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AI モデル 84759

• embeddinggemma-2-ONNX↗

  onnx-community

  **EmbeddingGemma 2** is an open multimodal embedding model built by Google DeepMind which maps text (incl. code), images, video, and audio inputs—and combinations thereof—into a single, unified 768-dimensional vector space. The model has 740M total parameters, combining a 270M parameter text model with modular vision (170M) and audio (300M) encoders. Designed to run on consumer hardware such as mobile devices and laptops, EmbeddingGemma 2 delivers low-latency semantic representations for on-device applications, like search, retrieval-augmented generation (RAG), classification, and clustering. EmbeddingGemma 2 builds upon the architectural and capability advancements of Gemma 4, offering several core features:

• OpenMed-NER-DNADetect-PubMed-v2-109M↗

  OpenMed

  **Specialized model for Biomedical Entity Recognition - Proteins, DNA, RNA, cell lines, and cell types** This model is a **state-of-the-art** fine-tuned transformer engineered to deliver **enterprise-grade accuracy** for biomedical entity recognition - proteins, dna, rna, cell lines, and cell types. 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 JNLPBA dataset - **Production-Ready**: Validated on clinical benchmarks - **Easy Integration**: Compatible with Hugging Face Transformers ecosystem

• TinyLlama-1.1B-Chat-v1.0-int8-ov↗

  OpenVINO

• AffectNet-face-to-emotion-tflite↗

  pat229988

• Qwen-Image-Japanese-Female-Anatomy-Realism-NSFW-LoRA-v1↗

  perpetual3x

• Qwen-Image-Edit-Rapid-AIO-GGUF↗

  Phil2Sat

  No AIO, just NSFW and Lightning loras included, packaging VAE and text_encoder into GGUF is not possible ATM, needs the text encoder and the VAE. I personally recommend: Qwen2.5-VL-7B-Instruct-abliterated but the default text_encoder does also fine. I have cloned the abliberated files into my repo and fixed the mmproj ready to use. HERE: https://huggingface.co/Phil2Sat/Qwen-Image-Edit-Rapid-AIO-GGUF/tree/main/Qwen2.5-VL-7B-Instruct-abliterated

• Boole-1.7B↗

  precision-numerics

• grug-27b-v2-gguf↗

  ProCreations

• Qwen-1_8B↗

  阿里巴巴 · Qwen

• Qwen2.5-32B-Instruct-GPTQ-Int8↗

  阿里巴巴 · Qwen