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Ling-3.0-flash-Sante

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LLMsAI Models & LLMsOpen source · Web

Ling-3.0-flash-Sante is a healthcare-specialized model built on Ling-3.0-flash, designed for medical reasoning and healthcare research. It features 124B total parameters and 5.1B active parameters per token, supporting long-document analysis and multi-agent collaboration.

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Description

Ling-3.0-flash-Sante is a specialized model developed for the healthcare sector, built on the foundation of Ling-3.0-flash. It is designed to assist in medical reasoning, healthcare research, and tool-assisted workflows while maintaining the general capabilities of its predecessor. The model boasts 124 billion total parameters and activates 5.1 billion parameters per token, making it efficient for complex tasks.

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The model is particularly adept at handling professional healthcare tasks, complex medical reasoning, and safety benchmarks. Its architecture includes hybrid-linear MoE with Kimi Delta Attention, gated MLA, and sparse MoE layers, which contribute to its efficiency at scale. Ling-3.0-flash-Sante is also built for long contexts, with training extending to 256K tokens, enabling it to support long-document analysis and evidence synthesis.

Despite its capabilities, Ling-3.0-flash-Sante is a research model and not a medical device. Its outputs may be incomplete or incorrect, and it should not be used as a substitute for professional medical advice, diagnosis, or treatment. High-stakes use requires qualified human review and compliance with applicable regulations.

The model is evaluated across various benchmarks, including AFUMED-Drug for medication safety and MedMCQA for medical question answering. It supports tool-assisted research and multi-agent healthcare workflows, making it suitable for deep research and development scenarios. However, users must ensure the protection of personal health information and comply with privacy and security requirements.

  • CategoryAI Models & LLMs
  • Pricing ModelOpen source
  • DeploymentCloud
  • PlatformsWeb

Ling-3.0-flash-Sante Highlights

  • Healthcare-specialized model

  • 124B total parameters

  • 5.1B active parameters per token

  • Hybrid-linear MoE architecture

  • 256K token context window

  • Tool-assisted workflows

  • Multi-agent collaboration

  • Medical reasoning and research

Getting Started with Ling-3.0-flash-Sante

  1. Access model page: Visit Hugging Face

  2. Authenticate: Log in to your account

  3. Configure: Set parameters and context

  4. Send prompts: Use the API for queries

  5. Fine-tune: Adjust model for specific tasks

Ling-3.0-flash-Sante's Use Cases

  • Medical QA
  • Literature Search
  • Long-Document Analysis
  • Healthcare Workflows
  • AI Agent Development

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