mlinter

A standalone linter for Hugging Face Transformers modeling files. It enforces structural conventions on every modeling_*.py, modular_*.py, configuration_*.py, image_processing_*.py, and video_processing_*.py file under src/transformers/models.

Installation

pip install git+https://github.com/huggingface/transformers-mlinter@main

When working on the transformers repo, mlinter is included in the quality extras:

pip install -e ".[quality]"

How rule registration works

• Rule metadata lives in mlinter/rules.toml.
• The TOML schema is versioned with a top-level version = 1. Custom files passed with --rules-toml must use the same schema version.
• Executable TRF rules are auto-discovered from trf*.py modules in the mlinter/ package.
• Each module must define a check(tree, file_path, source_lines) -> list[Violation] function.
• The module name determines the rule id: trf003.py → TRF003.
• A RULE_ID module-level constant is set automatically by the discovery mechanism.
• Every discovered rule must have a matching entry in the TOML file, and every TOML rule must have a matching module. Import-time validation fails if either side is missing.
• Suppressions use # trf-ignore: TRFXXX on the same line or the line immediately above the flagged construct.

How to add a new TRF rule

The repo ships an add-mlinter-rule skill under .ai/skills/ that walks an agent through duplicate detection, numbering, module creation, running against all models, and test scaffolding. Enable it for your agent of choice:

make claude   # symlinks .claude/skills -> .ai/skills (for Claude Code)
make codex    # symlinks .agents/skills -> .ai/skills (for Codex)

After that, invoke it as /add-mlinter-rule in a new session. To add a rule manually:

1. Add a [rules.TRFXXX] entry to mlinter/rules.toml.
2. Fill in description, default_enabled, explanation.what_it_does, explanation.why_bad, and explanation.diff. Optional model-level exceptions go in allowlist_models.
3. Create a new module mlinter/trfXXX.py with a check(tree, file_path, source_lines) -> list[Violation] function.
4. Use the RULE_ID module constant instead of hardcoding "TRFXXX" inside the check.
5. Add or update focused tests in tests/.

CLI usage

Run from the root of a transformers checkout:

# Check all modeling, modular, and configuration files
mlinter

# Only check files changed against a git base ref
mlinter --changed-only --base-ref origin/main

# List all available TRF rules and their default state
mlinter --list-rules

# Use a custom rules TOML instead of the bundled mlinter/rules.toml
mlinter --rules-toml /path/to/custom-rules.toml

# Show the installed mlinter version
mlinter --version

# Show detailed documentation for one rule
mlinter --rule TRF001

# Enable additional rules on top of the defaults
mlinter --enable-rules TRF003

# Enable every TRF rule, including ones disabled by default
mlinter --enable-all-trf-rules

# Emit GitHub Actions error annotations
mlinter --github-annotations

When installed from a git checkout or a git+https://... URL, mlinter --version includes a short commit hash suffix such as 0.1.1+gabcdef1.

You can also invoke it as a Python module:

python -m mlinter

The lint cache is stored in the user cache directory instead of next to the installed package: $XDG_CACHE_HOME/mlinter/.mlinter_cache.json on Linux, ~/Library/Caches/mlinter/.mlinter_cache.json on macOS, and %LOCALAPPDATA%\mlinter\.mlinter_cache.json on Windows.

Python API

Import the supported Python API from the package root:

from mlinter import TRF001, analyze_file, model_dir_name, render_rules_reference

mlinter.mlinter and mlinter._helpers are implementation modules and may change without a compatibility promise.

Development

git clone https://github.com/huggingface/transformers-mlinter
cd transformers-mlinter
pip install -e ".[dev]"

Run the tests:

pytest tests/

Releasing

See docs/release.md for the current release process.