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---
library_name: pytorch
license: other
tags:
- android
pipeline_tag: text-generation
---
# Nomic-Embed-Text: Optimized for Qualcomm Devices
A text encoder that surpasses OpenAI text-embedding-ada-002 and text-embedding-3-small performance on short and long context tasks.
This is based on the implementation of Nomic-Embed-Text found [here](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5).
This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/nomic_embed_text) library to export with custom configurations. More details on model performance across various devices, can be found [here](#performance-summary).
Qualcomm AI Hub Models uses [Qualcomm AI Hub Workbench](https://workbench.aihub.qualcomm.com) to compile, profile, and evaluate this model. [Sign up](https://myaccount.qualcomm.com/signup) 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 |
|---|---|---|---|---|
| ONNX | float | Universal | QAIRT 2.37, ONNX Runtime 1.23.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/nomic_embed_text/releases/v0.46.0/nomic_embed_text-onnx-float.zip)
| QNN_DLC | float | Universal | QAIRT 2.42 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/nomic_embed_text/releases/v0.46.0/nomic_embed_text-qnn_dlc-float.zip)
| TFLITE | float | Universal | QAIRT 2.42, TFLite 2.17.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/nomic_embed_text/releases/v0.46.0/nomic_embed_text-tflite-float.zip)
For more device-specific assets and performance metrics, visit **[Nomic-Embed-Text on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/nomic_embed_text)**.
### Option 2: Export with Custom Configurations
Use the [Qualcomm® AI Hub Models](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/nomic_embed_text) 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 [Nomic-Embed-Text on GitHub](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/nomic_embed_text) for usage instructions.
## Model Details
**Model Type:** Model_use_case.text_generation
**Model Stats:**
- Model checkpoint: v1.5
- Input resolution: 1x128 (seqlen can vary)
- Number of parameters: 137M
- Model size (float): 523 MB
## Performance Summary
| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
|---|---|---|---|---|---|---
| Nomic-Embed-Text | ONNX | float | Snapdragon® X Elite | 8.962 ms | 263 - 263 MB | NPU
| Nomic-Embed-Text | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 5.925 ms | 0 - 488 MB | NPU
| Nomic-Embed-Text | ONNX | float | Qualcomm® QCS8550 (Proxy) | 8.472 ms | 0 - 323 MB | NPU
| Nomic-Embed-Text | ONNX | float | Qualcomm® QCS9075 | 11.424 ms | 0 - 4 MB | NPU
| Nomic-Embed-Text | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 4.75 ms | 0 - 457 MB | NPU
| Nomic-Embed-Text | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 4.317 ms | 0 - 455 MB | NPU
| Nomic-Embed-Text | QNN_DLC | float | Snapdragon® X Elite | 8.101 ms | 0 - 0 MB | NPU
| Nomic-Embed-Text | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 5.317 ms | 0 - 446 MB | NPU
| Nomic-Embed-Text | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 28.402 ms | 0 - 415 MB | NPU
| Nomic-Embed-Text | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 7.624 ms | 0 - 3 MB | NPU
| Nomic-Embed-Text | QNN_DLC | float | Qualcomm® SA8775P | 9.771 ms | 0 - 415 MB | NPU
| Nomic-Embed-Text | QNN_DLC | float | Qualcomm® QCS9075 | 10.408 ms | 2 - 4 MB | NPU
| Nomic-Embed-Text | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 10.925 ms | 0 - 427 MB | NPU
| Nomic-Embed-Text | QNN_DLC | float | Qualcomm® SA7255P | 28.402 ms | 0 - 415 MB | NPU
| Nomic-Embed-Text | QNN_DLC | float | Qualcomm® SA8295P | 10.716 ms | 0 - 399 MB | NPU
| Nomic-Embed-Text | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 4.236 ms | 0 - 414 MB | NPU
| Nomic-Embed-Text | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 3.824 ms | 0 - 413 MB | NPU
| Nomic-Embed-Text | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 5.297 ms | 0 - 459 MB | NPU
| Nomic-Embed-Text | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 28.35 ms | 0 - 425 MB | NPU
| Nomic-Embed-Text | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 7.407 ms | 0 - 3 MB | NPU
| Nomic-Embed-Text | TFLITE | float | Qualcomm® SA8775P | 9.726 ms | 0 - 424 MB | NPU
| Nomic-Embed-Text | TFLITE | float | Qualcomm® QCS9075 | 10.607 ms | 0 - 265 MB | NPU
| Nomic-Embed-Text | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 10.925 ms | 0 - 429 MB | NPU
| Nomic-Embed-Text | TFLITE | float | Qualcomm® SA7255P | 28.35 ms | 0 - 425 MB | NPU
| Nomic-Embed-Text | TFLITE | float | Qualcomm® SA8295P | 10.787 ms | 0 - 403 MB | NPU
| Nomic-Embed-Text | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 4.22 ms | 0 - 426 MB | NPU
| Nomic-Embed-Text | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 3.827 ms | 0 - 421 MB | NPU
## License
* The license for the original implementation of Nomic-Embed-Text can be found
[here](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md).
## References
* [Introducing Nomic Embed: A Truly Open Embedding Model](https://www.nomic.ai/blog/posts/nomic-embed-text-v1)
* [Source Model Implementation](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5)
## Community
* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).
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