MODEL PROFILE

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.

Text generationlicense:gemma
Context windowNot disclosed
Input → OutputText → Text
Model weightsNot verified
Profile updated2026/10/31 10:49:07
OVERVIEW

Model overview

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.

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

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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。

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Frequently asked questions

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Sources & traceability

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。