**Qwen3.6 27B**, 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 **Qwen3.6 27B chat template** is applied. Without it the model can emit malformed turns.
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**Qwen3.6 35B 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 **Qwen3.6 35B A3B chat template** is applied. Without it the model can emit malformed turns.
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Built from Qwen's original weights with our own importance matrix. The calibration corpora behind our builds are public. Learn how to run Qwen3.8 27B locally - read our guide . You can now run Qwen3.8 in Atomic Chat with toggles for thinking. See our quantization analysis below for measurements and instructions. Every quantization of this model we could find, ours and everyone else's, measured against the original weights on the same held-out text. The dashed line is the best file available at each size from anyone but us.
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Built from Qwen's original weights with our own importance matrix. The calibration corpora behind our builds are public. Built by the team behind Atomic Chat , the app that runs open-weight models locally and offline. Qwen3.8-Flash-Next is the first open-weight release of the architecture behind Qwen4. These GGUFs are self-quantized from Qwen's original weights with our own importance matrix, published alongside the quants. They need a llama.cpp build with Qwen3.8-Flash-Next support; Atomic Chat runs it as support ships. Need it without refusals? Qwen3.8-Flash-Next-Uncensored-GGUF has baked builds and a LoRA adapter for the quants here. Qwen3.8-Flash-Next has 177B parameters. Our 85 GB quant runs on an M5 Max with 64 GB of memory, with vision, at 36 tok/s. That is not a typo: the file is larger than the machine's entire RAM.
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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).*
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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.
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