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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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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

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**If you use this model, follow @procrastiness on X/Twitter.** That’s where drop notes, quant updates, and the next bake land first. One follow is the whole ask. Same idea as HauhauCS Aggressive on Qwen3.6: **keep the original model’s skills, strip the refusal / preamble habit.** No extra fine-tune. No dataset swap. This is a weight edit plus a baked chat template, not a jailbreak prompt you have to remember. > Safety alignment has been removed. The model will comply with requests the base checkpoint would refuse. Use it only in ways that are legal where you are. The publisher does not condone criminal use.

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GSQ-RCO upstream · layout preserved 144 tensors transplanted IQ3_S / IQ3_XXS / IQ2_XS / Q2_0 per-tensor blake2b verified 262K context APACHE-2.0 Qwen3.8-Flash-Next · GSQ-RCO-abliterated The refusal direction removed from an already-quantized GSQ-RCO model — 144 tensors across all 48 layers swapped for ready-made abliterated weights, with every value and scale GSQ learned left untouched and the upstream per-tensor type assignment restored exactly . The IQ3_S build is verified tensor by tensor: everything that changed was supposed to, nothing else moved. 38.0 GB · Q2_0 every tier costs only 0.25 – 0.44 GB over upstream 55.1 GB · IQ3_S 🧭 What this repository is An abliterated build of the official ISTA-DASLab GSQ-RCO quantizations . Not a single one of GSQ's learned quantized tensors was recomputed — 144 "write-to-residual-stream" projection tensors across all 48 layers were replaced with the corresponding tensors from a ready-made abliterated release. Base ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF — GSQ learned quantization + RCO budget-constrained type assignment Ablation source orcarouter/Qwen3.8-Flash-Next-Uncensored-GGUF — the 144 target tensors are taken straight from it Method Byte-level GGUF → GGUF tensor transplant (135 of 144 targets); the 9 remaining expert tensors, which upstream stores as Q2_0, are re-encoded from the abliterated values using GSQ's own learned block scales — no GSQ value is recomputed, only the codes Not done Any dequantize → quantize cycle; any re-quantization from BF16 / safetensors In one line: what you get is still the GSQ-RCO quantization you already know , with those 144 tensors abliterated. It is the only abliteration route that preserves GSQ's work.

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COST EXPLORER

好模型，也要算好成本。

费用分析

调整月度 Token 用量，直观比较不同模型的预估 API 开支。

1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价

使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。

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选型与常见问题
API 价格应该怎么比较？+

先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。

开放权重，就代表可以免费商用吗？+

不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。

排行榜第一，就是最适合我的模型吗？+

榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。

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