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nj-housing-qlora-onnx
rajkumar4466/nj-housing-qlora-onnx
nj-housing-qlora-onnx is a machine learning model from rajkumar4466. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for onnxruntime.
Qwen2.5-0.5B fine-tuned with QLoRA (4-bit NF4) on NJ housing data, then merged and exported to ONNX format for CPU inference.
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At a glance
- Library
- onnxruntime
- Model type
- qwen2
- Access
- Public
- Created
- Mar 2, 2026
- Updated
- Mar 2, 2026
- SHA
- 77187065
Datasets
Model card
From the Hugging Face model README
QLoRA Fine-tuned Qwen2.5-0.5B (ONNX) — NJ Housing Price Prediction
Qwen2.5-0.5B fine-tuned with QLoRA (4-bit NF4) on NJ housing data, then merged and exported to ONNX format for CPU inference.
Metrics (held-out test set)
| Metric | QLoRA (this model) | XGBoost Baseline |
|---|---|---|
| MAE | $140,141 | $128,013 |
| RMSE | $190,172 | $168,135 |
| R² | 0.6359 | 0.7154 |
| MAPE | 23.0% | 22.7% |
Model Details
- Base model: Qwen/Qwen2.5-0.5B
- Fine-tuning: QLoRA (rank=16, alpha=32, 4-bit NF4 quantization)
- Export: Merged to fp32, exported to ONNX
- Inference: ONNX Runtime (CPU), no GPU required
Prompt Format
Property: Single Family in zip 07650. 3 bedrooms, 2.0 bathrooms, 1500 sqft living area, 0.25 acre lot, built in 1990. Predicted price: $
The model generates the price value after the $ prefix.
Usage
import onnxruntime as ort
from transformers import AutoTokenizer
from huggingface_hub import snapshot_download
path = snapshot_download("rajkumar4466/nj-housing-qlora-onnx")
session = ort.InferenceSession(f"{path}/model.onnx")
tokenizer = AutoTokenizer.from_pretrained(path)
Dataset
Trained on rajkumar4466/nj-housing-prices
Comparison
See rajkumar4466/nj-housing-xgboost-baseline for the XGBoost baseline comparison.
- Library
- onnxruntime
- Type
- qwen2
- Created
- Mar 2, 2026
- Updated
- Mar 2, 2026