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moss_tts_delay llama.cpp inference path instead of
the full bf16 PyTorch weights — so MOSS-TTS fits comfortably on memory-constrained machines
(e.g. 16–24 GB Apple Silicon).backbone_q6_k.gguf — the MOSS-TTS v1.5 backbone quantized to Q6_K, with the
token-embedding tensor (token_embd) kept at F16. This is deliberate and important:
MOSS-TTS reads per-token text/audio embeddings directly from that table, and quantizing it
below F16 degrades output. Quantize everything else, keep token_embd at F16
(llama-quantize --token-embedding-type f16).extracted/ — the npy embedding + LM-head tables and tokenizer that the moss_tts_delay
llama.cpp pipeline loads alongside the GGUF (the audio/codec heads that don't live inside the
backbone GGUF).OpenMOSS-Team/MOSS-Audio-Tokenizer-ONNX.OpenMOSS-Team/MOSS-TTS-v1.5.convert_hf_to_gguf.py), then
llama-quantize --token-embedding-type f16 … Q6_K. On an internal speech-quality battery
(word-error-rate, speaker-similarity, and mel-cepstral-distortion), Q6_K matched Q8 while
using less memory — making it a good default quant for running MOSS-TTS locally.