Repository: localaiLicense: apache-2.0
MOSS-TTS-Local v1.5 (C++/ggml, moss-tts.cpp), Q8_0 (~6 GB), near-lossless. Native C++ text-to-speech for the OpenMOSS MOSS-TTS-Local Transformer v1.5 (a GPT-J local transformer decoded through the MOSS-Audio-Tokenizer-v2 neural codec), 48kHz stereo output with reference-audio voice cloning (set `voice` to a reference .wav). Verified per component against the reference PyTorch and about 2x faster per frame on CPU.
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MOSS-TTS-Local v1.5 (C++/ggml, moss-tts.cpp), F16 (~9.4 GB), code-exact versus the reference. 48kHz stereo text-to-speech with reference-audio voice cloning.
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Repository: localaiLicense: apache-2.0

MOSS-Image-Base is a 5B diffusion transformer image generation model from OpenMOSS-Team. It uses Qwen3.5 9B as the LLM text encoder, the FLUX.2 VAE, and an external tokenizer. The Base variant supports text-to-image generation with classifier-free guidance (default: CFG 3.0, 1024x1024). This entry uses the int8_convrot quantization of the diffusion model (~5.4 GB).
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Repository: localaiLicense: apache-2.0

MOSS-Image-SFT is the supervised fine-tuned variant of MOSS-Image, a 5B diffusion transformer image generation model from OpenMOSS-Team. It uses Qwen3.5 9B as the LLM text encoder, the FLUX.2 VAE, and an external tokenizer. The SFT variant produces higher-quality outputs with classifier-free guidance (default: CFG 3.0, 1024x1024). This entry uses the int8_convrot quantization of the diffusion model (~5.4 GB).
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Repository: localaiLicense: apache-2.0
MOSS-Transcribe-Diarize 0.9B, an end-to-end audio understanding model for joint multi-speaker transcription, speaker diarization and timestamping in a single pass. Q5_K GGUF for the moss-transcribe-cpp backend (C++/ggml port of OpenMOSS MOSS-Transcribe-Diarize), byte-identical to the reference at ~1/6 the size. Faster than the reference PyTorch runtime on CPU (1.6 to 2.2x).
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