AI is happening. The future is updating.MiniMax-3.1-Flash-DeRisked-GGUF
GGUF conversion of Blackfrost-Research/MiniMax-3.1-Flash-DeRisked, converted with llama.cpp `convert_hf_to_gguf.py`. Vision tower and multimodal projector are included as a separate mmproj GGUF — image understanding works through `llama-mtmd-cli` / `llama-server`. The upstream DeRisked checkpoint ships experts pre-quantized to NVFP4 (compressed-tensors `nvfp4-pack-quantized`) and the rest as MXFP8. This conversion repacks the NVFP4 expert tensors losslessly into GGUF's native NVFP4 layout (per-expert `weight` + `.scale` + `.input_scale` companions) and dequantizes MXFP8 blocks to BF16 in the F16 master before quantizing. *Approximate; experts dominate size at every quant level. All language-model files are split GGUF — download **every** `-0000N-of-0000M` shard of the quant you want.
GGUF conversion of Blackfrost-Research/MiniMax-3.1-Flash-DeRisked, converted with llama.cpp `convert_hf_to_gguf.py`. Vision tower and multimodal projector are included as a separate mmproj GGUF — image understanding works through `llama-mtmd-cli` / `llama-server`. The upstream DeRisked checkpoint ships experts pre-quantized to NVFP4 (compressed-tensors `nvfp4-pack-quantized`) and the rest as MXFP8. This conversion repacks the NVFP4 expert tensors losslessly into GGUF's native NVFP4 layout (per-expert `weight` + `.scale` + `.input_scale` companions) and dequantizes MXFP8 blocks to BF16 in the F16 master before quantizing. *Approximate; experts dominate size at every quant level. All language-model files are split GGUF — download **every** `-0000N-of-0000M` shard of the quant you want.