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AI 모델 84759

• RxDecide-1.7B-GGUF↗

  mradermacher

• Self-Certainty-Qwen3-8B-Base-OpenRS-i1-GGUF↗

  mradermacher

• shadow-clown-7B-dare-GGUF↗

  mradermacher

• Solon_Athens_v2-i1-GGUF↗

  mradermacher

• spikingkiki-122b-a10b-i1-GGUF↗

  mradermacher

• synoema-coder-7b-v6-0.1.0a3-GGUF↗

  mradermacher

• Unhinged-Qwen2.5-R1-1M-Uncensored-BF16-i1-GGUF↗

  mradermacher

• vibevoice.cpp-models↗

  mudler

  **Brought to you by the LocalAI team** — the creators of LocalAI, the open-source AI engine that runs any model — LLMs, vision, voice, image, video — on any hardware. No GPU required. Quantized GGUF weights for vibevoice.cpp, a C/C++ port of Microsoft VibeVoice (TTS + ASR) on top of `ggml`. `scripts/quantize_gguf.py` in the source repo selectively quantizes only the LM matmul weights — attention q/k/v/o, ffn gate/up/down, and lm_head — to Q8_0. Everything else (1-D conv kernels, RMSNorm scales, biases, layer-scale gammas, token embeddings, small scalars) passes through unchanged. The conv1d implementation in vibevoice.cpp casts kernels to F16 inline rather than dequantizing on the fly, so quantizing those would corrupt the convolution outputs.

• moss-transcribe-diarize↗

  OpenASR

  **Joint transcription and speaker diarization in a single 0.9B-parameter model — know who said what, and when** Native speech-to-text in the **OpenASR** runtime — engineered for peak performance on CPU & GPU, **no Python at inference time**. RTF = real-time factor on the fixed 11s JFK clip (**lower is faster**); RAM peak measured per pack in an isolated subprocess. JFK ΔWER compares each quantized build's JFK transcript to this model's fp16 JFK transcript, so it measures quantization drift rather than absolute recognition accuracy. **q8_0** is the recommended default — near-reference quality at a fraction of the footprint.

• MOSS-VoiceGenerator↗

  OpenMOSS-Team

  MOSS‑TTS Family is an open‑source **speech and sound generation model family** from MOSI.AI and the OpenMOSS team. It is designed for **high‑fidelity**, **high‑expressiveness**, and **complex real‑world scenarios**, covering stable long‑form speech, multi‑speaker dialogue, voice/character design, environmental sound effects, and real‑time streaming TTS. When a single piece of audio needs to **sound like a real person**, **pronounce every word accurately**, **switch speaking styles across content**, **remain stable over tens of minutes**, and **support dialogue, role‑play, and real‑time interaction**, a single TTS model is often not enough. The **MOSS‑TTS Family** breaks the workflow into five production‑ready models that can be used independently or composed into a complete pipeline. **MOSS Voice Generator** is an open-source voice generation system designed to enable the creation of custom speaker timbres from free-form textual descriptions. This model allows users to generate voices that reflect specific characters, personalities, and emotions. It is particularly notable for its ability to produce speech with natural-sounding emotional expressiveness, providing a realistic and nuanced listening experience. As an open-source tool, MOSS Voice Generator is suitable for a variety of applications, such as audiobooks, game dubbing, role-playing agents, and conversational assistants.