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StartLux-Decision

StartLux-Decision offers open decision models ranging from 0.8B to 27B parameters, providing calibrated probabilities for each option. It generates concise thoughts when uncertain, serving as an engine for vLLM, llama.cpp, and MLX.

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StartLux-Decision is a sophisticated tool designed to offer open decision models with parameters ranging from 0.8 billion to 27 billion. This tool is engineered to provide a calibrated probability for every option in a single pass, ensuring that users receive precise and reliable outputs. When the system encounters uncertainty, it generates a short thought to guide users further.

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The tool serves as an engine for vLLM, llama.cpp, and MLX, making it versatile and adaptable for various applications. StartLux-Decision is particularly useful for developers and researchers who require robust decision-making models that can handle complex scenarios with ease.

The repository is actively maintained, with the latest commit indicating the introduction of AutoThink models and enhancements to the inference engine. This ensures that users have access to the latest features and improvements, enhancing the tool's reliability and performance.

StartLux-Decision is open-source, allowing users to contribute to its development and customize it according to their specific needs. The tool is ideal for those in the AI research and development sectors, providing a reliable foundation for building advanced decision-making systems.

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

  • Open decision models from 0.8B to 27B parameters

  • Calibrated probability for each option

  • Generates short thoughts when unsure

  • Engine for vLLM, llama.cpp, and MLX

  • Active development with recent updates

  • Open-source for customization

  • Supports complex decision-making scenarios

  • Ideal for AI research and development

  1. Clone: Download the repository from GitHub

  2. Install dependencies: Set up the required libraries

  3. Configure: Adjust settings for your specific use case

  4. Execute: Run the models to generate decisions

  5. Optimize: Fine-tune the models for better performance

  • AI Research
  • Complex Decision-Making
  • Model Customization
  • Inference Engine
  • Development

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