GLM-5.3-Flash-NestQuant-1.5-4bit

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Qwen3.8-27B-NVFP4

Inferact Inc.

Quantized version of https://huggingface.co/Qwen/Qwen3.8-27B

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Qwen3.8-27B-NVFP4

RadixArk

The RadixArk Qwen3.8-27B-NVFP4 model is the quantized version of Qwen/Qwen3.8-27B. The quantization was produced at RadixArk using NVIDIA Model Optimizer, following a mixed NVFP4 W4A4 recipe. Run on SGLang: launch command and per-platform recipes in the Qwen3.8-27B cookbook. This model is not owned or developed by RadixArk. It is a quantized derivative of Qwen's model; see the upstream Qwen3.8-27B model card for the source model's capabilities, training information, limitations, and license. Global Developers looking to deploy an off-the-shelf, pre-quantized model in AI agent systems, chatbots, RAG systems, and other AI-powered applications. Hugging Face 08/14/2026 via…

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Swift-Qwen3.8-27B-Uncensored-NVFP4

AJ Gazin

NVFP4 checkpoint of an abliterated Swift-Qwen3.8-27B (UkisAI's reasoning-efficient fine-tune of Qwen3.8-27B). For vLLM and SGLang. GGUFs for llama.cpp: The Swift 1.5 version is source). - Swift's own NVFP4 recipe, unmodified, from ukisai/Swift-Qwen3.8-27B-NVFP4, calibrated with NVIDIA ModelOpt. - MTP head and vision tower in BF16, bit-identical to the source. 21.9 GB, NVIDIA ModelOpt mixed-precision format. Needs a vLLM with ModelOpt mixed-precision support (tested on 0.29.0). No --quantization flag. Sampling, as for Swift and Qwen: temperature 1.0, topp 0.95, topk 20, minp 0. The model thinks before answering by default. Tested on an RTX 5090 (32 GB) with vLLM 0.29.0: NVFP4 layers on…

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Swift-1.5-Qwen3.8-27B-Uncensored-NVFP4

AJ Gazin

NVFP4 checkpoint of an abliterated Swift 1.5 Qwen3.8-27B (UkisAI's reasoning-efficient fine-tune of Qwen3.8-27B). For vLLM and SGLang. GGUFs for llama.cpp: (measured on the BF16 source). - Swift's own NVFP4 recipe, unmodified, from ukisai/Swift-Qwen3.8-27B-NVFP4, calibrated with NVIDIA ModelOpt. The module split matches UkisAI's Swift 1.5 NVFP4 exactly. - MTP head and vision tower in BF16, bit-identical to the source. 21.9 GB, NVIDIA ModelOpt mixed-precision format. Needs a vLLM with ModelOpt mixed-precision support. No --quantization flag. Sampling, as for Swift and Qwen: temperature 1.0, topp 0.95, topk 20, minp 0. The model thinks before answering by default. Same format, recipe, module…

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Qwen3.8-27B-Uncensored-NVFP4-v100-skinny

Tim Eastwood

Mixed-precision quantization of prepared for dnv2003/v100-skinny. - MLP gateproj, upproj, downproj, and lmhead: NVFP4, group size 16 87c9f8cf83021957d1a1a575c90c9a4eaaf7ef0c See quantization-audit.json and hfquantconfig.json for the complete machine-readable layout. This model has had safety alignment substantially removed. Use it only for lawful, controlled research and add appropriate safeguards before deployment.

quantized to OCP MXFP4 4-bit weights in the same Quark checkpoint container as The weights are plain RTN, not AWQ: see the note below. The 15 MTP tensors are kept in BF16; the radiance runtime loads them with RADIANCEQUARKBF16MTP=1. Why RTN instead of AWQ. The first build of this checkpoint used AWQ with the same smoothing recipe as AMD's release. The AWQ fold itself was mathematically consistent, but on this model a few layers converged to weights to 32-element MXFP4 blocks then destroyed those layers on real, outlier-carrying inputs (layer 7 output relative error ~324), and repairing the worst layers individually was not enough; the remaining smoothed layers still accumulated too much…