MODEL PROFILE

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.

license:other
Context windowNot disclosed
Input → Output待核验 → 待核验
Model weightsNot verified
Profile updated2026/10/30 02:07:40
OVERVIEW

Model overview

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.

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

PRICING & COST

Source prices & cost analysis

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Estimate monthly base token costs

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

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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Frequently asked questions

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Sources & traceability

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。