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AI 모델 84759

• MiniCPM-V-4.7-35B-A3B-DSpark↗

  openbmb

  `MiniCPM-V-4.7-35B-A3B-DSpark` is a DSpark draft checkpoint trained for exact pairing with MiniCPM-V-4.7-35B-A3B and its tokenizer. The llama.cpp version please see: MiniCPM-V-4.7-35B-A3B-DSpark-gguf Training responses were generated by MiniCPM-V-4.7-35B-A3B from a mixture of Vision & Text prompts.

• InternViT-6B-448px-V1-0↗

  OpenGVLab

• OpenMed-NER-PathologyDetect-EuroMed-212M↗

  OpenMed

  **Specialized model for Disease Entity Recognition - Disease entities from the NCBI dataset** This model is a **state-of-the-art** fine-tuned transformer engineered to deliver **enterprise-grade accuracy** for disease entity recognition - disease entities from the ncbi 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 NCBI_DISEASE dataset - **Production-Ready**: Validated on clinical benchmarks - **Easy Integration**: Compatible with Hugging Face Transformers ecosystem

• tiny-random-smolvlm2↗

  optimum-intel-internal-testing

• tiny-random-stable-diffusion-with-safety-checker↗

  optimum-intel-internal-testing

  This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

• Ornith-1.5-122B-GGUF↗

  ornith-ai

  Chirp Chirp! 🐦 We are introducing Ornith-1.5, a major step toward building foundation models through end-to-end self-improvement. Ornith-1.5 extends Ornith-1.0 (which was developed on top of Qwen3.5 and Gemma4 with additional continued pretraining, mid-training, and post-training) by expanding the self-improvement loop from scaffold and rollout optimization to jointly optimizing task generation, scaffold construction, and solution rollouts. Rather than relying on a fixed set of human-curated tasks and manually designed harnesses, Ornith-1.5 continuously generates new training tasks, discovers effective strategies for solving them, and improves the policy through reinforcement learning. For more details on the task, harness, and rollout reward design, please refer to our blog. This model card documents **Ornith-1.5-122B**, the most capable and efficient.member of the Ornith-1.5 family — a 122B MOE model designed for efficient single-GPU deployment, and edge-deployable on mobile devices via its quantized Ornith-1.5-122B-Mobile variant.

• linne-oak-1-0↗

  PNPM244

• Reasoning-Llama-1b-v0.1-GGUF↗

  QuantFactory

• Ornith-1.5-122B-Uncensored-GGUF↗

  rax4901

• AlyGreo_-_finetune-Qwen2-0.5B-codeDataset-gguf↗

  RichardErkhov