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AI Models 84759

• ThinkEdit-deepseek-qwen-14b-i1-GGUF↗

  mradermacher

• bert-mini-finetuned-age_news-classification↗

  mrm8488

• flux-2-klein-4B-360-erp-outpaint-lora↗

  nomadoor

• NorwAI-Mistral-7B-instruct↗

  NorwAI

• GLM-5.3-NVFP4↗

  NVIDIA

  The NVIDIA GLM-5.3 NVFP4 model is the quantized version of ZAI's GLM-5.3 model, which is an auto-regressive language model that uses an optimized transformer architecture. GLM-5.3 is a Mixture-of-Experts (MoE) model for reasoning, coding, and agentic tasks; it uses the same base model as GLM-5.2, with gains from post-training, and uses sparse attention (with an IndexShare indexer) to support a long context. For more information, please check here. The NVIDIA GLM-5.3 NVFP4 model is quantized with Model Optimizer. This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party's requirements for this application and use case; see link to Non-NVIDIA (GLM-5.3) Model Card from ZAI. **License type:** NVIDIA Proprietary **Governing Download Terms:** Use of this model is governed by NVIDIA Open Model Agreement. **Additional Information:** GLM-5.3 License. Third-Party Notice Text File: The original GLM-5.3 model are licensed under the GLM-5.3 License, included in this repository. The NVIDIA Open Model Agreement does not modify or supersede the GLM-5.3 License or its conditions.

• Llama3-ChatQA-1.5-8B↗

  NVIDIA

  We introduce Llama3-ChatQA-1.5, which excels at conversational question answering (QA) and retrieval-augmented generation (RAG). Llama3-ChatQA-1.5 is developed using an improved training recipe from ChatQA paper, and it is built on top of Llama-3 base model. Specifically, we incorporate more conversational QA data to enhance its tabular and arithmetic calculation capability. Llama3-ChatQA-1.5 has two variants: Llama3-ChatQA-1.5-8B and Llama3-ChatQA-1.5-70B. Both models were originally trained using Megatron-LM, we converted the checkpoints to Hugging Face format. **For more information about ChatQA, check the website!** Llama3-ChatQA-1.5-70B   Evaluation Data   Training Data   Retriever   Website   Paper Note that ChatQA-1.5 is built based on Llama-3 base model, and ChatQA-1.0 is built based on Llama-2 base model. ChatQA-1.5 models use HybriDial training dataset. To ensure fair comparison, we also compare average scores excluding HybriDial. The data and evaluation scripts for ChatRAG Bench can be found here.

• MiniMax-M3-NVFP4↗

  NVIDIA

  MiniMax-M3 is a multimodal model with frontier-level coding and agentic capabilities, built on a Mixture-of-Experts architecture with a 1M-token context window. The model processes text, image, video, and computer use inputs and produces text outputs, with emphasis on long-horizon coding tasks, agentic and tool-use workflows, and long-form video understanding. The NVIDIA MiniMax-M3 NVFP4 model is quantized with Model Optimizer. This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party's requirements for this application and use case; see link to Non-NVIDIA MiniMax-M3 Model Card. **GOVERNING TERMS:** Use of the checkpoints is governed by the MiniMax Community License. **Additional Information:** Built with MiniMax M3.

• MiniCPM-Embedding-Light↗

  openbmb

• Mini-InternVL2-4B-DA-DriveLM↗

  OpenGVLab

• OpenMed-NER-DNADetect-BioMed-335M↗

  OpenMed

  **Specialized model for Biomedical Entity Recognition - Proteins, DNA, RNA, cell lines, and cell types** This model is a **state-of-the-art** fine-tuned transformer engineered to deliver **enterprise-grade accuracy** for biomedical entity recognition - proteins, dna, rna, cell lines, and cell types. 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 JNLPBA dataset - **Production-Ready**: Validated on clinical benchmarks - **Easy Integration**: Compatible with Hugging Face Transformers ecosystem