Muse-Glimmer-30B-Heretic-Uncensored-GGUF

모델 소개

This repository disseminates a comprehensive suite of 27 GGUF (GPT-Generated Unified Format) quantizations of **Muse-Glimmer-30B-Heretic-Uncensored**, an abliterated derivative of Meta's Muse-Glimmer-30B produced with Heretic v1.4.0 by darkc0de. The release spans a memory footprint of 6.53 GB (IQ1_S) to 55.73 GB (F16/BF16), thereby accommodating deployment across heterogeneous hardware, from CPU-only systems to 32 GB VRAM accelerators. All sub-4-bit quantizations were calibrated with an importance matrix (imatrix) to mitigate quality degradation. The fidelity of the Q4_K_S variant relative to the F16 reference is quantified via token-level perplexity on a fixed evaluation set. The model exhibits **zero refusals on adult/NSFW content**; this property was empirically verified. The quantization selection is derived from the model's architectural properties and the memory constraints of representative target hardware. The model employs **grouped-query attention with 2 KV heads**, which renders the KV cache exceptionally compact: - 52 layers × 2 KV heads × 128 head_dim = **52 KiB/token at FP16** - Q8_0 KV cache: **26 KiB/token** → 64K context ≈ 1.7 GB - Q4_0 KV cache: **13 KiB/token** → 64K context ≈ 0.85 GB

주요 특징

特点摘要待依据来源补齐，不凭模型名称推断能力。

한계 및 유의 사항

구체적 한계를 판단할 출처가 부족합니다. 실제 성능, 지연, 데이터 정책, 라이선스와 버전 수명을 확인하세요.

출처별 요금 및 비용 분석

요금은 채널, 지역, 설정에 따라 다릅니다. 각 채널 요금은 개별 표시되며 제공사의 통일 공식 요금이 아닙니다.

공개 평가 및 실행 설정

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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출처 및 추적 기록

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。