AI BriefingSwift-1.5-Qwen3.8-27B-GSQ-RCO-GGUF: GGUF with 27B-class inference performance via IQ2 quantization
ukisai/Swift-1.5-Qwen3.8-27B-GSQ-RCO-GGUF
About the project
Provides a 27B parameter model based on Qwen3 in ultra-low capacity quantized versions such as IQ2_S and IQ3_S. With a total file size of approximately 9.2GB, it is designed to enable large language model inference in environments with limited VRAM.
GSQ and RCO techniques are applied to minimize performance degradation during quantization. It particularly supports reasoning and efficient-thinking tags, making it suitable for tasks requiring complex logical reasoning or token efficiency.
Compatible with major inference engines such as llama.cpp, vLLM, and Ollama. It includes imatrix data to maintain model quality even at low-bit quantization, and is distributed under the swift-open-license-1.0 license.
ukisai/Swift-1.5-Qwen3.8-27B-GSQ-RCO-GGUF
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