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How to run a small open decision model locally with Awesome Decision Models

Pull a small open-weight decision model into Ollama and score your own options on your own machine.

Who it is forA developer who wants to keep input data on a local machine and avoid per-request API costs while testing decision questions.

The problemHosted decision APIs send your state text to a provider, and you may need a local option for privacy or offline testing.

The outcomeA local model that answers Choice, Score, or Noul questions and returns probabilities, served from your own Ollama install.

What you need first

  • A web browser to open the repo's README and the linked provider docs
  • An account and API key for the hosted provider you choose (for example TypeSafe console, OpenRouter, or Microsoft Foundry)
  • For local runs: Ollama installed, or a Python environment with the package named in the model's own page
  • A list of your own options (labels, tool names, or rubric levels) to score

Steps

  1. Open the Open Models section and pick a model that the README says runs in Ollama, such as Nimble (listed as nimble) or Tev1 (listed as tev1).
  2. Pull the model with ollama pull nimble after checking the model name in the README. (inferred, check the README)
  3. Read the model card on Hugging Face or GitHub for the expected request format and any license terms before using it.
  4. Send a state and your options to the local model and compare its probabilities against a few cases you already know the answer to.

Prompt for your agent

Using the Open Models section of github.com/AnotiaWang/awesome-decision-models, pick one model that the README lists as available in Ollama and run it locally on the sample state in <sample-file> with the options in <options-file>. Expected output: one probability per option. Verify the license of the chosen model in its model card before you use the output in a product.

Caveat. Scores in the Open Models section are as reported by each project on the benchmark it chose, so they are not comparable across entries and the local model may behave differently on your data.

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