Quyet-1.0-Tiny

Quyet-1.0-Tiny is a decision model: given a state (any text, JSON or conversation) and one or more typed questions, it picks one option per question and returns calibrated probabilities. Question types: choice (pick one label), score (an ordered scale) and noul (true / false). It is part of the Quyet 1.0 family (Large, Medium, Small, Small-EN, Tiny), released by Chinh Nguyen under Apache-2.0.

How to use

Live demo: try Quyet-1.0-Large in your browser at quyet.ai.

pip install quyet

import quyet

m = quyet.load("chinhnc/Quyet-1.0-Tiny")          # pip install quyet; downloads from Hugging Face
r = m.predict(
    {"message": "Please close my card, I lost it yesterday."},
    {"intent": {"type": "choice", "instructions": "What does the customer want?",
                 "criteria": {"cancel": "close the card", "limit": "change the limit", "other": None}},
     "urgent": {"type": "noul", "instructions": "The request is urgent."},
     "mood": {"type": "score", "instructions": "How upset is the customer?", "criteria": ["calm", "annoyed", "angry"]}},
)
print(r["answers"])   # {"intent": {"choice": ..., "confidence": ..., "probabilities": {...}}, "urgent": {"noul": P(true)}, ...}

Runs on CPU or any GPU. It reads the question, every option and the state in one pass (8,192 tokens) and scores each option; probabilities are temperature-calibrated per question type and option count.

Answers follow the TypeSafe /v1/systemone shape: choice (with probabilities), score (expected level, probabilities, legend) and noul (P(true)). At most 10 options per question. Only the state is ever truncated: conversation lists keep their most recent turns, other states keep their beginning.

Credits

• mmBERT-small by the mmBERT authors, JHU CLSP (MIT); first 16 of its 22 layers.
• Distilled from Quyet-1.0-Medium, which is fine-tuned from Qwen3.5-4B by Alibaba Cloud (Apache-2.0): https://huggingface.co/Qwen/Qwen3.5-4B.
• The option rendering follows Laya by Convai Innovations (Apache-2.0); no Laya weights are used.

Citation

@misc{quyet2026,
  title  = {Quyet 1.0: calibrated decision models},
  author = {Chinh Nguyen},
  year   = {2026},
  url    = {https://huggingface.co/chinhnc/Quyet-1.0-Tiny}
}

License

Apache-2.0. Keep the NOTICE file (it starts with "Quyet by Chinh Nguyen") when you redistribute this model or anything derived from it. Questions and issues: email@quyet.ai.