Kimi-K3-GGUF
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Laguna-S-2.1-GGUF
AtomicChat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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View details ↗Laguna-XS-2.1-GGUF
AtomicChat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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View details ↗Laguna-XS-2.1-MLX-8bit
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View details ↗LFM2.5-24B-A2B-GGUF
AtomicChat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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View details ↗LFM2.5-24B-A2B-Uncensored-GGUF
AtomicChatLiquid's refusal behaviour projected out of the original weights, in the second two thirds of the network only. Shipped two ways: six baked GGUF builds, and a 24 MB LoRA adapter for the LFM2.5-24B-A2B 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 96.3% to 1.2% in English, and from 30 of 30 to none with thinking. MMLU stays where it was: 64.40% before, 65.55% after (inside the noise). No empty or looping replies. Only blocks 14-39 are edited. The same edit across all 40 blocks also removed the refusals, but cost 4 MMLU points; the first 14 blocks carry the damage, not the refusal. Already running our LFM2.5-24B-A2B quants ? Add the adapter with --lora-scaled instead of downloading a new model. **`AD-Q4_K_M` is the one to take** on a 24 GB machine. With 32 GB, `AD-Q6_K` is nearly as close to the original as `AD-Q8_0` at 5 GB less; `AD-IQ3_M` is the smallest build here. Refusals were measured on `AD-Q8_0` and `AD-Q4_K_M`; neither gave an empty or looping reply. A smaller `AD-IQ3_XXS` (11.1 GB) was made too and is held back: it matched the original's token choice only 74.5% of the time and refused 3.7% of English prompts.
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View details ↗LFM3-1.7B-GGUF
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View details ↗LFM3-1.7B-Uncensored-GGUF
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View details ↗LFM3-Audio-2B-GGUF
AtomicChat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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View details ↗LFM3-Omni-2B-GGUF
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View details ↗LFM3-VL-2B-GGUF
AtomicChat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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View details ↗Ling-3.0-flash-GGUF
AtomicChatLing 3.0 flash is a 124B MoE with 5.1B active parameters; its GGUF chat template includes thinking mode and tool calling. Our AD layouts have 31–41% lower mean KL than stock llama.cpp presets at comparable sizes. The raw measurements and controls are public. From the team behind Atomic Chat , the local AI app for chat and agents. These Ling files require our TurboQuant build; use the CLI instructions below . The tests documented below cover the CLI workflow, not app compatibility. Quantizations of `inclusionAI/Ling-3.0-flash`: 124B total, 5.1B active, hybrid linear attention (35 KDA blocks interleaved 5:1 with 7 gated MLA blocks) over a 512-expert MoE. Bits are placed by hand rather than by the default rules, and the controls that prove it is worth something are published next to the files. At the same size, our layout sits 31 to 41 % closer to BF16 than what `llama-quantize` produces on its own
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View details ↗Ling-3.0-flash-NVFP4-GGUF
AtomicChat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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View details ↗Ling-3.0-flash-Sante-GGUF
AtomicChatBuilt from inclusionAI's original weights with our own importance matrix. The calibration corpora behind our builds are public. You can now run Ling in Atomic Chat . See our quantization analysis below for measurements and instructions. Every number below is measured, not estimated. How we measured it is at the bottom, and the raw logs are in the metrics repo so you can check any of it yourself.
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View details ↗Ling-3.0-flash-Sante-Uncensored-GGUF
AtomicChat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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View details ↗Ling-3.0-flash-VL-GGUF
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View details ↗Ling-3.1-flash-GGUF
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View details ↗Ling-3.1-flash-Uncensored-GGUF
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View details ↗MiniCPM-V-4.7-1B-GGUF
AtomicChatBuilt from openbmb's original weights with our own importance matrix. The calibration corpora behind our builds are public. You can now run MiniCPM in Atomic Chat . See our quantization analysis below for measurements and instructions. Every number below is measured, not estimated. How we measured it is at the bottom, and the raw logs are in the metrics repo so you can check any of it yourself.
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View details ↗MiniCPM-V-4.7-35B-A3B-GGUF
AtomicChatBuilt from openbmb's original weights with our own importance matrix. The calibration corpora behind our builds are public. You can now run MiniCPM in Atomic Chat . See our quantization analysis below for measurements and instructions. Every number below is measured, not estimated. How we measured it is at the bottom, and the raw logs are in the metrics repo so you can check any of it yourself.
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View details ↗MiniCPM-V-4.7-35B-A3B-Uncensored-GGUF
AtomicChatOne refusal direction projected out of OpenBMB's original weights, shipped two ways: three baked GGUF builds, and a 106 MB LoRA adapter for the MiniCPM-V 4.7 quants you already have. The measurements behind this card are public. On held-out prompts, refusals fall from 96.3% to 0% in English, and from 13 of 30 to none with thinking on. MMLU moves by 0.25 points, inside the noise. You can now run the Uncensored MiniCPM-V 4.7 in Atomic Chat , locally on your own machine. The 20.1 GB build matches the original's token choice 94.5% of the time; our quant of the unmodified model at the same size does 94.9%. Already running our MiniCPM-V 4.7 quants ? Add the adapter with --lora-scaled instead of downloading a new model. *28 Oct 2026: each build is now a single file instead of 11 to 16 shards of 2 GB. The weights are unchanged (tensor-for-tensor identical).*
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View details ↗MiniMax-M3.1-Flash-GGUF
AtomicChat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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View details ↗Muse-Glimmer-30B-GGUF
AtomicChatGGUF builds of Meta's Muse Glimmer 30B, made from the original BF16 weights with a calibrated importance matrix and a bit layout chosen by measurement. > [!NOTE] > Support was merged into llama.cpp master in > #26841 (commit `62bf73d`). > You need a build from master — the last tagged release predates it. > [!TIP] > `AD-Q6_K` at 28.3 GB is dominated by plain `Q8_0` at 29.6 GB, which is 1.3 GB larger > and 31 % more accurate. Take `Q8_0` instead unless you need exactly that size.
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View details ↗Muse-Spark-Open-816B-A42B-GGUF
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