A 7B model fine-tuned to formulate and solve operations research problems (LP, IP, network flow, queueing, stochastic optimization).
Built on Qwen2.5-Math-7B with a two-stage training pipeline: supervised fine-tuning (SFT) followed by Direct Preference Optimization (DPO). Distributed as a LoRA adapter (~646 MB).
Intended use
Formulating optimization problems from natural-language descriptions
Solving small LPs, IPs, shortest-path, max-flow, knapsack, and similar OR problems end-to-end
Educational support for OR / optimization students
Not a replacement for a real solver. Use this to set up the problem; verify with Gurobi, CPLEX, OR-Tools, or SciPy.
Evaluation
Evaluated on a held-out set of 60 OR problems across three categories: closed_form (n=20, auto-graded), open_conceptual (n=20, qualitative), long_form (n=20, qualitative).
Closed-form numeric accuracy (final-answer grader, last 5 lines of completion only):
Model
Closed-form score
SFT-only
48.3%
SFT + DPO (this model)
55.4%
DPO gives a +7.1 percentage point absolute improvement (~15% relative). Across 20 closed-form items, DPO improved 7, regressed 5, tied 8.
Example: A01 (LP formulation)
Prompt: "A factory produces A and B. A needs 2h labor, 3 units material, profit $5. B needs 1h labor, 2 units material, profit $4. Available: 100h labor, 200 units material. Maximize profit."
Expected: x_A=40, x_B=20, profit=280
SFT (incorrect): "the optimal solution is x_A = 20 and x_B = 60, which gives a total profit of $340"
DPO (correct): "the maximum profit occurs at the point (x_A, x_B) = (40, 20), yielding a total profit of $280"
How to use
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
base = AutoModelForCausalLM.from_pretrained(
"Qwen/Qwen2.5-Math-7B",
torch_dtype=torch.bfloat16,
device_map="auto",
trust_remote_code=True,
)
model = PeftModel.from_pretrained(base, "billwang37/WWang-Lab-OptimAI")
tokenizer = AutoTokenizer.from_pretrained("billwang37/WWang-Lab-OptimAI", trust_remote_code=True)
prompt = "Maximize 3x + 5y subject to x + 2y <= 14, 3x - y >= 0, x - y <= 2, x,y >= 0."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=1024, do_sample=False)
print(tokenizer.decode(out[0], skip_special_tokens=True))
Training details
Base model: Qwen/Qwen2.5-Math-7B
SFT data: ~6k OR problems with worked solutions
DPO data: ~1.2k preference pairs (chosen vs rejected)
Method: LoRA adapter via PEFT + TRL
Hardware: Single A100 (40 GB) on OU OSCER cluster
SFT runtime: ~4 hours (3 epochs)
DPO runtime: ~30 minutes
Limitations
60-problem eval set is small; numbers are a directional signal, not a definitive benchmark
Grader checks numbers, not reasoning
Can produce confidently wrong answers, especially on integer programming and combinatorial graph problems
LoRA adapter format; you must load Qwen2.5-Math-7B base separately
