MODEL PROFILE · 模型档案

Ornith-1.5-35B-A3B-GGUF

na0x2c6 · Ornith-1.5-35B-A3B-GGUF

GGUF quantizations of ornith-ai/Ornith-1.5-35B-A3B, made with an importance matrix, plus a KL-divergence comparison with the other published GGUFs of this model. > [!NOTE] > These files were made for a quantization experiment and are evaluated only by the KLD measurements below, not by task benchmarks, long-context use or speed tests. Check them on your own workload before relying on them. The four "comparison only" files are there so that the effect of `-hq` in the tables below can be checked; at each size the `-hq` file is better. All files are Q4_K_M with up to two kinds of changes. The `-hq` part raises the tensors outside the routed experts to Q8_0: attention, the linear-attention (SSM) projections, the shared expert, and the MTP projection. The `-exps` part changes the routed expert tensors (`ffn_{gate,up,down}_exps`) in layers 0-39. The token embedding and the output keep the Q4_K_M types (Q4_K and Q6_K). All files keep the built-in MTP (NextN) layer, so they work with `--spec-type draft-mtp`. For image input, use the vision projector from the official repo (`mmproj-Ornith-1.5-35B-BF16.gguf`).

译文排队中,当前展示来源原文。

文本生成license:mit
上下文窗口来源未披露
输入 → 输出文本 → 文本
模型权重待核验
档案更新时间2026/11/2 19:17:13
OVERVIEW

模型介绍

GGUF quantizations of ornith-ai/Ornith-1.5-35B-A3B, made with an importance matrix, plus a KL-divergence comparison with the other published GGUFs of this model. > [!NOTE] > These files were made for a quantization experiment and are evaluated only by the KLD measurements below, not by task benchmarks, long-context use or speed tests. Check them on your own workload before relying on them. The four "comparison only" files are there so that the effect of `-hq` in the tables below can be checked; at each size the `-hq` file is better. All files are Q4_K_M with up to two kinds of changes. The `-hq` part raises the tensors outside the routed experts to Q8_0: attention, the linear-attention (SSM) projections, the shared expert, and the MTP projection. The `-exps` part changes the routed expert tensors (`ffn_{gate,up,down}_exps`) in layers 0-39. The token embedding and the output keep the Q4_K_M types (Q4_K and Q6_K). All files keep the built-in MTP (NextN) layer, so they work with `--spec-type draft-mtp`. For image input, use the vision projector from the official repo (`mmproj-Ornith-1.5-35B-BF16.gguf`).

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

—

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:mit。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。