Ace-Step-1.5-ScragVAE-ComfyUI
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A GGUF Q4_K_M quantization of MegaPanchamZ/Qwen3.8-9B-abliterated-25, the abliterated (decensored) version of empero-ai/Qwen3.8-9B. Abliteration was performed with **Heretic v1.4.0** (GPLv3+, by Philipp Emanuel Weidmann), which ablates refusal directions from `attn.o_proj` and `mlp.down_proj` via LoRA adapters, then merges them back into the base weights. 25/100 refusals with a very low KL divergence (0.014, far below the 0.5 damage threshold) — strong refusal suppression with minimal impact on model capabilities.
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模型权重来自 Heretic ARA。DFlash2 companion 来自 z-lab/Qwen3.8-27B-DFlash2,固定 revision 为 `50307d4c4cde6860d4eee73e2547cd786fe8e8a4`。 本 artifact 使用官方 `Qwen/Qwen3.8-27B` frontend,固定 revision 为 `1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0`,包含 tokenizer、tokenizer 配置、chat template、generation 配置、image preprocessor 和 video preprocessor 六个资源。部分 Unicode 文本在官方 Qwen frontend 与 Heretic 源仓库资源中的 tokenization 不同。 Heretic ARA 原始权重没有沿用普通 Qwen3.8 checkpoint 的运行 identity。本项目为它建立了独立的 NInfer identity,并将官方 Qwen frontend 作为明确的 frontend 输入;converter wrapper 对 Heretic 权重、frontend 资源和 DFlash2 companion 分开校验,runtime registry 和 binder 负责识别并加载该独立 identity。这样可以在保留 Heretic 权重的同时,使用 NInfer 已有的 Qwen3.8 执行路径、MTP、DFlash2 和 Vision 接口。
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**This is not a new model.** It is a repackaging of MahtaFetrat/Homo-GE2PE-Persian by Elnaz Rahmati et al. — the weights are byte-identical. All credit for the model belongs to the original authors. The `-HF` suffix follows the `Llama-2-7b-hf` convention: same weights, packaged in transformers-native format rather than the original release format. It is not a new version of the model. What changed is only the packaging: the original ships zipped weights plus a vendored copy of Parsivar and a custom `GE2PE.py` wrapper. This repo is a plain `from_pretrained()` model with no extra dependencies, so it can be dropped into an inference pipeline directly.
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Adjust provider, category or input/output filters. New models are added continuously.
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Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.
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