audio-cpp/AuK-Base-and-Flash-GGUF

text-to-speech model by audio-cpp

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These GGUF files are for use with audio.cpp. The card's basemodel points to the Tencent AuK base model. This package also includes optional files converted from AuK-Flash; Flash is a separate distilled variant and is not the pinned base model. Pass the downloaded directory to audiocppcli --model. Keep the config/ and tokenizer/ directories alongside the GGUF files. The default components are auk-base-f32.gguf, qwen2.5-omni-3b-bf16.gguf, and auk-vae-f32.gguf. AuK-Flash requires --session-option auk.variant=flash; its default generator is auk-flash-f32.gguf. The Qwen component is Qwen2.5-Omni-3B. For example, instruction TTS with explicitly selected Base components: To use AuK-Flash, select auk.variant=flash and an auk-flash-.gguf generator. To use the Qwen Q80 component, select auk.qwengguf=qwen2.5-omni-3b-q80.gguf. Keep the F32 VAE in either case. For editing, use --task gen --audio input.wav --text "". See the audio.cpp AuK guide and the AuK cookbook for request options and example instructions. The 16-task C++/Python comparison forced FP32 inference and disabled TF32 in both implementations. Input…

audio-cpp/AuK-Base-and-Flash-GGUF on Hugging Face A short preview, not the full document.

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Inside the original model card — Document outline
  1. AuK GGUF for audio.cpp
  2. Sixteen-task validation
  3. Dtypes and validation scope

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Model overview

audio-cpp/AuK-Base-and-Flash-GGUF is a text-to-speech model repository published by audio-cpp on Hugging Face. A library was not reported.

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Model facts

Task
text-to-speech
Library
Not reported
Recent downloads (30 days)
16,106
Cumulative likes
0
Hugging Face trending score
0
Reported safetensors parameters
Not reported
Architecture
Not reported
License
mit
Access
Not gated by Hugging Face
Created
2026-09-20T01:02:43.000Z
Last modified
2026-09-21T14:18:04.000Z

Compatibility and lineage

Language coverage was not reported.

Reported base models: tencent/AuK

Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.

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