(snapshot_path)A single snapshot request resolves one revision and downloads the repository contents for that point. This is generally safer than making many independent file requests against a moving branch. You can then open files beneath the returned snapshot directory in your own code.
Use a pull-request reference deliberately
The Hub download documentation shows references such as refs/pr/3. This is useful for testing a proposed change, not for a long-lived production pin: the pull request may be rebased, closed, or removed. If the change becomes a release, replace the pull-request reference with the resulting tag or commit ID.
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Separate calls made with revision="main" can resolve to different commits if the repository changes between calls. That can produce a mixed set—for example, a new configuration paired with older weights. The Hub cache guide recommends resolving the revision once for complex, multi-file integrations and reusing the resolved value (Hugging Face Hub cache guide).
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Prefer one snapshot for ordinary whole-repository use
If your application needs most of a repository, call snapshot_download() once with the selected revision. Every file you read then comes from that snapshot resolution.
Resolve once when individual downloads are necessary
If you must download files separately—for example, because different subsystems request them at different times—resolve the branch or tag to a commit once, then supply that resolved commit to every subsequent request for that repository. The exact resolver and return shape can vary with the installed huggingface_hub version, so check the current API reference for your pinned library version. Conceptually, the flow is:
- Choose a repository and initial reference, such as a release tag.
- Resolve that reference to a commit in that repository.
- Store the resulting commit ID with the run or build metadata.
- Pass that same commit ID to each
hf_hub_download()call. - Resolve a separate commit for every other repository involved.
Do not assume that a cache hit means two requests used the same revision. Cache entries preserve content, but your application still needs an explicit, recorded revision to explain which content was selected.
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Cache, offline, and CI considerations
The Hub cache is content-aware and can retain files from multiple revisions. That is useful for switching between experiments, but it can hide an omitted pin during local testing because an older file may already be present. A clean-environment test, a fresh container, or an explicitly selected snapshot helps demonstrate that the program can obtain the declared revision rather than relying on an accidental cache entry.
In continuous integration, put the revision in the job configuration or lockfile instead of constructing it from the current date or branch tip. Log the model ID and full revision at the beginning of a build. If a job downloads separate files, pass the same resolved commit to every request. If it downloads an entire repository, prefer one snapshot_download() call and archive the resulting metadata with the build artifacts.
For an offline or air-gapped run, first populate the cache or stage the snapshot while network access is available. The offline environment still needs the application to reference the intended revision and repository; an existing cache alone does not explain which version was selected. Keep the recorded commit with the staged files so operators can compare the deployed content with the approved configuration.
Pin custom model code carefully
Some repositories require trust_remote_code=True so Transformers can load Python code supplied by the repository. The Transformers loading guide recommends selecting a specific revision in this situation (Transformers model-loading guide):
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model = AutoModel.from_pretrained(
"organization-or-user/model-name",
revision="<full-commit-hash>",
trust_remote_code=True,
)Pinning ensures that a later repository update does not silently replace the code selected by your command. It is an additional control, not a security certification. Review the repository’s code, limit execution privileges where practical, and use the narrowest network and filesystem permissions your deployment permits. A fixed revision can preserve untrusted code just as reliably as trusted code.
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Upgrade and rollback without losing the old pin
When a new model release is needed, do not replace main with another moving branch as a shortcut. Select the new tag or commit, run the same loading and integration tests, and update the recorded value in a reviewed change. Retain the previous model ID and revision in release history so a failed deployment can be rebuilt from the earlier snapshot.
Compare all repositories in a pipeline during an upgrade. A new base-model commit may require a compatible adapter, tokenizer, or processor revision. Because each commit is scoped to its own repository, record a small map of repository IDs to revisions rather than one global hash. This also makes rollback precise: restore the prior map, not merely the base model’s value.
Verify that the pin is actually being used
- Check the call site: confirm every Hub-loading call has an explicit
revisionwhen reproducibility matters. - Check repository scope: ensure the commit belongs to the exact model, adapter, tokenizer, or dataset repository named in that call.
- Record the value: save the full commit ID or fixed tag in your lockfile, experiment record, container build, or deployment manifest.
- Inspect the resolved files: compare the downloaded configuration and weight filenames with the repository state you selected.
- Test a clean environment: run the load without relying on an already populated cache. This can reveal an omitted revision or an undeclared dependency.
- Pin the client deliberately: keep a known-compatible version of
transformersandhuggingface_hubin your environment, then consult the current API reference when upgrading.
Pinning the repository revision does not freeze every other input. Python dependencies, operating-system libraries, hardware kernels, environment variables, and your own preprocessing code can also affect results. Record those separately when exact reproduction is required.
Troubleshoot common revision mistakes
The model changes between runs
Look for a missing revision argument or an explicit branch such as main. Replace it with a full commit ID or a project tag that is documented as fixed. Apply the change to tokenizer, processor, adapter, and configuration calls as well as the model call.
Files appear to come from different versions
This often occurs when separate calls resolve a moving branch at different times. Use one snapshot_download() request, or resolve the branch once and reuse the resulting commit for every file request from that repository.
A revision works for one repository but not another
Resolve the second repository independently. Commit IDs are scoped to repositories; an identifier copied from the base model does not automatically identify an adapter or tokenizer revision.
A shortened hash is rejected
Use the full commit ID. Documentation examples across interfaces do not establish one universal minimum abbreviation length, while the Hub download guide specifically recommends a full-length hash. A full value avoids ambiguity and is the most portable choice.
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Pull-request refs are suitable for temporary testing. Once the change is accepted, select the resulting commit or a fixed release tag and update the configuration.
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Recommended patterns by workflow
| Workflow | Recommended reference | Implementation pattern |
|---|---|---|
| Exploring the latest development version | A branch, knowingly | Use main and expect changes; do not call the run reproducible |
| Reproducible experiment | Full commit ID | Pass it to every load and record it with the experiment |
| Production release | Full commit ID, optionally accompanied by a fixed tag | Build from one pinned snapshot and retain the ID in deployment metadata |
| Testing an unmerged change | Pull-request ref or its current commit | Use temporarily, then replace it when the change is released |
| Several files from one repository | One resolved commit | Resolve once and reuse it, or download one snapshot |
| Base model plus adapter | One revision per repository | Store separate identifiers and pass each to the appropriate call |
Minimal complete examples
Transformers loading
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_id = "organization-or-user/model-name"
revision = "<full-commit-hash>"
tokenizer = AutoTokenizer.from_pretrained(
model_id,
revision=revision,
)
model = AutoModelForSequenceClassification.from_pretrained(
model_id,
revision=revision,
)Hub snapshot download
from huggingface_hub import snapshot_download
path = snapshot_download(
repo_id="organization-or-user/model-name",
revision="<full-commit-hash>",
)
# Load the files under path with your application.Replace the example model ID and placeholder with values copied from the repository you are using. Keep the revision beside the model ID in configuration so an upgrade is an intentional, reviewable change.
Frequently Asked Questions
Can I pin a Hugging Face model by tag instead of a commit hash?
Yes. Transformers and Hub download APIs accept tags, but the tag must be treated as fixed by the project. If a tag can be moved, use the full commit ID it resolves to for stronger repeatability.
Does pinning the revision make remote model code safe?
No. It prevents the selected repository code from changing silently, but it does not prove that the code is trustworthy. Review repositories that require trust_remote_code=True and restrict execution privileges.
Should the same revision be used for a base model and an adapter?
No. Resolve and record a revision separately for each repository. A commit ID is valid only within the repository where it was created.
What happens if I omit the revision argument?
Transformers documents main as the default revision. A later run can therefore retrieve a newer branch tip, including changed configuration, weights, tokenizer files, or custom code.
Is one revision enough when I load a tokenizer and model separately?
Use the same revision value when both objects come from one repository. If either component comes from another repository, resolve and record that repository’s revision independently.
Should I use snapshot_download() or several hf_hub_download() calls?
Use one snapshot_download() call when you need an ordinary whole-repository state. If separate file calls are necessary, resolve the reference once and pass the resulting commit to every call for that repository.
Are pull-request refs suitable for production?
Usually not. A reference such as refs/pr/3 is useful for testing a proposed change, but the pull request can be rebased, closed, or removed. Replace it with the resulting commit or a fixed release tag.
Does a cached file prove that the requested revision was used?
No. A cache can contain files from multiple revisions. Record the explicit revision and test in a clean environment when you need to verify the selected content.
The Bottom Line
For a durable Hugging Face pin, pair each repository ID with a full commit ID and pass it through every Transformers or Hub download call. Use one snapshot, or resolve once and reuse the commit for separate files. Treat tags as fixed only when the project does, and review any repository that requires remote code execution.
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