Qwopus3.6 27B v2 GPTQ Pro FOEM 4 bit g128 ns256 v2 These models are built and maintained on rented GPU compute. If you want to show some appreciation, a follow on X or a coffee helps keep the releases coming. Follow @xreyrobert Buy me a coffee This is a GPTQ Pro 4 bit quantization of Jackrong/Qwopus3.6 27B v2 , built to make this excellent Qwopus/Qwen3.6 model practical to run in vLLM with GPTQ Marlin kernels and long context inference. The goal is simple: preserve as much of the original model's character and capability as possible while making it efficient enough for single GPU RTX 3090 class vLLM deployments. This is not a new fine tune. It is a quantized derivative of the original Qwopus3.6 27B v2 model. Source and credits Source model: Jackrong/Qwopus3.6 27B v2 Quantization methodology and reference recipe: groxaxo/GPTQ Pro groxaxo/Qwen3.6 27B GPTQ Pro 4bit Thanks to Jackrong for the original Qwopus3.6 model, and to groxaxo for GPTQ Pro and the Qwen3.6 GPTQ Pro recipe this quantization was aligned with. Quantization recipe Setting Value : Method GPTQ Pro / GPTQModel Bits 4 Group size 128 Symmetric quantization true Desc act false True sequential true Calibration dataset WikiTe…
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