|
|
--- |
|
|
license: apache-2.0 |
|
|
base_model: Qwen/Qwen3-0.6B |
|
|
tags: |
|
|
- SAT |
|
|
- combinatorial-optimization |
|
|
- classification |
|
|
- cube-and-conquer |
|
|
language: |
|
|
- en |
|
|
pipeline_tag: text-classification |
|
|
--- |
|
|
|
|
|
# Qwen3-0.6B-SAT-VarSelector |
|
|
|
|
|
A lightweight Qwen3-0.6B model fine-tuned for **SAT branching variable selection** in Cube-and-Conquer (CnC) solvers. |
|
|
|
|
|
## Model Description |
|
|
|
|
|
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). |
|
|
|
|
|
### Architecture |
|
|
|
|
|
- **Base**: `Qwen/Qwen3-0.6B` (causal language model) |
|
|
- **Head**: LayerNorm → Linear(hidden_size, 501) |
|
|
- **Pooling**: Last non-pad token hidden state |
|
|
- **Masking**: Invalid variables (not in CNF) are masked to -10000 before softmax |
|
|
- **Size**: ~1.2GB (bfloat16) |
|
|
|
|
|
### Training |
|
|
|
|
|
- **Dataset**: 3,898 training / 434 validation samples |
|
|
- **Task**: Predict expert-selected branching variable |
|
|
- **Training**: 8 epochs, 8×H100 GPUs, DeepSpeed ZeRO-3 |
|
|
|
|
|
### Comparison with 4B Model |
|
|
|
|
|
| Model | Size | Top-1 Acc | Top-5 Acc | Inference Speed | |
|
|
|-------|------|-----------|-----------|-----------------| |
|
|
| Qwen3-4B | 8GB | 24% | 48% | ~150ms/sample | |
|
|
| **Qwen3-0.6B** | 1.2GB | ~12% | ~32% | ~45ms/sample | |
|
|
|
|
|
## Usage |
|
|
|
|
|
```python |
|
|
import torch |
|
|
from transformers import AutoTokenizer |
|
|
from sft_qwen_var_classifier import QwenVarClassifier, cnf_valid_mask |
|
|
|
|
|
# Load model |
|
|
model = QwenVarClassifier("Qwen/Qwen3-0.6B", max_vars=500) |
|
|
state_dict = torch.load("pytorch_model.bin", map_location="cpu") |
|
|
model.load_state_dict(state_dict, strict=False) |
|
|
model = model.to("cuda", dtype=torch.bfloat16) |
|
|
model.eval() |
|
|
|
|
|
# Load tokenizer |
|
|
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-0.6B") |
|
|
|
|
|
# Prepare CNF input |
|
|
cnf_text = """p cnf 100 250 |
|
|
1 -2 3 0 |
|
|
-1 2 -4 0 |
|
|
... |
|
|
""" |
|
|
|
|
|
# Tokenize |
|
|
inputs = tokenizer(cnf_text, return_tensors="pt", truncation=True, max_length=8192) |
|
|
inputs = {k: v.to("cuda") for k, v in inputs.items()} |
|
|
|
|
|
# Get valid variable mask |
|
|
valid_mask = torch.tensor([cnf_valid_mask(cnf_text, max_vars=500)], dtype=torch.bool, device="cuda") |
|
|
|
|
|
# Predict |
|
|
with torch.no_grad(): |
|
|
outputs = model(**inputs) |
|
|
logits = outputs["logits"] |
|
|
logits = logits.masked_fill(~valid_mask, -1e4) |
|
|
predicted_var = logits.argmax(dim=-1).item() |
|
|
|
|
|
print(f"Predicted branching variable: {predicted_var}") |
|
|
``` |
|
|
|
|
|
## Files |
|
|
|
|
|
- `pytorch_model.bin` - Model weights (~1.2GB, bfloat16) |
|
|
- `sft_qwen_var_classifier.py` - Model class definition (required for loading) |
|
|
|
|
|
## When to Use |
|
|
|
|
|
- **Production/Deployment**: Faster inference, smaller memory footprint |
|
|
- **Edge devices**: Can run on smaller GPUs |
|
|
- **Rapid prototyping**: Quick experiments |
|
|
- **CPU inference**: More practical than 4B model |
|
|
|
|
|
For maximum accuracy, use the [4B model](https://huggingface.co/Yale-ROSE/Qwen3-4B-SAT-VarSelector). |
|
|
|
|
|
## Limitations |
|
|
|
|
|
- Maximum 500 variables |
|
|
- Maximum 8192 tokens for CNF input |
|
|
- Lower accuracy than 4B model |
|
|
- Trained on specific CNF distribution |
|
|
|
|
|
## Citation |
|
|
|
|
|
If you use this model, please cite the Transformer-CnC paper. |
|
|
|
|
|
## License |
|
|
|
|
|
Apache 2.0 |
|
|
|
