MODEL PROFILE · 模型档案

Qwen3.8-Flash-Next-Uncensored-IQ4XS-NGQ4-GGUF

cygnal · Qwen3.8-Flash-Next-Uncensored-IQ4XS-NGQ4-GGUF

First working GGUF build of Qwen3.8-Flash-Next (`qwen4exp` architecture) with vision, running on stock llama.cpp — no custom tensor formats or forked runtime required. Built from orcarouter/Qwen3.8-Flash-Next-Uncensored, the abliterated (uncensored) release of Qwen's newest hybrid architecture. ⚠️ **Research artifact.** Refusal behaviour has been removed from the source model. This does not add capability — it removes guardrails. Use it deliberately, in a context where that is appropriate, and own the output. **5.61 BPW average, 98.4 GB total.** The n-gram table alone is ~29 GB of that at Q4_0 — it's lookup-only (hash → embedding), so it tolerates low-bit quantization well, but it needed a separate streaming dequant/requant pass (chunked, ~2M rows at a time) because standard `llama-quantize` can't process its unusual 160-wide row shape and would need ~200+ GB to dequantize it in one pass.

译文排队中,当前展示来源原文。

license:other
上下文窗口来源未披露
输入 → 输出待核验 → 待核验
模型权重待核验
档案更新时间2026/10/30 02:07:40
OVERVIEW

模型介绍

First working GGUF build of Qwen3.8-Flash-Next (`qwen4exp` architecture) with vision, running on stock llama.cpp — no custom tensor formats or forked runtime required. Built from orcarouter/Qwen3.8-Flash-Next-Uncensored, the abliterated (uncensored) release of Qwen's newest hybrid architecture. ⚠️ **Research artifact.** Refusal behaviour has been removed from the source model. This does not add capability — it removes guardrails. Use it deliberately, in a context where that is appropriate, and own the output. **5.61 BPW average, 98.4 GB total.** The n-gram table alone is ~29 GB of that at Q4_0 — it's lookup-only (hash → embedding), so it tolerates low-bit quantization well, but it needed a separate streaming dequant/requant pass (chunked, ~2M rows at a time) because standard `llama-quantize` can't process its unusual 160-wide row shape and would need ~200+ GB to dequantize it in one pass.

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。

按月估算基础 Token 费用

—

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。

查看完整已收录榜单 →

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:other。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。