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AI モデル 84759
- zephyr-7b-dpo-maximal-GGUF↗
mradermacher
- Holo-3.1-4B-GGUF↗
Mungert
- Qwen3.8-27B-NInfer↗
neroued
This model card is the version-controlled source for neroued/Qwen3.8-27B-NInfer. The repository contains Qwen3.8-27B converted to the native NInfer `.ninfer` artifact format. The artifact is intended only for NInfer; it is not a Transformers checkpoint, Safetensors distribution, or GGUF file. The Text body uses Q4/Q5 projections, while the token embedding and full output head use `q8_g32_fp16`. The file also contains Vision, MTP, DFlash2, the optimized proposal head and frontend resources. Vision and speculative weights are loaded only when selected at startup.
- babylm-mop-10m-gpt2↗
NeTS-lab
- gpt4all-j-sharegpt-dolly-decay-epoch_4↗
nomic-ai
- NV-Generate-MR-Brain↗
NVIDIA
- gpt-4-v↗
OpenAI
- OpenMed-NER-GenomicDetect-BigMed-560M↗
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.
- OpenMed-NER-OrganismDetect-BioClinical-108M↗
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
- tiny-random-llama4↗
optimum-intel-internal-testing
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).