The Qwen3.5-9B-Cold-Fusion-GAIN model has been released with increased performance

Developers presented Qwen3.5-9B-Cold-Fusion-GAIN-v1.0 — a specialized version of the language model optimized for high detail in responses. The release includes quantized GGUF versions prepared using the Imatrix method to maintain accuracy during compression. Key feature of the model is the application of the GAIN method, which allows the 9B model to achieve performance metrics comparable to 27B class solutions. Testing was conducted in 4-bit and 8-bit quantization modes, providing a balance between generation speed and quality of logical conclusions. The model is positioned as an "uncensored" solution, implying the absence of built-in content generation restrictions. During internal testing, the system scored 29 points on a specialized benchmark in 1 hour of data processing, demonstrating improved reasoning capabilities compared to the base architecture. Using the GGUF format makes the model accessible for running on consumer equipment with limited video memory. Developers note that the Cold-Fusion-GAIN architecture is aimed at users who require high information density in responses without security system filtering. This release expands the ecosystem of compact models capable of competing with heavier weight categories due to optimization of training methods and quantization. The tool is already available for download and integration into local execution environments.