billwang37
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WWang-Lab-OptimAI

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 ---
 base_model: Qwen/Qwen2.5-Math-7B
 library_name: peft
 pipeline_tag: text-generation
-tags:
-- base_model:adapter:/scratch/billwang37/optim_ai/models/Qwen2.5-Math-7B
-- dpo
-- lora
-- transformers
-- trl
 ---
-# Model Card for Model ID
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-## Model Details
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-### Framework versions
-- PEFT 0.17.1

 ---
+license: apache-2.0
 base_model: Qwen/Qwen2.5-Math-7B
+tags:
+  - operations-research
+  - optimization
+  - linear-programming
+  - integer-programming
+  - lora
+  - dpo
+  - peft
+language:
+  - en
 library_name: peft
 pipeline_tag: text-generation
 ---
+# OptimAI
+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
+- Studying optimality conditions (KKT, dual formulations, sensitivity analysis)
+- 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
+- English-only
+## License
+Apache-2.0, inherited from the base model.