A 144M-parameter System One model on the multilingual mmBERT-small encoder. Takes a state, a question and 2 to 20 options and returns one decision with probabilities for choice, score and yes/no questions, on a CPU.
Small open typed-decision model with calibrated probabilities, first-class Nepali coverage.
Decides
choice, score, noul, classify, route
choice, score, noul, classify, route
Architecture
julia
mira
Fine-tuned from
jhu-clsp/mmbert-small
jhu-clsp/mmbert-small
License
apache-2.0
apache-2.0
Availability
Open weights
Open weights
Hosted by
—
—
Input price
—
—
Decision accuracy
73.2%
74.5%
Calibration error
—
0.025
Valid action rate
—
100.0%
Median latency
—
30 ms
p95 latency
—
43 ms
Evaluation suite
typed-decisions test set (400 cases, 2,000 questions)
s1-decision-bench
Latest version
1.0.0
Figures are from each model’s manifest; accuracy and latency are what the publishers report, on their own suites and hardware. Add a third model.
Questions
What is the difference between julia-1 and mira?
julia-1 is from Supersonic Labs and mira from SAGEA. Both have open weights you can download and run. Both answer choice, score, noul, classify and route questions.
Which is more accurate, julia-1 or mira?
They report on different suites — julia-1 73.2% on typed-decisions test set (400 cases, 2,000 questions), mira 74.5% on s1-decision-bench — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, julia-1 or mira?
julia-1: Free (open weights). mira: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run julia-1 or mira locally?
Yes, both: systemone pull supersonic-labs/julia-1 and systemone pull sagea/mira download the weights.
