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AI 模型 84759
- OpenMed-NER-PharmaDetect-ElectraMed-335M↗
OpenMed
**Specialized model for Chemical Entity Recognition - Chemical entities from the BC5CDR dataset** This model is a **state-of-the-art** fine-tuned transformer engineered to deliver **enterprise-grade accuracy** for chemical entity recognition - chemical 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_CHEM dataset - **Production-Ready**: Validated on clinical benchmarks - **Easy Integration**: Compatible with Hugging Face Transformers ecosystem
- OpenMed-PII-German-BiomedELECTRA-Base-110M-v1↗
OpenMed
**OpenMed-PII-German-BiomedELECTRA-Base-110M-v1** is a transformer-based token classification model fine-tuned for **Personally Identifiable Information (PII) detection in German text**. This model identifies and classifies **54 types of sensitive information** including names, addresses, social security numbers, medical record numbers, and more.
- privacy-filter-mlx-8bit↗
OpenMed
- Qwen3-Coder-30B-A3B-Instruct-NVFP4↗
OPENZEKA
- P-Q-Image-2.1↗
PrunaAI
6 steps (v0.2), 5 or 8 steps (v0.1) · Up to 6.3x faster · No CFG · Text-to-image and image editing **P-Q-Image-2.1** is a set of LoRA adapters that let `Qwen/Qwen-Image-2.1` generate and edit images in only 6 steps (v0.2), or 5 or 8 steps (v0.1). The adapters load on top of the base model, so the pipeline, text encoder, and VAE stay unchanged. > **v0.2: two new 6-step adapters.** > > - An advanced distillation for more faithful generation and editing. > - The first distillation removing need for prompt upsampling into the LoRA, thus significantly reducing end-to-end generation. > The v0.1 adapters remain available.
- Qwen1.5-MoE-A2.7B-Chat↗
阿里巴巴 · Qwen
Qwen1.5-MoE is a transformer-based MoE decoder-only language model pretrained on a large amount of data. For more details, please refer to our blog post and GitHub repo. Qwen1.5-MoE employs Mixture of Experts (MoE) architecture, where the models are upcycled from dense language models. For instance, `Qwen1.5-MoE-A2.7B` is upcycled from `Qwen-1.8B`. It has 14.3B parameters in total and 2.7B activated parameters during runtime, while achieching comparable performance to `Qwen1.5-7B`, it only requires 25% of the training resources. We also observed that the inference speed is 1.74 times that of `Qwen1.5-7B`.
- 01-ai_-_Yi-1.5-6B-gguf↗
RichardErkhov
- EleutherAI_-_Mistral-7B-v0.1-population-first-ft-gguf↗
RichardErkhov
- roberta-base-formality-ranker↗
s-nlp
The model has been trained to predict for English sentences, whether they are formal or informal. Datasets: GYAFC from Rao and Tetreault, 2018 and online formality corpus from Pavlick and Tetreault, 2016. Data augmentation: changing texts to upper or lower case; removing all punctuation, adding dot at the end of a sentence. It was applied because otherwise the model is over-reliant on punctuation and capitalization and does not pay enough attention to other features.
- kitty-bash-llm↗
sahellx