I built a tiny (332MB) CPU-friendly model for document sorting that actually knows when to say "none fits" (BeeNara)

Hey r/LocalLLaMA!

​I wanted to share a small project I’ve been working on called BeeNara

​Why I built this:

I was looking for a way to automatically sort my local documents (invoices, letters, contracts) into my personal folders. While local LLMs are amazing, I noticed that smaller models (like Qwen3.5-4B) really struggle with one specific thing: admitting when a document doesn't fit into any of the provided categories. Instead of saying "I don't know", they tend to hallucinate and just shove the document into a random folder. Running a massive model just for basic sorting felt like overkill, especially on a laptop without a heavy GPU.

​What it does:

BeeNara is a tiny (332 MB) ONNX cross-encoder model. You give it a document and a custom list of your folder names (like "Tax 2025" or "Invoices"), and it puts the document in the right one.

The best part? It uses split-conformal prediction, meaning its confidence is highly calibrated. If it's not absolutely sure, or if none of your folders are a good match, it simply returns "none fits" and flags the document for human review.

​Key Features:

​Zero-shot: You just use plain text folder names. No fine-tuning or retraining needed.

​Fast & Local: Runs entirely offline on a laptop CPU in about 0.2–0.3 seconds per document (no PyTorch/GPU required, just ONNX runtime).

​Bilingual: Works seamlessly with English and German documents/folder names.

​High "None fits" recall: In benchmarks, it successfully catches 96.8% of documents where the correct folder is missing from the list.

​I originally built this as the category decider for a local document archivist tool, but you can easily use it standalone in Python.

​You can check out the model, code, and benchmark comparisons here: https://huggingface.co/Kwokou/BeeNara

​I'd love to hear your thoughts, feedback, or if you have ideas on how to improve it! Just wanted to share it with the community in case anyone else needs a fast, local "folder decider" that doesn't confidently lie to you.

​(Disclosure: I am the creator of this model!)