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README.md

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----
-license: mit
----
+---
+license: mit
+language:
+- en
+base_model:
+- unsloth/gpt-oss-20b-BF16
+tags:
+- optimization
+- operations-research
+- milp
+- gurobi
+- sft
+- transformers
+---
+
+# 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](https://huggingface.co/datasets/CardinalOperations/OR-Instruct-Data-3K) and [OptMATH-Train](https://huggingface.co/datasets/Aurora-Gem/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](https://huggingface.co/unsloth/gpt-oss-20b-BF16) \
+**Additional Assets:** [GitHub Repository](https://github.com/microsoft/OptiGuide) 
+
+
+# Usage
+
+## Sample Useage
+
+OptiMind-SFT is best served with **SGLang**. we use SGLang’s OpenAI-compatible API together with the official openai Python client:
+
+```
+pip install "sglang[all]" openai gurobipy
+
+# Make sure you have a valid Gurobi license and PYTHON>=3.12
+python -m sglang.launch_server \
+  --model-path microsoft/OptiMind-SFT \
+  --host 0.0.0.0 \
+  --port 30000 \
+  --tensor-parallel-size 1 \
+  --trust-remote-code
+```
+
+Below is the sample code to query the model:
+```
+from openai import OpenAI
+
+# SGLang exposes an OpenAI-compatible endpoint
+client = OpenAI(
+    base_url="http://localhost:30000/v1",
+    api_key="EMPTY"  # Not used by local SGLang, but required by the client
+)
+
+system_prompt = """You are an expert in optimization and mixed integer programming. You are given an
+optimization problem and you need to solve it using gurobipy.
+Reason step by step before generating the gurobipy code.
+When you respond, first think carefully.
+After thinking, output the math modeling of the problem.
+Finally output a ```python ...``` code block that solves the problem.
+The code must include:
+import gurobipy as gp
+from gurobipy import GRB
+"""
+
+user_problem = "A factory produces products A and B with capacity and demand constraints ..."
+
+response = client.chat.completions.create(
+    model="microsoft/OptiMind-SFT",
+    messages=[
+        {"role": "system", "content": system_prompt},
+        {"role": "user", "content": user_problem},
+    ],
+    temperature=0.9,   # recommended default
+    top_p=1.0,         # recommended default
+    max_tokens=4096,
+)
+
+print(response.choices[0].message.content)
+```
+
+This will return a response that first describes the mathematical model and then includes a python code block implementing it in gurobipy.
+
+
+## Primary Use Cases
+
+- Translating natural-language Operations Research (OR) problems into mixed-integer linear programs (MILPs) and corresponding `gurobipy` code for research and prototyping.
+- Studying and benchmarking NL to MILP modeling pipelines on public OR datasets such as IndustryOR, Mamo-Complex, and OptMATH.
+- Educational use for teaching how to derive optimization models (variables, constraints, objectives) from informal problem descriptions.
+- Performing ablations and research on solver-in-the-loop prompting and multi-turn correction in domain-specific modeling tasks.
+
+## Out-of-Scope Use Cases
+
+- General-purpose chat, open-domain reasoning, or tasks unrelated to optimization modeling.
+- Safety-critical or regulated applications (e.g., healthcare, finance, legal decisions, credit scoring) without expert human review of both the model output and the resulting optimization.
+- Fully automated deployment where optimization results are used directly for real-world decisions without human oversight.
+- Automatic execution of generated code in production systems without sandboxing, logging, and appropriate security controls.
+
+
+## Technical Requirements & Integration
+
+We recommend **≥32GB GPU VRAM** (e.g., A100/H100/B200) for comfortable inference, especially for long prompts and multi-turn interactions. 
+Please checkout our [GitHub page](https://github.com/microsoft/OptiGuide) for instructions on the inference pipeline.
+
+# Data Overview
+## Training and Validation Data
+We fine-tune OptiMind-SFT on cleaned versions of the OR-Instruct and OptMATH training sets, and validate on a held-out validation split drawn from the same cleaned corpora.
+
+## Testing Data
+For testing, we use manually cleaned and expert-validated versions of the IndustryOR, Mamo-Complex, and OptMATH benchmarks. Please visit our [GitHub page](https://github.com/microsoft/OptiGuide) to download the cleaned benchmarks.
+
+# Known Technical Limitations
+
+- The model can still produce incorrect formulations or invalid code, or declare feasibility/optimality incorrectly.   
+- It is specialized to OR benchmarks; behavior on general text or other problem domains is not guaranteed.
+- No dedicated red-teaming against unsafe content categories (e.g., hate, violence, self-harm) or jailbreak attacks has been performed; the paper focuses on technical robustness metrics. 
+
+Users **must** keep a human in the loop for all consequential decisions and carefully review any generated code before execution.
+
+# Other Sources & Maintenance
+- Evaluation code and cleaned benchmarks: [GitHub page](https://github.com/microsoft/OptiGuide)
+- Paper: [Arxiv link](https://arxiv.org/abs/2509.22979)
+For questions, issues, or feature requests, please use the GitHub issue tracker or the Hugging Face “Community” tab.
+
+# Citation
+If you use OptiMind-SFT or the associated datasets/benchmarks in your work, please cite:
+
+```
+@article{chen2025optimind,
+  title={OptiMind: Teaching LLMs to Think Like Optimization Experts},
+  author={Chen, Zeyi and Zhang, Xinzhi and Zope, Humishka and Barbalho, Hugo and Mellou, Konstantina and Molinaro, Marco and Kulkarni, Janardhan and Menache, Ishai and Li, Sirui},
+  journal={arXiv preprint arXiv:2509.22979},
+  year={2025}
+}
+```