MODEL PROFILE

Qwen3.8-Flash-Next-Uncensored-GGUF

AtomicChat · Qwen3.8-Flash-Next-Uncensored-GGUF

One refusal direction projected out of Qwen's original weights, shipped two ways: three baked GGUF builds, and a 318 MB LoRA adapter for the Flash-Next quants you already have. The measurements behind this card are public. Built by the team behind Atomic Chat , the app that runs open-weight models locally and offline. On held-out prompts, refusals fall from 92.6% to 1.2% in English, and to none of 30 with thinking on. MMLU moves by 0.5 points, inside the noise. The 93.9 GB build matches the original's token choice 91.1% of the time: closer to the original than our published 94.5 GB quant of the unmodified model (89.6%). Already running our Flash-Next quants ? Add the adapter with --lora-scaled instead of downloading a new model. *28 Oct 2026: re-split from 2 GB shards into three files per build: the weights in two, cut at 45 GB, and the n-gram table in a third of its own. The weights are unchanged (tensor-for-tensor identical).*

Text generationlicense:other
Context windowNot disclosed
Input → OutputText → Text
Model weightsNot verified
Profile updated2026/10/30 03:28:07
OVERVIEW

Model overview

One refusal direction projected out of Qwen's original weights, shipped two ways: three baked GGUF builds, and a 318 MB LoRA adapter for the Flash-Next quants you already have. The measurements behind this card are public. Built by the team behind Atomic Chat , the app that runs open-weight models locally and offline. On held-out prompts, refusals fall from 92.6% to 1.2% in English, and to none of 30 with thinking on. MMLU moves by 0.5 points, inside the noise. The 93.9 GB build matches the original's token choice 91.1% of the time: closer to the original than our published 94.5 GB quant of the unmodified model (89.6%). Already running our Flash-Next quants ? Add the adapter with --lora-scaled instead of downloading a new model. *28 Oct 2026: re-split from 2 GB shards into three files per build: the weights in two, cut at 45 GB, and the n-gram table in a third of its own. The weights are unchanged (tensor-for-tensor identical).*

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

Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.

Source / channelInputOutputBilling unitCollected at
No verified prices yet. Check the source channel.

Estimate monthly base token costs

—

Estimates exclude cache discounts, tiered pricing, multimodal surcharges, web search, taxes and additional reasoning output.

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。

View full indexed rankings →

Frequently asked questions

Do open weights permit free commercial use?

Not necessarily. Check the version license, commercial conditions and usage policies. Self-hosting incurs compute and maintenance costs.

Are these real-time billing prices?

These are timestamped source snapshots, not live billing prices. Check the channel for current versions, regions, discounts and billing methods.

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。