Quyet

Quyet is a family of decision models. Give it a state (any text, JSON or conversation) and one or more typed questions; it picks one option per question and returns calibrated probabilities. Nothing is generated: every answer is a probability distribution over the options you supplied.

This package is the runtime for all Quyet 1.0 models. The weights are on Hugging Face.

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

The models are built for English and also tuned for Vietnamese; other languages work, with lower accuracy. Small-EN is trained on English only.

model	kind	parameters
Quyet-1.0-Large	LLM (GPU)	31.3B (30.7B text)
Quyet-1.0-Medium	LLM (GPU)	4.66B (4.21B text)
Quyet-1.0-Small	encoder (CPU or GPU)	328M
Quyet-1.0-Small-EN	encoder (CPU or GPU)	153M
Quyet-1.0-Tiny	encoder (CPU or GPU)	183M

Install

pip install quyet                 # CPU or single GPU
pip install "quyet[multi-gpu]"    # spread Quyet-1.0-Large over several GPUs (device_map="auto")

Python 3.10 or newer. The first quyet.load downloads the weights from Hugging Face.

Use

import quyet

m = quyet.load("chinhnc/Quyet-1.0-Small")
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"])

quyet.load(name_or_path, device=None, revision=None, dtype=None, device_map=None) accepts a Hugging Face repo id or a local directory. predict_many answers a list of requests; raw_probabilities returns the uncalibrated distributions.

Question types and answers

Answers follow the TypeSafe /v1/systemone shape:

• choice: pick one label from criteria (a dict of label to description, or a list of labels). Answer: choice, confidence, probabilities.
• score: an ordered scale given as a list of levels. Answer: expected level score, probabilities, legend.
• noul: a statement that is true or false. Answer: noul = P(true).

Probabilities are temperature-calibrated per question type and option count.

Command line

quyet predict chinhnc/Quyet-1.0-Small < requests.jsonl > answers.jsonl

Each input line is {"state": ..., "questions": {...}}; each output line is the response or {"error": ...}. Options: --input FILE, --device cpu|cuda|cuda:1, --strict (fail instead of truncating a long state).

Visual example

Gem Run turns model decisions into a gem-collecting race with English and Vietnamese instructions, rapidly increasing speed, and a stop at the first rock. The source checkout includes an offline replay and a runner for your own measurements.

python examples/gem_run/replay.py --open   # from a source checkout; no model download needed

Limits

• At most 10 options per question; more are rejected, not truncated.
• Only the state is ever truncated: conversation lists keep their most recent turns, other states keep their beginning. Encoders read 8,192 tokens; the LLMs keep up to 6,000 state tokens.
• Not evaluated for safety, bias or adversarial robustness; do not use as the only control for high-stakes decisions.

Licence and credit

Apache-2.0 for the code and the weights. Keep the NOTICE file, which starts with "Quyet by Chinh Nguyen", when you redistribute this package, a model, or anything derived from them.

@misc{quyet2026,
  title  = {Quyet 1.0: calibrated decision models},
  author = {Chinh Nguyen},
  year   = {2026},
  url    = {https://github.com/ncchinh/quyet}
}

Questions and issues: GitHub issues or email@quyet.ai.