CED (GGUF) for ced.cpp / LocalAI GGUF quantizations of the CED family (Consistent Ensemble Distillation, Xiaomi) SOTA tier audio tagging models that classify everyday sounds (baby cry, footsteps, glass breaking, alarms, dog bark, ...) into the 527 class AudioSet ontology. These files run with ced.cpp , a standalone C++/ggml port (no Python, no PyTorch at inference), and with LocalAI via the ced backend. Converted from the mispeech/ced checkpoints (Apache 2.0). CED is a plain AST/DeiT Vision Transformer over a log mel spectrogram; the port is numerically equal to the PyTorch reference. Files One self contained GGUF per size + quant (config, 527 labels, and the mel filterbank/window are all embedded). Pick by your accuracy/size budget: size params f16 q8 0 f32 tiny 5.5M ced tiny f16.gguf (11 MB) ced tiny q8 0.gguf (6 MB) mini 9.6M ced mini f16.gguf (19 MB) ced mini q8 0.gguf (11 MB) small 22M ced small f16.gguf (42 MB) ced small q8 0.gguf (23 MB) base 86M ced base f16.gguf (165 MB) ced base q8 0.gguf (88 MB) ced base f32.gguf (328 MB) tiny / q8 0 (6 MB) is ideal for Raspberry Pi class CPUs; base / f16 is the accuracy default. Parity vs PyTorch (ced base, end to end probs) quant max a…
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