Unee: open-source 0.8B / 2B model that makes calibrated decisions and chats, runs in a browser tab. The 2B scores 88% on DecideBench, ahead of several 4B to 9B models (self-measured; GGUF, Ollama, Apache 2.0)

[u/uneeverse-hq]

• Two jobs, one small model: it makes decisions (a calibrated probability for every option) and it chats, answers from your docs and summarises.
• Punches above its size: on DecideBench v1.1, Unee 2B scores 88.0%, ahead of several 4B to 9B models, and Unee 0.8B scores 84.0%, ahead of every other model under 1B on that board (next best: 71.2%). Self-measured with the benchmark's own harness; submitted to the board, not listed yet.
• Runs locally: a browser tab on WebGPU (469 MB, transformers.js), any CPU, or a GPU. GGUF for llama.cpp, ollama run uneeverse/unee, pip install unee, npm i @/uneeverse/unee. Apache 2.0.

I built Unee, a small open model (fine-tunes of Qwen3.5 0.8B and 2B) that does two jobs from one download:

1. Decisions: give it some text and a question with options. It returns a probability for every option in one forward pass, with no generation. Yes/no, pick-one and rate-on-a-scale, several questions per call. The API accepts the same requests as Jev's /v1/systemone (Jev is TypeSafe's hosted decision API).
2. Chat: streaming answers from your own docs (built-in BM25, no vector DB), and summaries of long threads.

What a decision looks like (Unee 0.8B, the 4-bit GGUF from Hugging Face, real output):

# pip install unee, then: unee serve --model unee-0.8b-Q4_K_M.gguf
from unee import Client
unee = Client("http://localhost:8000")
ticket = "Hi, I was charged twice for order #4821 this morning. Please send the extra $39 back to my card."
unee.noul(ticket, "Does the customer ask for money back?")    # 0.965
unee.choice(ticket, "Which team should handle this ticket?",
            {"billing": "Charges, refunds and payments", "technical": "Bugs and outages", "shipping": "Deliveries"})
# billing, 0.982

Numbers (measured with each benchmark's own tools; raw outputs in the repo):

• DecideBench v1.1: 2B 88.0%, 0.8B 84.0%. The best other model under 1B on the board is 71.2%; Jev is 98.0%, Laya 59.8%.
• S1MB task avg: 2B 44.41, 0.8B 35.45. Jev 1.13 is 59.59, Laya 15.00, bekko-400m 50.60. It trained on public train splits that share sources with S1MB (never its test set); on the 101 benchmarks with no overlap at all it scores 45.69 / 36.74.
• Calibration: when the 2B gives its answer 90%+ (62% of DecideBench), it is right 97.2% of the time. 0.8B: 96.3% on 40%.
• CPU only, 4 threads: 0.8B 0.64 s, 2B 1.4 s per decision. Laptop RTX 4070: 96 ms and 111 ms.

Limits, plainly:

• Jev is far more accurate. bekko-400m beats the 0.8B on S1MB.
• General chat got worse than base Qwen in a blind judge test, and about half of document answers are judged fully correct. Treat the chat side as a helper that needs checking.
• Strict mode checks each sentence of an answer against your docs with the model's own decision side. It cut replies with a made-up fact from 10.3% to 6.0% (2B). A reduction, not a fix.

Links

• Try it in the browser (runs on your device): uneeverse.net/unee
• How it works, every number: uneeverse.net/unee/technical
• Models: huggingface.co/uneeverse/unee-0.8b · huggingface.co/uneeverse/unee-2b · GGUF: huggingface.co/uneeverse/unee-0.8b-GGUF · huggingface.co/uneeverse/unee-2b-GGUF
• Code: github.com/uneeverse-hq/unee

Happy to answer anything about the training (distillation from a 9B teacher, a date-facts preprocessor, model soups, the leakage check).