A library of pretrained machine-learning models for text, vision, audio, and multimodal tasks.
The root AGENTS.md and CLAUDE.md files link to .ai/AGENTS.md. This short guide covers contribution coordination and generated models, including how to edit and check both legacy copies and current modular sources.
├── · symlink → .ai/AGENTS.md ├── · symlink → .ai/AGENTS.md ├── .ai │ └── ├── docs │ └── source │ └── en │ └── └──
01 / Generated file guards
The file describes two mechanisms for maintaining standalone model files. It permits existing copied blocks to be maintained through their source but directs new work toward modular files.
**Do not add new `# Copied from` statements**; write a modular file instead.
02 / Generated file guards
A named modular file also generates another model family. The guide requires regeneration and review of every changed output, so a fix cannot quietly leave dependent generated code stale.
**Other models inherit your modular file.** `modular_deepseek_vl.py` also generates `modeling_deepseek_vl_hybrid.py`.
03 / Architecture as narrative
AttributeError is intentional converter syntax for removing inherited members. The file explains that replacing it with an apparently sensible value can change model sharding behavior.
**`attr = AttributeError()` deletes an inherited attribute** — it is an instruction to the converter, not a bug or placeholder.
04 / Contribution etiquette
Before a PR, agents must coordinate on the issue and inspect open work. The guide supplies issue and PR queries and asks for an explanation when a different approach warrants another patch.
Check for overlapping work, and do not open a second PR for a fix already covered:
These instructions explain how to work in Transformers. Modem shows your agents what customers said, who is affected, and what changed.
