MODEL PROFILE · 模型档案

Gemma4-26B-A4B-Uncensored-HauhauCS-1M-GGUF

satgeze · Gemma4-26B-A4B-Uncensored-HauhauCS-1M-GGUF

HauhauCS/Gemma4-26B-A4B-QAT-Uncensored-HauhauCS-Balanced-MTP (26B MoE, 4B active, Google QAT checkpoint) with a **1,048,576-token context baked in** (4x the native 262,144), shipping with its MTP speculative-decoding draft head and vision tower. All numbers below were measured on these exact files. 1M context ~91% mean recall across two seed sets (honesty note below) MTP speculative decoding 249.1 to 369.2 tok/s ( +48% ), acceptance 0.679 (measured on this trunk, RTX 5090) Vision Verified July 6, 2026: reads image text and identifies objects Uncensored HauhauCS Balanced abliteration; trunk weights bit-identical to the source release Full transparency: across two complete seed sets this trunk averages ~91 percent needle recall, dropping roughly one needle per rung at random depths, including inside the native 262K range. The official censored trunk scored 10/10 at 393K under the same harness, so this is a small flat abliteration tax, not a context-length failure. If a rare retrieval miss is unacceptable for your workload, use the 12B, which is certified clean. Every run, including the imperfect ones, is in `results.jsonl`. Rungs above 524K exceed 32 GB VRAM and have not been run yet; they are queued for larger hardware and will publish here as measured, pass or fail.

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文本生成license:gemma
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模型权重待核验
档案更新时间2026/10/31 10:49:07
OVERVIEW

模型介绍

HauhauCS/Gemma4-26B-A4B-QAT-Uncensored-HauhauCS-Balanced-MTP (26B MoE, 4B active, Google QAT checkpoint) with a **1,048,576-token context baked in** (4x the native 262,144), shipping with its MTP speculative-decoding draft head and vision tower. All numbers below were measured on these exact files. 1M context ~91% mean recall across two seed sets (honesty note below) MTP speculative decoding 249.1 to 369.2 tok/s ( +48% ), acceptance 0.679 (measured on this trunk, RTX 5090) Vision Verified July 6, 2026: reads image text and identifies objects Uncensored HauhauCS Balanced abliteration; trunk weights bit-identical to the source release Full transparency: across two complete seed sets this trunk averages ~91 percent needle recall, dropping roughly one needle per rung at random depths, including inside the native 262K range. The official censored trunk scored 10/10 at 393K under the same harness, so this is a small flat abliteration tax, not a context-length failure. If a rare retrieval miss is unacceptable for your workload, use the 12B, which is certified clean. Every run, including the imperfect ones, is in `results.jsonl`. Rungs above 524K exceed 32 GB VRAM and have not been run yet; they are queued for larger hardware and will publish here as measured, pass or fail.

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:gemma。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。