全站搜索

AI 模型 85003

• ThinkingCap-Qwen3.8-27B-abliterated-GGUF↗

  mradermacher

• Unicorn-VL-R3-i1-GGUF↗

  mradermacher

• WaifuGemma4-26b-a4b-v1-GGUF↗

  mradermacher

• ASR-Somali↗

  Mustafaa4a

  This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3660 - Wer: 0.3060 The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP

• cerulean-lab4-q3-8c4c6237↗

  Nat1an

• ChronoEdit-14B-Diffusers↗

  NVIDIA

• xTTSv2-GGUF↗

  onicubag

• tiny-mistral↗

  openaccess-ai-collective

• OpenMed-NER-ProteinDetect-MultiMed-335M↗

  OpenMed

  **Specialized model for Biomedical Entity Recognition - Various biomedical entities** This model is a **state-of-the-art** fine-tuned transformer engineered to deliver **enterprise-grade accuracy** for biomedical entity recognition - various biomedical 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 FSU dataset - **Production-Ready**: Validated on clinical benchmarks - **Easy Integration**: Compatible with Hugging Face Transformers ecosystem

• cyrillic_PP-OCRv5_mobile_rec↗

  PaddlePaddle