Unsloth drops MiniMax M2.7 GGUF quants

This release democratizes access to a model that rivaled GPT-5.4 on MLE Bench Lite, positioning Unsloth's Dynamic 2.0 quantization as a gold standard for running massive MoE models on consumer hardware. 1-bit quants enable running the 229B model in roughly 60GB VRAM, while high performance on SWE-Pro benchmarks and native support for stable "Agent Teams" makes it a top-tier candidate for autonomous workflows. These custom GGUF implementations even outperform GPT-5.3 on productivity tasks, though users should note the explicit warning against CUDA 13.2 for maximum precision.