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AI 모델 84759
- Nemotron-Cascade-8B-Intermediate-ckpts↗
NVIDIA
- Nemotron 3 Ultra 550B A55B (Reasoning)↗
NVIDIA
- prompt-task-and-complexity-classifier↗
NVIDIA
This is a multi-headed model which classifies English text prompts across task types and complexity dimensions. Tasks are classified across 11 common categories. Complexity is evaluated across 6 dimensions and ensembled to create an overall complexity score. Further information on the taxonomies can be found below. **Task types:** * Open QA: A question where the response is based on general knowledge * Closed QA: A question where the response is based on text/data provided with the prompt * Summarization * Text Generation * Code Generation * Chatbot * Classification * Rewrite * Brainstorming * Extraction * Other **Complexity dimensions:** * Overall Complexity Score: The weighted sum of the complexity dimensions. Calculated as 0.35\*CreativityScore + 0.25\*ReasoningScore + 0.15\*ConstraintScore + 0.15\*DomainKnowledgeScore + 0.05\*ContextualKnowledgeScore + 0.05\*NumberOfFewShots * Creativity: The level of creativity needed to respond to a prompt. Score range of 0-1, with a higher score indicating more creativity. * Reasoning: The extent of logical or cognitive effort required to respond to a prompt. Score range of 0-1, with a higher score indicating more reasoning * Contextual Knowledge: The background information necessary to respond to a prompt. Score range of 0-1, with a higher score indicating more contextual knowledge required outside of prompt. * Domain Knowledge: The amount of specialized knowledge or expertise within a specific subject area needed to respond to a prompt. Score range of 0-1, with a higher score indicating more domain knowledge is required. * Constraints: The number of constraints or conditions provided with the prompt. Score range of 0-1, with a higher score indicating more constraints in the prompt. * Number of Few Shots: The number of examples provided with the prompt. Score range of 0-n, with a higher score indicating more examples provided in the prompt.
- stt_rw_conformer_transducer_large↗
NVIDIA
- Qwen3-4B-OBLITERATED↗
OBLITERATUS
- NeoHorse-1-4B-Uncensored-Zero-Refusal-GGUF↗
OpenIntelligenceNet
- OpenMed-NER-BloodCancerDetect-BioMed-335M↗
OpenMed
**Specialized model for Clinical Entity Recognition - Clinical entities related to Chronic Lymphocytic Leukemia** This model is a **state-of-the-art** fine-tuned transformer engineered to deliver **enterprise-grade accuracy** for clinical entity recognition - clinical entities related to chronic lymphocytic leukemia. 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 CLL dataset - **Production-Ready**: Validated on clinical benchmarks - **Easy Integration**: Compatible with Hugging Face Transformers ecosystem
- My-Name-is-Riemann-Prerelease↗
OpenPeerAI
- Qwen2.5-7B-Instruct-converge-collective-v1↗
Optitransfer
- gpt-5.6-sol↗
oroboros-labs