Small open typed-decision model with calibrated probabilities, first-class Nepali coverage.
A local, Jev-compatible decision model from Rizzo AI Academy. XHToken's Spark-X2.5-4B with a merged typed-decisions LoRA, run on llama.cpp; yes/no, choice and score questions share one prefill of the state and are read from the answer-letter logits. No text is generated.
Decides
choice, score, noul, classify, route
choice, score, noul, classify, route
Architecture
mira
rizzo-flow
Fine-tuned from
jhu-clsp/mmbert-small
xhtoken/spark-x2.5-4b
License
apache-2.0
apache-2.0
Availability
Open weights
Open weights
Hosted by
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Input price
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Decision accuracy
74.5%
64.8%
Calibration error
0.025
0.112
Valid action rate
100.0%
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Median latency
30 ms
195 ms
p95 latency
43 ms
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Evaluation suite
s1-decision-bench
LocalLLaMA/typed-decisions test split (400 cases, 2,000 decisions), Q8_0
Latest version
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 mira and rizzo-flow?
mira is from SAGEA and rizzo-flow from Rizzo AI Academy. Both have open weights you can download and run. Both answer choice, score, noul, classify and route questions.
Which is more accurate, mira or rizzo-flow?
They report on different suites — mira 74.5% on s1-decision-bench, rizzo-flow 64.8% on LocalLLaMA/typed-decisions test split (400 cases, 2,000 decisions), Q8_0 — so the numbers do not rank them. Test both on your own labelled examples.
Which is cheaper, mira or rizzo-flow?
mira: Free (open weights). rizzo-flow: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Which is faster, mira or rizzo-flow?
By their publishers’ figures, mira answers in about 30 ms at the median and rizzo-flow in about 195 ms — measured on different hardware, so treat it as a rough guide.
Can I run mira or rizzo-flow locally?
Yes, both: systemone pull sagea/mira and systemone pull rizzo-ai-academy/rizzo-flow download the weights.
