Luigi/MOSS-TTS-Nano-100M-GGUF overview
MOSS TTS Nano 100M — GGUF Q8 0 for RapidSpeech.cpp / ggml CUDA Q8 0 GGUF conversion of OpenMOSS Team/MOSS TTS Nano 100M https://huggingface.co/OpenMOSS Team/MO…
Runs locally from ~12.7 MB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).
Repository Files & Downloads
Model Details
Model README
---
license: apache-2.0
language: [zh, en]
tags: [tts, gguf, moss, jetson, ggml, edge]
base_model: OpenMOSS-Team/MOSS-TTS-Nano-100M
---
MOSS-TTS-Nano-100M — GGUF (Q8_0) for RapidSpeech.cpp / ggml-CUDA
Q8_0 GGUF conversion of OpenMOSS-Team/MOSS-TTS-Nano-100M
for on-device inference on Jetson Nano gen1 (Maxwell sm_53) via ggml-CUDA.
moss_nano_full.gguf— merged AR model + codec decoder, Q8_0 (~138 MB from 440 MB fp32).
- AR: GPT-2 12-layer global + 1-layer local decoder + 16 audio codebook heads (interleaved RoPE, gelu_new).
- Codec: MOSS-Audio-Tokenizer-Nano decoder + 16-way RVQ (weight_norm reconstructed) → 48 kHz.
moss_nano.gguf— AR model only.moss_codec.gguf— codec only.
Verified (torch-free, vs the deployed ONNX): global transformer prefill+decode MSE 1.4e-6,
local decoder 14/16 argmax (Q8), codec round-trip corr 0.95–0.998.
Performance (Jetson Nano GPU, ggml-CUDA): Q8 RTF 0.86 (full 12L) / 0.50 (4L student);
~0.35 with the custom sm_53 matvec kernel. RTF floor ~0.12.
Runtime: vieenrose/RapidSpeech.cpp (branch jetson-nano-gen1),
arch moss_tts_nano. Converters in scripts/convert_moss_*_to_gguf.py.
Run Luigi/MOSS-TTS-Nano-100M-GGUF with guIDE
Download guIDE — the AI-native code editor with local LLM inference and 69 built-in tools.
Source: Hugging Face · Compare models