diff --git a/README.md b/README.md
index 41c48098915389597067dccba0c3ddb02c608155..a17e0fec0805b8876cd108ca3c1269eb8f057241 100644
--- a/README.md
+++ b/README.md
@@ -14,3 +14,8 @@ up a working environment. If you're using conda, do
 conda env create -n predtuner -f env.yaml
 conda activate predtuner
 ```
+
+## Tuning with HPVM Binary
+
+This branch (`hpvm`) contains beta support for HPVM binaries.
+Please refer to `examples/tune_hpvm_bin.py` for an example with explanations.
diff --git a/examples/tune_hpvm_bin.py b/examples/tune_hpvm_bin.py
new file mode 100644
index 0000000000000000000000000000000000000000..8dfffc7331a6129df69879ef991b6c246094b0a3
--- /dev/null
+++ b/examples/tune_hpvm_bin.py
@@ -0,0 +1,36 @@
+import site
+from pathlib import Path
+
+import torch
+from torch.utils.data.dataloader import DataLoader
+from torch.utils.data.dataset import Subset
+
+site.addsitedir(Path(__file__).parent.parent.absolute().as_posix())
+from predtuner import PipedBinaryApp, config_pylogger
+from predtuner.model_zoo import CIFAR, VGG16Cifar10
+
+# Set up logger to put log file in /tmp
+msg_logger = config_pylogger(output_dir="/tmp", verbose=True)
+
+# TODO: fill in these 2 paths with path to binary file and path to json file, respectively:
+binary_file, metadata_file = "", ""
+# Create a `PipedBinaryApp` that communicates with HPVM bin.
+# "TestHPVMApp" is an identifier of this app (used in logging, etc.) and can be anything.
+# Other arguments: 
+#   base_dir: which directory to run binary in (default: the dir the binary is in)
+#   qos_relpath: the name of accuracy file generated by the binary.
+#     Defaults to "final_accuracy". For HPVM apps this shouldn't change.
+#   model_storage_folder: where to put saved P1/P2 models.
+app = PipedBinaryApp("TestHPVMApp", binary_file, metadata_file)
+# Tuning procedure is exactly the same as that for PyTorch DNN.
+# Please refer to `./tune_vgg16_cifar10.py` for details.
+tuner = app.get_tuner()
+tuner.tune(100, 3.0, 3.0, True, 50, cost_model="cost_linear")
+tuner.dump_configs("configs.json")
+fig = tuner.plot_configs(show_qos_loss=True)
+fig.savefig("configs.png", dpi=300)
+app.dump_hpvm_configs(tuner.best_configs, "hpvm_confs.txt")
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/predtuner/__init__.py b/predtuner/__init__.py
index c11d18f205241f54b32405475e6e19d22d85538a..babe3e013b099b78047d3cea48fea562ac834c8e 100644
--- a/predtuner/__init__.py
+++ b/predtuner/__init__.py
@@ -9,5 +9,6 @@ from .modeledapp import (
     QoSModelP1,
     QoSModelP2,
 )
+from .pipedbin import PipedBinaryApp
 from .torchapp import TorchApp, TorchApproxKnob
 from .torchutil import accuracy