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As of Oct 10, NandhaKishorM released vegaml on GitHub and PyPI: typed decisions from a frozen language-model backbone plus a small physics engine, returning choice/score/boolean answers with calibrated probabilities, conformal sets, and abstain—without generating text.
- Official
vegaml open-sources Vega, an 800M/4B physics-based typed decision model with 73k context and images
GitHub @NandhaKishorM/vegaml published this on Oct 10, 2026 (source: github.com).
NandhaKishorM released vegaml on GitHub and PyPI: typed decisions from a frozen language-model backbone plus a small physics engine, returning choice/score/boolean answers with calibrated probabilities, conformal sets, and abstain—without generating text. Defaults are an 800M (Qwen3.5-0.8B frozen) build with a 14.3M engine; a 4B option is in the same checkpoint repo. Context is 73,728 tokens with read-once prefix caching, and an image path is supported. The README cites LocalLLaMA/typed-decisions test-split figures (for example soft accuracy 0.680 on 0.8B and 0.725 on 4B) and says inference on a single T4 is demonstrated in Colab.
Source: GitHub @NandhaKishorM/vegaml
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