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AI 模型 85003

• NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4↗

  NVIDIA

  * The pre-training data has a cutoff date of September 2025. * The post-training data has a cutoff date of May 2026. NVIDIA Nemotron™ is a family of open models with open weights, training data, and recipes, delivering leading efficiency and accuracy for building specialized AI agents. **NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4** is a large language model (LLM) trained by NVIDIA.

• embeddinggemma-300m-ONNX↗

  onnx-community

  **Model Page**: EmbeddingGemma * Responsible Generative AI Toolkit * EmbeddingGemma on Kaggle * EmbeddingGemma on Vertex Model Garden EmbeddingGemma is a 300M parameter, state-of-the-art for its size, open embedding model from Google, built from Gemma 3 (with T5Gemma initialization) and the same research and technology used to create Gemini models. EmbeddingGemma produces vector representations of text, making it well-suited for search and retrieval tasks, including classification, clustering, and semantic similarity search. This model was trained with data in 100+ spoken languages.

• tiny-random-qwen3-eagle3↗

  optimum-intel-internal-testing

• tiny-random-qwen3-omni↗

  optimum-intel-internal-testing

• gemma-4-26B-A4B-it-MXFP4-GGUF↗

  pirola

• TabPFN-v2-reg↗

  Prior-Labs

  TabPFN is a transformer-based foundation model for tabular data that leverages prior-data based learning to achieve strong performance on small tabular regression tasks without requiring task-specific training. - **Developed by:** Prior Labs - **Model type:** Transformer-based foundation model for tabular data - **License:** Prior Labs License (Apache 2.0 with additional attribution requirement) - **Paper:** Published in Nature (January 2025) - **Repository:** GitHub - priorlabs/tabpfn 📚 For detailed usage examples and best practices, check out: - Interactive Colab Tutorial

• Carbon-X-GGUF↗

  prithivMLmods

• WAI-illustrious-HSWQ_v17.0_base_checkpoint↗

  Quiho

• LLaDa-Image-Turbo_ComfyUI↗

  realrebelai

  This associates the repository with the main LLaDA-Image-Turbo model as a quantized derivative in the Hugging Face model relationship/quantization tree. The custom ComfyUI runtime loads the quantized transformer while preserving the LLaDA-Image transformer architecture. The LLaDA2-MoE text encoder is provided as Q4_K_M GGUF for substantially lower storage/runtime memory requirements than the original full text encoder.

• ideogram-4-gguf↗

  rectangleworm

  To balance generation quality, memory usage, and inference speed, we recommend the following quantization choices for each component: If you are optimizing for inference speed or trying to fit a specific model entirely into VRAM/RAM, keep these rules of thumb in mind: