Albert-Base-V2-Hf: Optimized for Qualcomm Devices

ALBERT is a lightweight BERT model designed for efficient self-supervised learning of language representations. It can be used for masked language modeling and as a backbone for various NLP tasks.

This is based on the implementation of Albert-Base-V2-Hf found here. This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the Qualcomm® AI Hub Models library to export with custom configurations. More details on model performance across various devices, can be found here.

Qualcomm AI Hub Models uses Qualcomm AI Hub Workbench to compile, profile, and evaluate this model. Sign up to run these models on a hosted Qualcomm® device.

Getting Started

There are two ways to deploy this model on your device:

Option 1: Download Pre-Exported Models

Below are pre-exported model assets ready for deployment.

Runtime	Precision	Chipset	SDK Versions	Download
QNN_DLC	float	Universal	QAIRT 2.45	Download
TFLITE	float	Universal	QAIRT 2.45	Download

For more device-specific assets and performance metrics, visit Albert-Base-V2-Hf on Qualcomm® AI Hub.

Option 2: Export with Custom Configurations

Use the Qualcomm® AI Hub Models Python library to compile and export the model with your own:

• Custom weights (e.g., fine-tuned checkpoints)
• Custom input shapes
• Target device and runtime configurations

This option is ideal if you need to customize the model beyond the default configuration provided here.

See our repository for Albert-Base-V2-Hf on GitHub for usage instructions.

Model Details

Model Type: Model_use_case.text_generation

Model Stats:

• Input resolution: 1x384
• Model checkpoint: albert/albert-base-v2
• Model size (float): 43.9 MB
• Number of parameters: 11.8M

Performance Summary

Model	Runtime	Precision	Chipset	Inference Time (ms)	Peak Memory Range (MB)	Primary Compute Unit
Albert-Base-V2-Hf	QNN_DLC	float	Snapdragon® X2 Elite	7.933 ms	1 - 1 MB	NPU
Albert-Base-V2-Hf	QNN_DLC	float	Snapdragon® X Elite	18.311 ms	1 - 1 MB	NPU
Albert-Base-V2-Hf	QNN_DLC	float	Snapdragon® 8 Gen 3 Mobile	12.655 ms	0 - 356 MB	NPU
Albert-Base-V2-Hf	QNN_DLC	float	Snapdragon® 8 Gen 1 Mobile	31.68 ms	0 - 324 MB	NPU
Albert-Base-V2-Hf	QNN_DLC	float	Qualcomm® Dragonwing™ IQ-8275	23.163 ms	0 - 2 MB	NPU
Albert-Base-V2-Hf	QNN_DLC	float	Qualcomm® Dragonwing™ IQ-8275	68.858 ms	0 - 324 MB	NPU
Albert-Base-V2-Hf	QNN_DLC	float	Qualcomm® Dragonwing™ QCS8550 (Proxy)	17.614 ms	0 - 3 MB	NPU
Albert-Base-V2-Hf	QNN_DLC	float	Qualcomm® SA8775P	21.906 ms	0 - 307 MB	NPU
Albert-Base-V2-Hf	QNN_DLC	float	Qualcomm® SA8650P	21.906 ms	0 - 307 MB	NPU
Albert-Base-V2-Hf	QNN_DLC	float	Qualcomm® SA8255P	21.906 ms	0 - 307 MB	NPU
Albert-Base-V2-Hf	QNN_DLC	float	Qualcomm® QCS8450	31.68 ms	0 - 324 MB	NPU
Albert-Base-V2-Hf	QNN_DLC	float	Qualcomm® Dragonwing™ IQ-9075	24.653 ms	2 - 4 MB	NPU
Albert-Base-V2-Hf	QNN_DLC	float	Qualcomm® Dragonwing™ IQ-X7181	18.311 ms	1 - 1 MB	NPU
Albert-Base-V2-Hf	QNN_DLC	float	Qualcomm® Dragonwing™ Q-8750	9.419 ms	0 - 314 MB	NPU
Albert-Base-V2-Hf	QNN_DLC	float	Qualcomm® SA7255P	68.858 ms	0 - 324 MB	NPU
Albert-Base-V2-Hf	QNN_DLC	float	Qualcomm® SA8295P	27.629 ms	0 - 334 MB	NPU
Albert-Base-V2-Hf	QNN_DLC	float	Snapdragon® 8 Elite Mobile	9.419 ms	0 - 314 MB	NPU
Albert-Base-V2-Hf	QNN_DLC	float	Snapdragon® 8 Elite Gen 5 Mobile	7.189 ms	0 - 341 MB	NPU
Albert-Base-V2-Hf	TFLITE	float	Snapdragon® 8 Gen 3 Mobile	13.003 ms	0 - 389 MB	NPU
Albert-Base-V2-Hf	TFLITE	float	Snapdragon® 8 Gen 1 Mobile	32.35 ms	0 - 370 MB	NPU
Albert-Base-V2-Hf	TFLITE	float	Qualcomm® Dragonwing™ IQ-8275	23.536 ms	0 - 33 MB	NPU
Albert-Base-V2-Hf	TFLITE	float	Qualcomm® Dragonwing™ IQ-8275	69.462 ms	0 - 339 MB	NPU
Albert-Base-V2-Hf	TFLITE	float	Qualcomm® Dragonwing™ QCS8550 (Proxy)	17.094 ms	0 - 7 MB	NPU
Albert-Base-V2-Hf	TFLITE	float	Qualcomm® SA8775P	22.096 ms	0 - 338 MB	NPU
Albert-Base-V2-Hf	TFLITE	float	Qualcomm® SA8650P	22.096 ms	0 - 338 MB	NPU
Albert-Base-V2-Hf	TFLITE	float	Qualcomm® SA8255P	22.096 ms	0 - 338 MB	NPU
Albert-Base-V2-Hf	TFLITE	float	Qualcomm® QCS8450	32.35 ms	0 - 370 MB	NPU
Albert-Base-V2-Hf	TFLITE	float	Qualcomm® Dragonwing™ IQ-9075	22.262 ms	0 - 32 MB	NPU
Albert-Base-V2-Hf	TFLITE	float	Qualcomm® Dragonwing™ Q-8750	9.715 ms	0 - 330 MB	NPU
Albert-Base-V2-Hf	TFLITE	float	Qualcomm® SA7255P	69.462 ms	0 - 339 MB	NPU
Albert-Base-V2-Hf	TFLITE	float	Qualcomm® SA8295P	27.861 ms	0 - 327 MB	NPU
Albert-Base-V2-Hf	TFLITE	float	Snapdragon® 8 Elite Mobile	9.715 ms	0 - 330 MB	NPU
Albert-Base-V2-Hf	TFLITE	float	Snapdragon® 8 Elite Gen 5 Mobile	7.485 ms	0 - 333 MB	NPU

License

• The license for the original implementation of Albert-Base-V2-Hf can be found here.

References

• ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
• Source Model Implementation

Community

• Join our AI Hub Slack community to collaborate, post questions and learn more about on-device AI.
• For questions or feedback please reach out to us.