MODEL PROFILE · 模型档案

Qwen3.8-27B-abliterated-GGUF

prithivMLmods · Qwen3.8-27B-abliterated-GGUF

> **Qwen3.8-27B-abliterated-GGUF** is a GGUF-quantized conversion of huihui-ai/Huihui-Qwen3.8-27B-abliterated, an uncensored variant of Qwen/Qwen3.8-27B produced through abliteration — a crude, proof-of-concept activation-editing technique that removes refusal behavior directly from model weights without relying on TransformerLens. The underlying Qwen3.8-27B is a 27-billion-parameter dense causal language model with a native vision encoder, built on the Qwen3.5 architectural foundation, featuring a 64-layer hybrid design interleaving Gated DeltaNet linear-attention blocks with periodic Gated Attention layers, Multi-Token Prediction (MTP) training, a native 262,144-token context window extensible to 1M via YaRN, native image/video understanding, and flexible thinking control through a `reasoning_effort` parameter, delivering strong results on benchmarks like SWE-bench Pro (61.7), OSWorld-Verified (84.3), and GPQA Diamond (89.2). This GGUF release packages the abliterated weights across the standard quantization sweep for efficient local deployment via llama.cpp and compatible runtimes; note that, as with GGUF conversions generally, **the Multi-Token Prediction (MTP) heads are not preserved** in this format — the model runs as a standard single-token-per-step autoregressive decoder, so any latency or quality benefits tied to MTP-based speculative decoding in the original checkpoint do not carry over to these quantized builds. File Name | Quant Type | File Size | File Link | |-----------|------------|-----------|-----------| | Qwen3.8-27B-abliterated.BF16.gguf | BF16 | 53.8 GB | Download | | Qwen3.8-27B-abliterated.F16.gguf | F16 | 53.8 GB | Download | | Qwen3.8-27B-abliterated.Q2_K.gguf | Q2_K | 10.7 GB | Download | | Qwen3.8-27B-abliterated.Q3_K_L.gguf | Q3_K_L | 14.3 GB | Download | | Qwen3.8-27B-abliterated.Q3_K_M.gguf | Q3_K_M | 13.3 GB | Download | | Qwen3.8-27B-abliterated.Q3_K_S.gguf | Q3_K_S | 12.1 GB | Download | | Qwen3.8-27B-abliterated.Q4_0.gguf | Q4_0 | 15.5 GB | Download | | Qwen3.8-27B-abliterated.Q4_K_M.gguf | Q4_K_M | 16.5 GB | Download | | Qwen3.8-27B-abliterated.Q4_K_S.gguf | Q4_K_S | 15.6 GB | Download | | Qwen3.8-27B-abliterated.Q5_0.gguf | Q5

译文排队中,当前展示来源原文。

视觉问答multimodallicense:apache-2.0
上下文窗口来源未披露
输入 → 输出文本、图像 → 文本
模型权重待核验
档案更新时间2026/11/2 19:16:07
OVERVIEW

模型介绍

> **Qwen3.8-27B-abliterated-GGUF** is a GGUF-quantized conversion of huihui-ai/Huihui-Qwen3.8-27B-abliterated, an uncensored variant of Qwen/Qwen3.8-27B produced through abliteration — a crude, proof-of-concept activation-editing technique that removes refusal behavior directly from model weights without relying on TransformerLens. The underlying Qwen3.8-27B is a 27-billion-parameter dense causal language model with a native vision encoder, built on the Qwen3.5 architectural foundation, featuring a 64-layer hybrid design interleaving Gated DeltaNet linear-attention blocks with periodic Gated Attention layers, Multi-Token Prediction (MTP) training, a native 262,144-token context window extensible to 1M via YaRN, native image/video understanding, and flexible thinking control through a `reasoning_effort` parameter, delivering strong results on benchmarks like SWE-bench Pro (61.7), OSWorld-Verified (84.3), and GPQA Diamond (89.2). This GGUF release packages the abliterated weights across the standard quantization sweep for efficient local deployment via llama.cpp and compatible runtimes; note that, as with GGUF conversions generally, **the Multi-Token Prediction (MTP) heads are not preserved** in this format — the model runs as a standard single-token-per-step autoregressive decoder, so any latency or quality benefits tied to MTP-based speculative decoding in the original checkpoint do not carry over to these quantized builds. File Name | Quant Type | File Size | File Link | |-----------|------------|-----------|-----------| | Qwen3.8-27B-abliterated.BF16.gguf | BF16 | 53.8 GB | Download | | Qwen3.8-27B-abliterated.F16.gguf | F16 | 53.8 GB | Download | | Qwen3.8-27B-abliterated.Q2_K.gguf | Q2_K | 10.7 GB | Download | | Qwen3.8-27B-abliterated.Q3_K_L.gguf | Q3_K_L | 14.3 GB | Download | | Qwen3.8-27B-abliterated.Q3_K_M.gguf | Q3_K_M | 13.3 GB | Download | | Qwen3.8-27B-abliterated.Q3_K_S.gguf | Q3_K_S | 12.1 GB | Download | | Qwen3.8-27B-abliterated.Q4_0.gguf | Q4_0 | 15.5 GB | Download | | Qwen3.8-27B-abliterated.Q4_K_M.gguf | Q4_K_M | 16.5 GB | Download | | Qwen3.8-27B-abliterated.Q4_K_S.gguf | Q4_K_S | 15.6 GB | Download | | Qwen3.8-27B-abliterated.Q5_0.gguf | Q5

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。

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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。

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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: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。