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.
Native MLX FP16 conversion of Laya's multilingual checkpoint (mmBERT) for Apple silicon, with a 1,024-token context. The base for multilingual fine-tunes in Laya Studio.
Decides
choice, score, noul
choice, score, noul
Architecture
laya
laya
Fine-tuned from
convai-innovations/laya
convaiinnovations/laya-multilingual
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
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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 laya-mlx and laya-multilingual-mlx?
laya-mlx is from aac6fef and laya-multilingual-mlx from aac6fef. Both have open weights you can download and run. Both answer choice, score and noul questions. laya-multilingual-mlx reads up to 1K tokens of state, against 512 tokens for laya-mlx. laya-multilingual-mlx is the smaller model, at 322M parameters to 421M.
Which is more accurate, laya-mlx or laya-multilingual-mlx?
Neither publishes an accuracy figure. Test both on your own labelled examples.
Which is cheaper, laya-mlx or laya-multilingual-mlx?
laya-mlx: Free (open weights). laya-multilingual-mlx: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run laya-mlx or laya-multilingual-mlx locally?
Yes, both: systemone pull aac6fef/laya-mlx and systemone pull aac6fef/laya-multilingual-mlx download the weights.
