GGUF quants of Nanbeige's Nanbeige4.3-33B, built from Nanbeige's original BF16 weights on release day. Nanbeige4.3-33B is Nanbeige's 33B dense agent and reasoning model (Llama architecture, 512K context, thinking on by default), Apache-2.0. You can now run Nanbeige4.3-33B in Atomic Chat , with thinking on by default. Atomic Dynamic (AD) files with our own importance matrix, from 13.7 GB to 30.1 GB on disk, plus plain Q8_0 and Q5_K_M ; every one measured against the BF16 original on one stand. > [!NOTE] > **Built on release day, 25 October.** `Q8_0` and `Q5_K_M` are llama.cpp's own > presets, made without an importance matrix; its `Q6_K` and `Q4_K_M` were up > for the first hours and are removed now that `AD-` files beat them at the same > size (their numbers stay below as the baseline). The `AD-` files > use our own importance matrix, collected on the BF16 weights (`--parse-special`) > over 8,000 chunks of 512 tokens of our calibration corpus. The corpus build was > rendered for Qwen3.8-27B, whose chat format (ChatML) Nanbeige4.3 shares; it is > `imatrix/imatrix.gguf` in the metrics repo.
번역 대기 중 · 원문 표시
입력 텍스트→ 출력 텍스트
Atomic Dynamic (AD) quants, built from NVIDIA's original BF16 weights with our own importance matrix. At 21.9 GB, AD-IQ1_M_S stays closer to the original than NVIDIA's own 22.0 GB 2-bit file, measured side by side on one stand. The calibration corpora behind our builds are public. Every file here was measured against the BF16 original on the same stand as the other publishers' files. You can now run Nemotron 3.5 Bolt in Atomic Chat : 74B total parameters, 9B active per token. See our quantization analysis below for measurements and instructions. Lower is closer to the original. Each purple point is a file in this repo; the grey ones are other GGUF files of this model, downloaded and measured with exactly the same reference, text and llama.cpp build. NVIDIA's 2-bit file (the one Unsloth and LM Studio ship too) is a quantization-aware-trained checkpoint, so its distance to the BF16 weights also includes what that training changed; see below.
번역 대기 중 · 원문 표시
입력 텍스트 · 이미지→ 출력 텍스트
NVIDIA's refusal behaviour projected out of the released weights, baked into Atomic Dynamic GGUF builds and measured against the original. On held-out JailbreakBench prompts refusals fall from 97.5% to 0%, and with thinking on from 30 of 30 to none (2 of 30 on the 22 and 29 GB files). MMLU stays where it was: 81.35% before, 81.75% after (2000 questions, McNemar p = 0.29). Tool calls and long context are unaffected. You can now run the Uncensored Nemotron 3.5 Bolt in Atomic Chat , locally on your own machine. Made from the full-precision release, not from NVIDIA's 24 GB file, and measured on one stand against the other public Bolt abliterations: at 22 GB ours is the closest to the original and the only one that neither refuses with thinking off nor breaks into repeated text ( table ). The refusal is written into the residual stream by every kind of block: the edit covers the Mamba-2 outputs, the attention outputs, the shared experts and the latent projection through which all routed experts write. Same Atomic Dynamic layouts and importance matrix as our Nemotron 3.5 Bolt quants ; each file is the size of the original build with the same name. *28 Oct 2026: re-split from 2 GB shards: `AD-IQ2_XS` and `AD-IQ1_M_S` are now a single file each, `AD-Q4_K_M` two files cut at 45 GB. The weights are unchanged (tensor-for-tensor identical).*
번역 대기 중 · 원문 표시
입력 텍스트 · 이미지→ 출력 텍스트
Atomic Dynamic (AD) quants of NVIDIA's Nemotron 3.5 Bolt E4B, built from NVIDIA's original BF16 weights. From 4.0 GB to 8.0 GB on disk. Bolt E4B is NVIDIA's small reasoning and chat model, post-trained from Google's Gemma-4-E4B-it . You can now run Nemotron 3.5 Bolt E4B, NVIDIA's on-device model, in Atomic Chat . Every file here was measured against the BF16 original on one stand, next to the other publishers' files. > [!NOTE] > **Built on release day, 23 October.** Every `AD-` file below `AD-Q6_K` was > rebuilt the same evening with a better layout (below). `AD-Q4_K_M` uses our own > importance matrix, collected on the BF16 weights over a corpus rendered through > this model's chat template (`imatrix/imatrix.gguf` in the metrics repo). The > other files use Unsloth's published one (`imatrix_unsloth.gguf` from > unsloth/NVIDIA-Nemotron-3.5-Bolt-E4B-GGUF): > we built `AD-Q5_K_M` to `AD-IQ3_S` with both, and on held-out code the two > came out within noise of each other, slightly ahead with Unsloth's at 3 bits. > `AD-IQ3_XXS` (3.82 GB) was taken down the same evening: it picked the > original's next word only 74.6% of the time, below the 75% we publish at.
번역 대기 중 · 원문 표시
입력 텍스트→ 출력 텍스트
NVIDIA's refusal behaviour projected out of the released weights, baked into Atomic Dynamic GGUF builds and shipped as a 3.5 MB LoRA, measured against the original. On held-out JailbreakBench prompts refusals fall from 93.8% to 1.2%, on the safe XSTest prompts from 9.2% to 0.4%, and with thinking on from 29 of 30 to none. MMLU: 68.50% before, 67.85% after (2000 questions, McNemar p = 0.21). Tool calls and long context are unaffected. You can now run the Uncensored Nemotron 3.5 Bolt E4B in Atomic Chat , locally on your own machine. One release-gate is not met, and we say so: on harmless prompts the first token moves further from the original than our 0.10 limit (KL 0.141). On this model even a random direction edited the same way moves neutral text noticeably ( limitations ). Gemma 4 normalises every write into the residual stream with its own weights before adding it, so the edit is computed through those norms: each matrix stops writing the refusal direction as it lands in the stream, not before the norm. Built on the same weights as our Nemotron 3.5 Bolt E4B quants ; the LoRA loads on top of any of those files. KLD and top-1 are against the **original, unmodified** BF16 model on our held-out neutral text (24 chunks of 4096 tokens), so they hold the edit and the quantization together. The edit alone, on the BF16 weights, is 0.159; the plain `AD-Q6_K` of the original model is 0.026 and `AD-Q4_K_M` 0.091 on the same text, so at `AD-Q6_K` almost all of the distance is the edit itself.
번역 대기 중 · 원문 표시
입력 텍스트→ 출력 텍스트
GGUF quantizations of nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16, built with llama.cpp `ba360ef`. Every claim below is measured; raw logs are in the metrics repo. `AD-` marks an **Atomic Dynamic** layout: the two expert tensors are assigned explicitly instead of left to a preset, and the name carries the assignment — `AD- - `, collapsed to one name when both match. Everything outside the experts is `Q8_0`; it is 7% of the model, so spending bits there is cheaper than reasoning about it. Measured on `eval_neutral` held-out, KL divergence against the `Q8_0` reference. `tok/s` is generation on a single RTX 5090, full offload.
번역 대기 중 · 원문 표시
입력 텍스트→ 출력 텍스트
Atomic Dynamic (AD) quants, built from NVIDIA's original BF16 weights with our own importance matrix. Every file here matches or beats the other GGUF builds of the same size or larger, measured side by side on one stand. The calibration corpora behind our builds are public. Every file here was measured against the BF16 original on the same stand as the other publishers' files, and each one beats or ties the file of theirs that is the same size or larger. You can now run Nemotron 3.5 Super in Atomic Chat : 120B total parameters, 12B active per token. See our quantization analysis below for measurements and instructions. Lower is closer to the original. Each purple point is a file in this repo; the grey ones are the other publishers' files, downloaded and measured with exactly the same reference, text and llama.cpp build. At 75 GB, `AD-IQ4_NL` drifts about half as much as both NVFP4 builds, which are the same size or up to 3 GB larger. At 70 GB, `AD-IQ3_M` drifts about half as much as LM Studio's `Q4_K_M` of the same size. At 65 GB, `AD-IQ3_XXS` drifts 20% less than Unsloth's `UD-Q3_K_XL`, and from 46 to 58 GB the `AD-Q2_0_*` files drift 6 to 18% less than the Unsloth file of each size, and at 87 GB `AD-Q4_K_S` drifts 13% less than `UD-Q4_K_XL`.
번역 대기 중 · 원문 표시
입력 텍스트 · 이미지→ 출력 텍스트
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。
기본 정보 등록 · 상세 정보 보완 예정
입력 텍스트 · 이미지→ 출력 텍스트
Built from ornith-ai's original weights with our own importance matrix. The calibration corpora behind our builds are public. You can now run Ornith 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.
번역 대기 중 · 원문 표시
입력 텍스트 · 이미지→ 출력 텍스트
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。
입력 텍스트 · 이미지→ 출력 텍스트
Built from Ornith's original weights with our own importance matrix. The calibration corpora and raw eval logs behind this card are public. See our quantization analysis below for measurements and instructions. You can now run Ornith 1.5 in Atomic Chat with toggles for thinking. GGUF builds of ornith-ai/Ornith-1.5-35B-A3B, converted and measured by AtomicChat.
번역 대기 중 · 원문 표시
입력 텍스트 · 이미지→ 출력 텍스트
Built from Ornith's original weights with our own importance matrix. The calibration corpora and raw eval logs behind this card are public. Learn how to run Ornith 1.5 9B locally - read our guide . You can now run Ornith 1.5 in Atomic Chat with toggles for thinking. See our quantization analysis below for measurements and instructions. GGUF builds of ornith-ai/Ornith-1.5-9B, converted and measured by AtomicChat.
번역 대기 중 · 원문 표시
입력 텍스트 · 이미지→ 출력 텍스트
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。
기본 정보 등록 · 상세 정보 보완 예정
입력 텍스트→ 출력 텍스트
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。
기본 정보 등록 · 상세 정보 보완 예정
입력 텍스트→ 출력 텍스트
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。
기본 정보 등록 · 상세 정보 보완 예정
입력 텍스트→ 출력 텍스트
The 8-step Qwen-Image-2.1 as GGUF for stable-diffusion.cpp, built from the published BF16 weights with our own importance matrix. The renders and measurements behind this card are public. Generate images locally in Atomic Chat . It runs stable-diffusion.cpp, the engine these files were built and checked with. Turbo drifts from the original picture faster than you might expect at 4 bits and below. Our AD-Q4_K stays 21% closer to the BF16 image than the plain Q4_K , for 150 MB more. Turbo needs its own 8-step schedule at CFG 1. The exact settings are in Running it . Looking for uncensored? The open Qwen-Image pipeline has no content filter. Qwen-Image-2.1-Turbo-Uncensored-GGUF removes the refusal direction from its text encoder. On images that changed nothing we could measure, and the card shows why. Each file renders the same 48 prompts × 2 seeds from the same starting noise as the BF16 weights. We compare every image with the BF16 image of the same prompt and seed.
번역 대기 중 · 원문 표시
입력 텍스트→ 출력 이미지
Qwen-Image's text encoder, Qwen3-VL-8B-Instruct, with its refusal direction projected out: a drop-in --llm for stable-diffusion.cpp next to our Turbo denoiser GGUFs . Below, exactly which part is uncensored and what that does to the pictures. The measurements are public. As a chat model, the encoder's refusals fall from 88.9% to 1.2% on held-out English prompts. MMLU is unchanged: 77.35% before and after. On images it changes nothing we could measure. The stock Qwen-Image pipeline has no filter and already draws all 9 sensitive categories we tested, and with this encoder not one of 45 prompts changed outcome. See where exactly this is uncensored . Generate images locally in Atomic Chat . It runs stable-diffusion.cpp, the engine these files were built and checked with. Qwen-Image 2.1 makes a picture with three parts. Only one of them is changed here.
번역 대기 중 · 원문 표시
입력 텍스트→ 출력 이미지
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。
기본 정보 등록 · 상세 정보 보완 예정
입력 텍스트→ 출력 텍스트
**Qwen3 4B Dflash b16**, self-quantized to GGUF by Atomic Chat. Built straight from Z Lab's original weights with a per-tensor importance matrix, so this is not a repack of somebody else's files. Runs fully offline. - **0.5B parameters**: the weights this repo quantizes. - **Context length**: 40,960 tokens (40K), as published by Z Lab. - **5 layers**: Dense decoder. - **Full imatrix ladder**: every quant is calibrated with an importance matrix. > [!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.
번역 대기 중 · 원문 표시
입력 텍스트→ 출력 텍스트
**Qwen3.5 4B Dflash**, self-quantized to GGUF by Atomic Chat. Built straight from Z Lab's original weights with a per-tensor importance matrix, so this is not a repack of somebody else's files. Runs fully offline. - **0.6B parameters**: the weights this repo quantizes. - **Context length**: 262,144 tokens (256K), as published by Z Lab. - **6 layers**: Dense decoder, hybrid sliding-window (4096) and global attention. - **Full imatrix ladder**: every quant is calibrated with an importance matrix. > [!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.
번역 대기 중 · 원문 표시
입력 텍스트→ 출력 텍스트
**Qwen3.5 4B**, 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 **Qwen3.5 4B chat template** is applied. Without it the model can emit malformed turns.
번역 대기 중 · 원문 표시
입력 텍스트→ 출력 텍스트
**Qwen3.5 9B Dflash**, self-quantized to GGUF by Atomic Chat. Built straight from Z Lab's original weights with a per-tensor importance matrix, so this is not a repack of somebody else's files. Runs fully offline. - **1.3B parameters**: the weights this repo quantizes. - **Context length**: 262,144 tokens (256K), as published by Z Lab. - **6 layers**: Dense decoder, hybrid sliding-window (4096) and global attention. - **Full imatrix ladder**: every quant is calibrated with an importance matrix. > [!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.
번역 대기 중 · 원문 표시
입력 텍스트→ 출력 텍스트
**Qwen3.5 9B**, 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 **Qwen3.5 9B chat template** is applied. Without it the model can emit malformed turns.
번역 대기 중 · 원문 표시
입력 텍스트→ 출력 텍스트
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。
기본 정보 등록 · 상세 정보 보완 예정
입력 텍스트→ 출력 텍스트
