Model Overview OptiMind SFT is a specialized 20B parameter model designed to bridge the gap between natural language and executable optimization solvers. It automates the translation of complex decision making problems—such as supply chain planning, scheduling, and resource allocation—into correct MILP formulations. Model Summary Developer: Microsoft Research, Machine Learning and Optimization (MLO) Group \ Model Architecture: Mixture of Experts (MoE) variant of the transformer architecture (gpt oss family). \ Parameters: 20 Billion (3.6B activated) \ Inputs: Natural language optimization problem description. \ Context Length: 128,000 tokens \ Outputs: Mathematical formulation and executable Python code using GurobiPy. \ GPUs: 8x NVIDIA B200 (Training), 8x NVIDIA H100 (Inference/Evaluation) \ Training Time: ~8 hours \ Public Data Summary: Cleaned subsets of OR Instruct and OptMATH Train \ Dates: Trained in October 2025 \ Status: Static model trained on cleaned public datasets \ Release Date: November 2025 \ License: MIT \ Model Dependencies: unsloth/gpt oss 20b BF16 \ Additional Assets: GitHub Repository Usage Sample Useage OptiMind SFT is best served with SGLang . we use SGLang’s…
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