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.
Native MLX FP16 conversion of Laya for Apple silicon. The same ModernBERT-large encoder, decision transformer and heads, running in MLX with no PyTorch; the checkpoint Laya Studio fine-tunes from.
Decides
choice, score, noul, classify, route
choice, score, noul
Architecture
julia
laya
Fine-tuned from
jhu-clsp/mmbert-small
convai-innovations/laya
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
73.2%
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Calibration error
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Valid action rate
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Median latency
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p95 latency
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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 laya-mlx?
julia-1 is from Supersonic Labs and laya-mlx from aac6fef. Both have open weights you can download and run. Both answer choice, score and noul questions. Only julia-1 answers classify and route. julia-1 reads up to 8K tokens of state, against 512 tokens for laya-mlx. julia-1 is the smaller model, at 144M parameters to 421M.
Which is more accurate, julia-1 or laya-mlx?
Only julia-1 publishes an accuracy figure (73.2% on typed-decisions test set (400 cases, 2,000 questions)); laya-mlx does not, so there is no comparison to make without your own test.
Which is cheaper, julia-1 or laya-mlx?
julia-1: Free (open weights). laya-mlx: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run julia-1 or laya-mlx locally?
Yes, both: systemone pull supersonic-labs/julia-1 and systemone pull aac6fef/laya-mlx download the weights.
Evaluation suite
typed-decisions test set (400 cases, 2,000 questions)
