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AI モデル 85003
- NaviDC-OCR-GGUF↗
nandraj
GGUF conversions of the original **StarDoc-AI/NaviDC-OCR** vision-language model for use with **llama.cpp** and other GGUF-compatible inference runtimes. > **Important:** This repository contains converted GGUF files of the > original `StarDoc-AI/NaviDC-OCR` model. It is **not the original model > repository**, and the GGUF files have not been trained or fine-tuned by > the maintainer of this repository. > ⚠️ **Requires a patched llama.cpp build** — stock llama.cpp fails to load this model with a `check_tensor_dims` shape error; see PATCHES.md for the required source changes before building.
- KrakenSakura-Maelstrom-12B-v1-GGUF↗
Naphula
- ChemGPT-1.2B↗
ncfrey
- qwen3.5-9b-gguf↗
nile-academy
- Google_Gemma4-E2B-GGUF↗
NobodyWho
- Phi-3-mini-4k-instruct-function-calling-GGUF↗
nold
- Efficient-DLM-4B↗
NVIDIA
- InternVideo2-Stage2_6B↗
OpenGVLab
- OpenMed-NER-DiseaseDetect-ElectraMed-33M↗
OpenMed
**Specialized model for Disease Entity Recognition - Disease entities from the BC5CDR dataset** This model is a **state-of-the-art** fine-tuned transformer engineered to deliver **enterprise-grade accuracy** for disease entity recognition - disease entities from the bc5cdr 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 BC5CDR_DISEASE dataset - **Production-Ready**: Validated on clinical benchmarks - **Easy Integration**: Compatible with Hugging Face Transformers ecosystem
- OpenMed-NER-PathologyDetect-SuperClinical-141M↗
OpenMed
**Specialized model for Disease Entity Recognition - Disease entities from the NCBI dataset** This model is a **state-of-the-art** fine-tuned transformer engineered to deliver **enterprise-grade accuracy** for disease entity recognition - disease entities from the ncbi 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 NCBI_DISEASE dataset - **Production-Ready**: Validated on clinical benchmarks - **Easy Integration**: Compatible with Hugging Face Transformers ecosystem