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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 generation
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**Qwen3 Coder 30B A3B**, self-quantized to GGUF by Atomic Chat. Built straight from Qwen's original weights with a per-tensor importance matrix, so this is not a repack of somebody else's files. Runs fully offline. > [!NOTE] > These GGUFs are **self-quantized from the original weights**, not a repack. The importance matrix keeps low-bit quants closer to the full-precision model. > [!IMPORTANT] > Always pass `--jinja` so the **Qwen3 Coder 30B A3B chat template** is applied. Without it the model can emit malformed turns.

Text generation
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Qota-OCR

atonlee

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Image captioning
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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Category pending
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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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bayon

attentionlab

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Text generation
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bayon-it

attentionlab

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Text generation
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