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AI モデル 84759
- Lucy-128k-ONNX↗
onnx-community
- siglip2-base-patch16-256-ONNX↗
onnx-community
- InternVL3-2B-hf↗
OpenGVLab
> [!IMPORTANT] > This repository contains the Hugging Face 🤗 Transformers implementation for the OpenGVLab/InternVL3-2B model. > It is intended to be functionally equivalent to the original OpenGVLab release. > As a native Transformers model, it supports core library features such as various attention implementations (eager, including SDPA, and FA2) and enables efficient batched inference with interleaved image, video, and text inputs. We introduce InternVL3, an advanced multimodal large language model (MLLM) series that demonstrates superior overall performance. Compared to InternVL 2.5, InternVL3 exhibits superior multimodal perception and reasoning capabilities, while further extending its multimodal capabilities to encompass tool usage, GUI agents, industrial image analysis, 3D vision perception, and more. Additionally, we compare InternVL3 with Qwen2.5 Chat models, whose corresponding pre-trained base models are employed as the initialization of the langauge component in InternVL3. Benefitting from Native Multimodal Pre-Training, the InternVL3 series achieves even better overall text performance than the Qwen2.5 series. Here is how you can use the `image-text-to-text` pipeline to perform inference with the `InternVL3` models in just a few lines of code:
- OpenMed-NER-BloodCancerDetect-BioClinical-108M↗
OpenMed
**Specialized model for Clinical Entity Recognition - Clinical entities related to Chronic Lymphocytic Leukemia** This model is a **state-of-the-art** fine-tuned transformer engineered to deliver **enterprise-grade accuracy** for clinical entity recognition - clinical entities related to chronic lymphocytic leukemia. 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 CLL dataset - **Production-Ready**: Validated on clinical benchmarks - **Easy Integration**: Compatible with Hugging Face Transformers ecosystem
- OpenMed-NER-GenomicDetect-TinyMed-135M↗
OpenMed
This model is a **state-of-the-art** fine-tuned transformer engineered to deliver **enterprise-grade accuracy** for gene entity recognition - gene-related entities. 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 GELLUS dataset - **Production-Ready**: Validated on clinical benchmarks - **Easy Integration**: Compatible with Hugging Face Transformers ecosystem Gellus corpus targets gene recognition and genetics entities for genomics and molecular biology applications.
- tiny-random-latent-consistency↗
optimum-intel-internal-testing
- tiny-random-vision_perceiver_conv↗
optimum-intel-internal-testing
- Qwen2.5-0.5B-Instruct-Gensyn-Swarm-grazing_stealthy_chicken↗
posb
- Qwen3.8-27B-NVFP4-MTP-2x16GB↗
pottokao
- Hermes2-Gutenberg2-Mistral-7B-GGUF↗
QuantFactory