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Decider

Decider is a family of System One-style models fine-tuned from Qwen3.5. It is designed for making one-pass typed decisions with calibrated probabilities, providing a streamlined decision-making process.

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Decider is a project hosted on GitHub, offering a family of System One-style models that have been fine-tuned from Qwen3.5. These models are specifically designed to facilitate one-pass typed decisions, ensuring that each decision is accompanied by calibrated probabilities. This approach aims to streamline the decision-making process, making it more efficient and reliable.

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The models are particularly useful in scenarios where quick and accurate decision-making is crucial. By providing calibrated probabilities, Decider helps users understand the confidence level of each decision, which can be critical in fields such as finance, healthcare, and logistics.

Decider is open-source, allowing developers and researchers to contribute to its development and improvement. The project is hosted on GitHub, making it accessible to a wide audience interested in AI and machine learning models. Users can clone the repository, install dependencies, and configure the models according to their specific needs.

The primary language used in the Decider project is Python, which is widely used in the AI and machine learning community. This choice of language ensures that the project is accessible to a large number of developers and researchers who are familiar with Python.

Overall, Decider offers a robust solution for those looking to implement AI-driven decision-making processes in their applications. Its open-source nature and focus on calibrated probabilities make it a valuable tool for a variety of industries.

  • CategoryAI Models & LLMs
  • Pricing ModelOpen source
  • DeploymentCloud, Self-hosted
  • PlatformsWeb

  • System One-style models

  • Fine-tuned from Qwen3.5

  • One-pass typed decisions

  • Calibrated probabilities

  • Open-source

  • Python language

  • GitHub hosted

  • Community contributions

  1. Clone: Download the repository from GitHub

  2. Install dependencies: Set up the required libraries

  3. Configure: Adjust settings for specific use cases

  4. Execute: Run the models for decision-making

  • Financial Analysis
  • Healthcare Diagnostics
  • Logistics Optimization
  • Risk Assessment
  • Research and Development

Decider Reviews

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