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AI モデル 85003
- bert-small2bert-small-finetuned-cnn_daily_mail-summarization↗
mrm8488
- Qwen3.6-35B-A3B-MTP-MXFP4_MOE-GGUF↗
noctrex
- nunchaku-qwen-image-edit↗
nunchaku-ai
- **Developed by:** Nunchaku Team - **Model type:** image-to-image - **License:** apache-2.0 - **Quantized from model:** Qwen-Image-Edit **Data Type**: `INT4` for non-Blackwell GPUs (pre-50-series), `NVFP4` for Blackwell GPUs (50-series). **Rank**: `r32` for faster inference, `r128` for better quality but slower inference. 4-step distilled models fused with Qwen-Image-Edit-Lightning-4steps-V1.0 LoRA using LoRA strength = 1.0
- gpt-4o-mini-realtime-preview-2024-12-17↗
OpenAI
- gpt-oss-20b (Low)↗
OpenAI
- InternVL2_5-4B-AWQ↗
OpenGVLab
- OpenMed-NER-DNADetect-ElectraMed-335M↗
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
- tiny-random-MobileBertModel↗
optimum-intel-internal-testing
- pqn-ai-v1↗
pqn-ai
- Ph3task2-14B-GGUF↗
QuantFactory