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AI Models 84760

• NVIDIA-NemotronLabs-RE-USE-laughter↗

  NVIDIA

  NVIDIA NemotronLabs RE-USE-laughter is a 9.6-million-parameter **multilingual universal speech enhancement (USE)** model designed to restore speech quality across a wide range of real-world degradations while preserving speech fidelity. It has the following desirable properties: - **This model better preserves laughter while restoring the quality of the input speech.** - Robustness to **diverse degradations**, including additive noise, reverberation, clipping, bandwidth limitation, codec artifacts, packet loss and low-quality mics. - Support for **multiple input sampling rates**, including 8, 16, 22.05, 24, 32, 44.1, and 48 kHz. - Strong **language-agnostic** capability, enabling effective performance across different languages. (Note: For overlapping speech, this model is trained to preserve the main speaker while suppressing other speakers.) Comparison with other open-source USE models on the (subsets) of the URGENT 2025 non-blind test set, referenced against original anechoic clean speech.

• Qwen3.6-35B-v2-MoE-Ablit-Heretic-Uncensor-Hermes-MTP-Vision-Llama↗

  oktayd

• Qwen2.5-0.5B-Instruct-Gensyn-Swarm-grunting_toothy_elk↗

  okuzarabasi

• Qwen2.5-0.5B-Instruct-Gensyn-Swarm-coiled_rapid_chinchilla↗

  Okwgreg

• MiniCPM5-1B↗

  openbmb

  MiniCPM Tech Report | MiniCPM Wiki(Chinese) | GitHub Repo | UltraData | MiniCPM Desk Pet | Online Demo We are releasing **MiniCPM5-1B**, the first model in the **MiniCPM5** series. It is a dense 1B Transformer built for on-device, local deployment, and resource-constrained scenarios, reaching 1B-class open-source SOTA. 🏆 **1B-class open-source SOTA**: compared with strong open-source models in the same size class, MiniCPM5-1B reaches SOTA within this comparison set. Its advantage is most visible in agentic tool use, code generation, and difficult reasoning.

• OpenMed-NER-AnatomyDetect-ModernMed-149M↗

  OpenMed

  **Specialized model for Anatomical Entity Recognition - Anatomical structures and body parts** This model is a **state-of-the-art** fine-tuned transformer engineered to deliver **enterprise-grade accuracy** for anatomical entity recognition - anatomical structures and body parts. This specialized model excels at identifying and extracting biomedical entities from clinical texts, research papers, and healthcare documents, enabling applications such as **drug interaction detection**, **medication extraction from patient records**, **adverse event monitoring**, **literature mining for drug discovery**, and **biomedical knowledge graph construction** with **production-ready reliability** for clinical and research applications. - **High Precision**: Optimized for biomedical entity recognition - **Domain-Specific**: Trained on curated ANATOMY dataset - **Production-Ready**: Validated on clinical benchmarks - **Easy Integration**: Compatible with Hugging Face Transformers ecosystem

• whisper-medium-int8-ov↗

  OpenVINO

• Huihui-CyberStrike-OffSec-35B-abliterated-mlx-6Bit↗

  osxest

• SLANet_plus↗

  PaddlePaddle

• Q-TensorFormer↗

  Premchan369