gliclass-small-v1.0
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gliclass-v4-edge
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View details ↗gliclass-x-base
knowledgator基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗gliformer-large-v1
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View details ↗gliner-multitask-large-v0.5
knowledgator基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗gliner-pii-base-v1.0
knowledgatorA production-grade open-source model for privacy-focused PII, PHI, and PCI detection with zero-shot entity recognition capabilities. This model was developed in collaboration between Wordcab and Knowledgator. For enterprise-ready, specialized PII/PHI/PCI models, contact us at info@wordcab.com. GLiNER (Generalist and Lightweight Named Entity Recognition) is a bidirectional transformer model that can identify **any entity type** without predefined categories. Unlike traditional NER models that are limited to specific entity classes, GLiNER allows you to specify exactly what entities you want to extract at runtime. Instead of predicting from a fixed set of entity classes, GLiNER takes both text and a list of desired entity types as input, then identifies spans that match those categories:
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View details ↗gliner-pii-edge-v1.0
knowledgator基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗gliner-pii-large-v1.0
knowledgatorA production-grade open-source model for privacy-focused PII, PHI, and PCI detection with zero-shot entity recognition capabilities. This model was developed in collaboration between Wordcab and Knowledgator. For enterprise-ready, specialized PII/PHI/PCI models, contact us at info@wordcab.com. GLiNER (Generalist and Lightweight Named Entity Recognition) is a bidirectional transformer model that can identify **any entity type** without predefined categories. Unlike traditional NER models that are limited to specific entity classes, GLiNER allows you to specify exactly what entities you want to extract at runtime. - **Zero-shot recognition**: Extract any entity type without retraining - **Privacy-first**: Process sensitive data locally without API calls - **Lightweight**: Much faster than large language models for NER tasks - **Production-ready**: Quantization-aware training with FP16 and UINT8 ONNX models - **Comprehensive**: 60+ predefined PII categories with custom entity support
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View details ↗gliner-pii-small-v1.0
knowledgator基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗gliner-relex-large-v0.5
knowledgator基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗gliner-relex-large-v1.0
knowledgator基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗gliner-x-base
knowledgator基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗gliner-x-large
knowledgator基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗gliner-x-small
knowledgator基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗opir-multitask-large-v1.0
knowledgator基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗opir-multitask-multilang-v1.0
knowledgator基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗SMILES2IUPAC-canonical-base
knowledgator基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗2-bit-LLMs
KnutJaegersbergThis is a model collection of mostly larger LLMs quantized to 2 bit with the novel quip# inspired approach in llama.cpp Sometimes both xs and xxs are available. Note that for some larger models, like Qwen-72b based models, the context length might be too large for most GPUs, so you have to reduce it yourself in textgen-webui via the n_ctx setting. Rope scaling for scaled models like longalpaca or yarn should be 8, set compress_pos_emb accordingly.
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View details ↗CPU-LLM-Horde
KnutJaegersberg基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Nanbeige4.3-33B-Q8_0-GGUF
KnutJaegersberg基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Qwen-1_8B-Llamafied
KnutJaegersberg基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Tri-21B-Think-gguf
KnutJaegersberg基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗exp1_averaged
ko-yoshida基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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