# Jericho Siahaya: Open-Weight Models and Datasets
Source: https://savrn.com/model-publishers/jerichosiahaya
Markdown alternate of the page above; the site index is https://savrn.com/llms.txt

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## Models

Model · Zero-shot classification

### [naluri-xlmr-base](https://savrn.com/models/naluri-xlmr-base)

[Jericho Siahaya](https://savrn.com/model-publishers/jerichosiahaya)

Instinctive, multilingual decisions in one pass. Naluri (Indonesian for instinct; Neural Answering via Label Understanding and Rapid Inference) is a small System One decision model: about 280M parameters, built on XLM-RoBERTa base. You give it a text or JSON state and a typed question with its options written out. It returns calibrated probabilities over those options in a single forward pass, taking about 5 ms per question on a laptop GPU and about 50–60 ms on a CPU. It never generates text. It supports the same three question types as TypeSafe Jev and Laya: Code, training pipeline and full report: github.com/jerichosiahaya/naluri. - Option descriptions matter. Naluri reads them, so clear…

Open weights cc-by-nc-4.0 278M parameters 514 tokens

[View model](https://savrn.com/models/naluri-xlmr-base)

## Explore More

- [All model publishers](https://savrn.com/model-publishers)
- [The model directory](https://savrn.com/models)
- [The dataset directory](https://savrn.com/datasets)

## Source

- Listed from their public repositories, read 2026-10-02.
- [Hugging Face profile](https://huggingface.co/jerichosiahaya)
- [How the hub is built](https://savrn.com/model-hub/methodology)