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AI モデル 84757

• comic-text-and-bubble-detector↗

  ogkalu

  RT-DETR-v2 r50vd model fine-tuned on about 11k Manga, Webtoon, Manhua and Western Comic style Images for text and speech bubble detection. Training Image Size = 640. Training Images were resized, not cropped. Tall Webtoons were split vertically. Classes are: 0: bubble 1: text_bubble (text inside bubbles) 2: text_free (text outside bubbles)

• OneJev-0.8B↗

  OmniJev

• chatterbox-multilingual-ONNX↗

  onnx-community

• OpenMed-NER-OrganismDetect-BioMed-109M↗

  OpenMed

  **Specialized model for Species Entity Recognition - Species names from the Species-800 dataset** This model is a **state-of-the-art** fine-tuned transformer engineered to deliver **enterprise-grade accuracy** for species entity recognition - species names from the species-800 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 SPECIES800 dataset - **Production-Ready**: Validated on clinical benchmarks - **Easy Integration**: Compatible with Hugging Face Transformers ecosystem

• PP-OCRv3_mobile_rec↗

  PaddlePaddle

• financial-roberta-large-macro-sentiment↗

  peyterho

• Baguettotron-GGUF↗

  PleIAs

• ghost-8b-beta-GGUF↗

  QuantFactory

• Wan2.2-TI2V-5B-GGUF↗

  QuantStack

  This GGUF file is a direct conversion of Wan-AI/Wan2.2-TI2V-5B Since this is a quantized model, all original licensing terms and usage restrictions remain in effect. The model can be used with the ComfyUI custom node ComfyUI-GGUF by city96

• Qwen2.5-Coder-1.5B-Instruct-GGUF↗

  阿里巴巴 · Qwen

  Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). As of now, Qwen2.5-Coder has covered six mainstream model sizes, 0.5, 1.5, 3, 7, 14, 32 billion parameters, to meet the needs of different developers. Qwen2.5-Coder brings the following improvements upon CodeQwen1.5: - Significantly improvements in **code generation**, **code reasoning** and **code fixing**. Base on the strong Qwen2.5, we scale up the training tokens into 5.5 trillion including source code, text-code grounding, Synthetic data, etc. Qwen2.5-Coder-32B has become the current state-of-the-art open-source codeLLM, with its coding abilities matching those of GPT-4o. - A more comprehensive foundation for real-world applications such as **Code Agents**. Not only enhancing coding capabilities but also maintaining its strengths in mathematics and general competencies. **This repo contains the instruction-tuned 1.5B Qwen2.5-Coder model in the GGUF Format**, which has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Architecture: transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias and tied word embeddings - Number of Parameters: 1.54B - Number of Paramaters (Non-Embedding): 1.31B - Number of Layers: 28 - Number of Attention Heads (GQA): 12 for Q and 2 for KV - Context Length: Full 32,768 tokens - Note: Currently, only vLLM supports YARN for length extrapolating. If you want to process sequences up to 131,072 tokens, please refer to non-GGUF models. - Quantization: q2_K, q3_K_M, q4_0, q4_K_M, q5_0, q5_K_M, q6_K, q8_0