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AI モデル 84759

• OpenMed-ZeroShot-NER-Species-Small-166M↗

  OpenMed

  **Specialized model for Species Entity Recognition - Species and organism names** Specialized in **species and organism mentions** with robust handling of scientific/common names and abbreviations.Applies to **biodiversity mining**, **metagenomics reporting**, and **taxonomy-aware literature curation**. OpenMed ZeroShot NER is an advanced, domain-adapted Named Entity Recognition (NER) model designed specifically for medical, biomedical, and clinical text mining. Leveraging state-of-the-art zero-shot learning, this model empowers researchers, clinicians, and data scientists to extract expert-level biomedical entities—such as diseases, chemicals, genes, species, and clinical findings—directly from unstructured text, without the need for task-specific retraining.

• PP-LCNet_x1_0_doc_ori_safetensors↗

  PaddlePaddle

• FireRed-OCR-GGUF↗

  prithivMLmods

• tencent-WeVisOne-VL-4B-GGUF↗

  prithivMLmods

• dolphin-2.9-llama3-70b-GGUF-smashed↗

  PrunaAI

• DeepSeek-R1-Distill-Qwen-1.5B-GGUF↗

  QuantFactory

• DuckDB-NSQL-7B-v0.1-GGUF↗

  QuantFactory

• Qwen3 30B A3B (Reasoning)↗

  阿里巴巴 · Qwen

• Qwen3-Embedding-4B-GGUF↗

  阿里巴巴 · Qwen

  The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. Building upon the dense foundational models of the Qwen3 series, it provides a comprehensive range of text embeddings and reranking models in various sizes (0.6B, 4B, and 8B). This series inherits the exceptional multilingual capabilities, long-text understanding, and reasoning skills of its foundational model. The Qwen3 Embedding series represents significant advancements in multiple text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bitext mining. **Exceptional Versatility**: The embedding model has achieved state-of-the-art performance across a wide range of downstream application evaluations. The 8B size embedding model ranks **No.1** in the MTEB multilingual leaderboard (as of June 5, 2025, score **70.58**), while the reranking model excels in various text retrieval scenarios. **Comprehensive Flexibility**: The Qwen3 Embedding series offers a full spectrum of sizes (from 0.6B to 8B) for both embedding and reranking models, catering to diverse use cases that prioritize efficiency and effectiveness. Developers can seamlessly combine these two modules. Additionally, the embedding model allows for flexible vector definitions across all dimensions, and both embedding and reranking models support user-defined instructions to enhance performance for specific tasks, languages, or scenarios.

• Qwen3-VL-4B-Thinking-GGUF↗

  redponike