DeepSeek-V4.1-Flash-1017-Maya-GGUF

GGUF quantizations of deepseek-ai/DeepSeek-V4.1-Flash-1017 (a mixture of experts: 384 routed experts per layer, 6 active per token, plus a shared expert), made for **Project Maya**. Project Maya keeps the most-used experts on the GPU and reads the rest from RAM and the SSD, so the model runs well below its size. Both quants are made from DeepSeek's own release (FP4 experts, FP8 dense weights), not from a re-quantized file, and keep every expert. Measured on held-out text never used for calibration, against the original model's own predictions (DeepSeek's reference implementation running the original weights). As a yardstick, the original weights run through Project Maya's engine give 96.4% - the engine's own rounding; the rest of each quant's distance is the quantization. > **Built partly on the IST Austria DAS Lab (ISTA-DASLab) recipe.** The experts > are rounded with GPTQ-style error feedback (each against its own input statistics), and the result is measured > against the full-precision model.

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