MODEL PROFILE

opendecider-nano

manjunathshiva · opendecider-nano

**Open, calibrated System 1 decision model.** Give it a **state** (text, email, ticket or JSON) and **typed questions** (`choice`, `score`, `noul`); it returns a calibrated probability for every option in a single forward pass: **17 ms on an NVIDIA L40S, 18 ms on an Apple M4 Max, ~9 ms per question batched**. ~400M parameters, Apache-2.0. It never generates text, so there is nothing to parse and nothing to hallucinate. **Ahead of Laya like for like on typed-decisions: 0.796**, against 0.766 for Laya's typed-decisions checkpoint (+0.030, 95% CI +0.014 to +0.044), both fine-tuned on its train split. TypeSafe Jev scores 0.754 there zero-shot (measured through TypeSafe's own API): a reference, not a head-to-head. **Documentation: manjunathshiva.github.io/opendecider**: getting started, choosing a model, guides for serving, LM Studio, Ollama and vLLM and automating the confident decisions, plus the Python and HTTP API reference.

license:apache-2.0
Context windowNot disclosed
Input → Output待核验 → 待核验
Model weightsNot verified
Profile updated2026/11/2 19:16:56
OVERVIEW

Model overview

**Open, calibrated System 1 decision model.** Give it a **state** (text, email, ticket or JSON) and **typed questions** (`choice`, `score`, `noul`); it returns a calibrated probability for every option in a single forward pass: **17 ms on an NVIDIA L40S, 18 ms on an Apple M4 Max, ~9 ms per question batched**. ~400M parameters, Apache-2.0. It never generates text, so there is nothing to parse and nothing to hallucinate. **Ahead of Laya like for like on typed-decisions: 0.796**, against 0.766 for Laya's typed-decisions checkpoint (+0.030, 95% CI +0.014 to +0.044), both fine-tuned on its train split. TypeSafe Jev scores 0.754 there zero-shot (measured through TypeSafe's own API): a reference, not a head-to-head. **Documentation: manjunathshiva.github.io/opendecider**: getting started, choosing a model, guides for serving, LM Studio, Ollama and vLLM and automating the confident decisions, plus the Python and HTTP API reference.

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

PRICING & COST

Source prices & cost analysis

Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.

Source / channelInputOutputBilling unitCollected at
No verified prices yet. Check the source channel.

Estimate monthly base token costs

—

Estimates exclude cache discounts, tiered pricing, multimodal surcharges, web search, taxes and additional reasoning output.

INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

View full indexed rankings →

Frequently asked questions

Do open weights permit free commercial use?

Not necessarily. Check the version license, commercial conditions and usage policies. Self-hosting incurs compute and maintenance costs.

Are these real-time billing prices?

These are timestamped source snapshots, not live billing prices. Check the channel for current versions, regions, discounts and billing methods.

Sources & traceability

许可证:apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。