MiniMax-M3.1-Flash-Maya-GGUF

GGUF quantizations of MiniMaxAI/MiniMax-M3.1-Flash (428B parameters, 23B active per token: 128 routed experts per layer, 4 active, plus a shared expert; sparse attention in every layer; up to a 1M-token context), made for **Project Maya**. Project Maya keeps the most-used experts on the GPU and computes the rest from RAM, so the model runs well below its size. Both quants are made from MiniMax's own release (FP4 experts, FP8 dense weights), not from a re-quantized file, and keep every expert. Measured on held-out conversations never used for calibration, on the model's own answers, against the original model's predictions (MiniMax's released weights, decoded exactly), with the files as published running in Project Maya's engine - the same results on NVIDIA and AMD. 1. **Calibration text** in MiniMax's own chat format, thinking included: tool calls and agent traces, code (with three.js / canvas / WebGL and other front-end work), reasoning, long answers, chat, multilingual chat - 384 sequences of 2,048 tokens. A separate held-out set is kept for evaluation. 2. **Statistics from the model itself**, layer by layer: an importance matrix for every expert separately. 3. **Where the bits go:** every layer's gate, up and down matrices were measured in each candidate type, and the extra bits went to the layers where the model is most sensitive. 4. **Error-feedback rounding** of every expert, at the quantizer's 256-weight blocks, each expert against its own input statistics - and each layer calibrated on what the already-quantized layers before it produce. 5. **llama.cpp's own quantizers (ggml)**, so the files are ordinary GGUFs in llama.cpp's `minimax-m3` layout.

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