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--- |
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license: apache-2.0 |
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base_model: Qwen/Qwen3-4B |
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tags: |
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- SAT |
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- combinatorial-optimization |
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- classification |
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- cube-and-conquer |
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language: |
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- en |
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pipeline_tag: text-classification |
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--- |
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# Qwen3-4B-SAT-VarSelector |
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A Qwen3-4B model fine-tuned for **SAT branching variable selection** in Cube-and-Conquer (CnC) solvers. |
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## Model Description |
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This model predicts which variable to branch/cube on next, given a SAT CNF formula state. Instead of generating text, it outputs a **classification over variable IDs** (1-500). |
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### Architecture |
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- **Base**: `Qwen/Qwen3-4B` (causal language model) |
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- **Head**: LayerNorm → Linear(hidden_size, 501) |
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- **Pooling**: Last non-pad token hidden state |
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- **Masking**: Invalid variables (not in CNF) are masked to -10000 before softmax |
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### Training |
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- **Dataset**: 3,898 training / 434 validation samples |
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- **Task**: Predict expert-selected branching variable |
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- **Best validation accuracy**: 16.36% (16x better than random ~1%) |
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- **Training**: 8 epochs, 8×H100 GPUs, DeepSpeed ZeRO-3 |
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## Usage |
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```python |
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import torch |
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from transformers import AutoTokenizer |
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from sft_qwen_var_classifier import QwenVarClassifier, cnf_valid_mask |
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# Load model |
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model = QwenVarClassifier("Qwen/Qwen3-4B", max_vars=500) |
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state_dict = torch.load("pytorch_model.bin", map_location="cpu") |
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model.load_state_dict(state_dict, strict=False) |
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model = model.to("cuda", dtype=torch.bfloat16) |
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model.eval() |
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# Load tokenizer |
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tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-4B") |
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# Prepare CNF input |
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cnf_text = """p cnf 100 250 |
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1 -2 3 0 |
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-1 2 -4 0 |
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... |
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""" |
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# Tokenize |
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inputs = tokenizer(cnf_text, return_tensors="pt", truncation=True, max_length=8192) |
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inputs = {k: v.to("cuda") for k, v in inputs.items()} |
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# Get valid variable mask |
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valid_mask = torch.tensor([cnf_valid_mask(cnf_text, max_vars=500)], dtype=torch.bool, device="cuda") |
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# Predict |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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logits = outputs["logits"] |
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logits = logits.masked_fill(~valid_mask, -1e4) |
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predicted_var = logits.argmax(dim=-1).item() |
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print(f"Predicted branching variable: {predicted_var}") |
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``` |
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## Files |
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- `pytorch_model.bin` - Model weights (8GB, bfloat16) |
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- `sft_qwen_var_classifier.py` - Model class definition (required for loading) |
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- `inference_demo.py` - Example inference script |
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## Metrics |
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| Metric | Value | |
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|--------|-------| |
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| Validation Accuracy | 16.36% | |
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| Validation Loss | 3.87 | |
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| Random Baseline | ~1% | |
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| Improvement | 16x | |
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## Limitations |
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- Maximum 500 variables |
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- Maximum 8192 tokens for CNF input |
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- Trained on specific CNF distribution (may not generalize to all SAT instances) |
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## Citation |
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If you use this model, please cite the Transformer-CnC paper. |
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## License |
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Apache 2.0 |
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