MODEL PROFILE · 模型档案

Muse-Glimmer-30B-Abliterated-GGUF

The-freezer · Muse-Glimmer-30B-Abliterated-GGUF

> [!IMPORTANT] > ## Improvement update — August 15, 2026 > > This release now includes compact **abliterated Q4_K_M companions**: a **1.63 GB DFlash drafter** and a **1.40 GB multimodal projector**, matching Meta's consumer-hardware footprint while preserving this model's modified weights. The complete text quant ladder has also been refreshed with Meta's post-release Jinja correction, which normalizes `Reasoning effort` to `Reasoning strength` and prevents duplicate reasoning directives. Text generation, image input, and DFlash speculative decoding were validated together on current llama.cpp. > ## ✅ All quants live > > The full text quant ladder (`Q2_K` → `Q8_0`), compact Q4_K_M and full-precision vision projectors, and compact Q4_K_M and full-precision DFlash drafters are uploaded — see the **Files** tab. > ## ⚠️ EXPERIMENTAL > > Same-day arch, quantized. Expect sharp edges — decode, coherence, tool-parse, serve edge cases under load. Please open a Community discussion with loader/version, quant, prompt, sampling, and failure mode. Real repros get fixed faster.

译文排队中,当前展示来源原文。

视觉问答multimodallicense:apache-2.0
上下文窗口来源未披露
输入 → 输出文本、图像 → 文本
模型权重待核验
档案更新时间2026/11/2 19:16:20
OVERVIEW

模型介绍

> [!IMPORTANT] > ## Improvement update — August 15, 2026 > > This release now includes compact **abliterated Q4_K_M companions**: a **1.63 GB DFlash drafter** and a **1.40 GB multimodal projector**, matching Meta's consumer-hardware footprint while preserving this model's modified weights. The complete text quant ladder has also been refreshed with Meta's post-release Jinja correction, which normalizes `Reasoning effort` to `Reasoning strength` and prevents duplicate reasoning directives. Text generation, image input, and DFlash speculative decoding were validated together on current llama.cpp. > ## ✅ All quants live > > The full text quant ladder (`Q2_K` → `Q8_0`), compact Q4_K_M and full-precision vision projectors, and compact Q4_K_M and full-precision DFlash drafters are uploaded — see the **Files** tab. > ## ⚠️ EXPERIMENTAL > > Same-day arch, quantized. Expect sharp edges — decode, coherence, tool-parse, serve edge cases under load. Please open a Community discussion with loader/version, quant, prompt, sampling, and failure mode. Real repros get fixed faster.

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: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。