MODEL PROFILE · 模型档案

opendecider-small-GGUF

manjunathshiva · opendecider-small-GGUF

GGUF builds of OpenDecider-small, the 4B OpenDecider decision model, for **LM Studio**, **Ollama** and anything else built on llama.cpp. Best for decisions it has never seen: the best general accuracy and calibration of the 4B models (zero-shot on typed-decisions). A decision model answers typed questions (`choice`, `score`, yes/no) about any text or JSON with a probability for every option, instead of generating text. The opendecider package builds the exact prompt the model was trained on and reads the option probabilities from the server's token log-probabilities. On typed-decisions (2,000 questions), the Q8_0 build gives the same top answer as the full-precision model on 1,975 (98.8%), at the same accuracy: Measured through Ollama's llama.cpp engine; LM Studio runs the same engine and returned identical log-probabilities on the same file. **Q8_0 is the recommended file**; Q4_K_M is for machines with little memory.

译文排队中,当前展示来源原文。

license:apache-2.0
上下文窗口来源未披露
输入 → 输出待核验 → 待核验
模型权重待核验
档案更新时间2026/11/2 19:16:12
OVERVIEW

模型介绍

GGUF builds of OpenDecider-small, the 4B OpenDecider decision model, for **LM Studio**, **Ollama** and anything else built on llama.cpp. Best for decisions it has never seen: the best general accuracy and calibration of the 4B models (zero-shot on typed-decisions). A decision model answers typed questions (`choice`, `score`, yes/no) about any text or JSON with a probability for every option, instead of generating text. The opendecider package builds the exact prompt the model was trained on and reads the option probabilities from the server's token log-probabilities. On typed-decisions (2,000 questions), the Q8_0 build gives the same top answer as the full-precision model on 1,975 (98.8%), at the same accuracy: Measured through Ollama's llama.cpp engine; LM Studio runs the same engine and returned identical log-probabilities on the same file. **Q8_0 is the recommended file**; Q4_K_M is for machines with little memory.

主要特点

特点摘要待依据来源补齐,不凭模型名称推断能力。

局限与注意事项

暂无足够来源支持具体缺点结论。请重点验证实际任务表现、时延、数据政策、许可与版本生命周期。

PRICING & COST

来源报价与费用分析

同一模型在不同渠道、地区和配置下可能采用不同价格。下表独立保留各渠道报价,不将渠道价格视为厂商官方统一价格。

来源 / 渠道输入输出计费单位采集时间
暂未取得可核验报价,请查阅来源渠道。

按月估算基础 Token 费用

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预估不包含缓存优惠、阶梯定价、多模态附加费用、网络检索、税费及推理额外输出。

INDEPENDENT BENCHMARKS

公开评测与运行配置

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。

查看完整已收录榜单 →

常见问题

开放权重是否代表可以免费商用?

不一定。应核对具体版本许可证、商用门槛和使用政策。自部署还会产生算力和维护成本。

这些价格是实时结算价格吗?

不是实时结算价格,而是带采集时间的来源报价快照。请到对应渠道核对当前版本、区域、折扣和计费方式。

资料来源与可追溯记录

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。