Uncensored Nemotron-3-Nano-Omni multimodal model lands in GGUF format
An uncensored multimodal Nemotron-3-Nano variant with vision, audio, and Mamba2 architecture is now available in GGUF format for local inference.
Nemotron-3-Nano-Omni-AEON-Ultimate-Uncensored-GGUF, a new uncensored multimodal model, is now available on HuggingFace in quantized GGUF format for local deployment. The model combines vision, audio, and text capabilities using Mamba2, a state-space architecture that offers an alternative to transformer attention mechanisms.
The GGUF quantization makes the weights compatible with llama.cpp and other CPU/GPU inference engines, enabling local inference without cloud dependencies. Multimodal support means the model can process image and audio inputs alongside text prompts, positioning it for tasks requiring cross-modal understanding without the memory overhead of full-precision weights. The uncensored flag indicates the model has no safety filters or content restrictions, giving users full control over outputs.
Architecture and design
The "Omni" designation signals broad modality coverage—vision, audio, and text in a single model. Mamba2 is a state-space design that scales more efficiently than transformers on long sequences, though adoption in production multimodal systems remains limited. The "AEON" and "Ultimate" labels appear to be fine-tune or merge identifiers; the model card does not yet detail the training recipe or base checkpoint lineage.
The model is available now on HuggingFace under the hotdogs namespace, with GGUF files ready for download and local inference.