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AI Models 85003

• ThinkPRM-7B-i1-GGUF↗

  mradermacher

• tinyllama-roleplay-i1-GGUF↗

  mradermacher

• TinyMixtral-4x248M-MoE-GGUF↗

  mradermacher

• Trinity-Nano-Preview-i1-GGUF↗

  mradermacher

• granite-3.3-8b-base-GGUF↗

  mrutkows

• Gemma-4-26B-A4B-NVFP4↗

  NVIDIA

  Gemma 4 26B IT is an open multimodal model built by Google DeepMind that handles text and image inputs, can process video as sequences of frames, and generates text output. It is designed to deliver frontier-level performance for reasoning, agentic workflows, coding, and multimodal understanding on consumer GPUs and workstations, with a 256K-token context window and support for over 140 languages. The model uses a hybrid attention mechanism that interleaves local sliding-window and full global attention, with unified Keys and Values in global layers and Proportional RoPE (p-RoPE) to support long-context performance. The NVIDIA Gemma 4 26B IT NVFP4 model is quantized with NVIDIA Model Optimizer. This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party's requirements for this application and use case; see link to Non-NVIDIA Gemma 4 26B IT Model Card **Use Case:** Designed for text generation, chatbots and conversational AI, text summarization, image data extraction, reasoning, coding, multimodal understanding, function calling, and research or educational use.

• comic-text-and-bubble-detector↗

  ogkalu

  RT-DETR-v2 r50vd model fine-tuned on about 11k Manga, Webtoon, Manhua and Western Comic style Images for text and speech bubble detection. Training Image Size = 640. Training Images were resized, not cropped. Tall Webtoons were split vertically. Classes are: 0: bubble 1: text_bubble (text inside bubbles) 2: text_free (text outside bubbles)

• OneJev-0.8B↗

  OmniJev

• chatterbox-multilingual-ONNX↗

  onnx-community

• OpenMed-NER-OrganismDetect-BioMed-109M↗

  OpenMed

  **Specialized model for Species Entity Recognition - Species names from the Species-800 dataset** This model is a **state-of-the-art** fine-tuned transformer engineered to deliver **enterprise-grade accuracy** for species entity recognition - species names from the species-800 dataset. 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 SPECIES800 dataset - **Production-Ready**: Validated on clinical benchmarks - **Easy Integration**: Compatible with Hugging Face Transformers ecosystem