# Qualcomm model deployment

***Copyright © Quectel Wireless Solutions Co., Ltd. 2026. All rights reserved.***

---

Qualcomm provides the **qai_hub_models** **Python** library. It enables developers to easily perform model conversion and quantization, and export the as **BIN** files that can be directly loaded by Qualcomm **NPU**. The library also supports model inference and validation using Qualcomm's online virtual devices.

# Preparations

1. Install **qai_hub_models** on device.

```shell
pip3 install qai_hub_models
```

2. Configure **API Token**.

**📌 Prompt**  
Please register an account on [Qualcomm® AI Hub](<https://aihub.qualcomm.com/>) and log in to obtain the user API Token.

```shell
export PATH=~/.local/bin/:$PATH
qai-hub configure --api_token <API_TOKEN>
```

# Model core information

The models supported by **qai_hub_madels** include **4** types of **Computer vision**, **Multinodal**, **Audio**, and **Generation**. Please refer to the table below for details.

## Computer vision

| **Model** | **README** |
| --- | --- |
| Qualcomm model download center | Github repository address |
| Image classification | &nbsp; |
| Beit | qai_hub_models.models.beit |
| ConvNext-Base | qai_hub_models.models.convnext_base |
| ConvNext-Tiny | qai_hub_models.models.convnext_tiny |
| DLA-102-X | qai_hub_models.models.dla102x |
| DenseNet-121 | qai_hub_models.models.densenet121 |
| EfficientFormer | qai_hub_models.models.efficientformer |
| EfficientNet-B0 | qai_hub_models.models.efficientnet_b0 |
| EfficientNet-B4 | qai_hub_models.models.efficientnet_b4 |
| EfficientNet-V2-s | qai_hub_models.models.efficientnet_v2_s |
| EfficientViT-b2-cls | qai_hub_models.models.efficientvit_b2_cls |
| EfficientViT-l2-cls | qai_hub_models.models.efficientvit_l2_cls |
| GoogLeNet | qai_hub_models.models.googlenet |
| Inception-v3 | qai_hub_models.models.inception_v3 |
| LeViT | qai_hub_models.models.levit |
| MNASNet05 | qai_hub_models.models.mnasnet05 |
| Mobile-VIT | qai_hub_models.models.mobile_vit |
| MobileNet-v2 | qai_hub_models.models.mobilenet_v2 |
| MobileNet-v3-Large | qai_hub_models.models.mobilenet_v3_large |
| MobileNet-v3-Small | qai_hub_models.models.mobilenet_v3_small |
| NASNet | qai_hub_models.models.nasnet |
| RegNet | qai_hub_models.models.regnet |
| ResNeXt101 | qai_hub_models.models.resnext101 |
| ResNeXt50 | qai_hub_models.models.resnext50 |
| ResNet101 | qai_hub_models.models.resnet101 |
| ResNet18 | qai_hub_models.models.resnet18 |
| ResNet50 | qai_hub_models.models.resnet50 |
| Sequencer2D | qai_hub_models.models.sequencer2d |
| Shufflenet-v2 | qai_hub_models.models.shufflenet_v2 |
| SqueezeNet-1.1 | qai_hub_models.models.squeezenet1_1 |
| Swin-Base | qai_hub_models.models.swin_base |
| Swin-Small | qai_hub_models.models.swin_small |
| Swin-Tiny | qai_hub_models.models.swin_tiny |
| VIT | qai_hub_models.models.vit |
| WideResNet50 | qai_hub_models.models.wideresnet50 |
| Image editing | &nbsp; |
| AOT-GAN | qai_hub_models.models.aotgan |
| LaMa-Dilated | qai_hub_models.models.lama_dilated |
| Image generation | &nbsp; |
| Simple-Bev | qai_hub_models.models.simple_bev_cam |
| Super resolution | &nbsp; |
| ESRGAN | qai_hub_models.models.esrgan |
| QuickSRNetLarge | qai_hub_models.models.quicksrnetlarge |
| QuickSRNetMedium | qai_hub_models.models.quicksrnetmedium |
| QuickSRNetSmall | qai_hub_models.models.quicksrnetsmall |
| Real-ESRGAN-General-x4v3 | qai_hub_models.models.real_esrgan_general_x4v3 |
| Real-ESRGAN-x4plus | qai_hub_models.models.real_esrgan_x4plus |
| SESR-M5 | qai_hub_models.models.sesr_m5 |
| XLSR | qai_hub_models.models.xlsr |
| Semantic segmentation | &nbsp; |
| BGNet | qai_hub_models.models.bgnet |
| BiseNet | qai_hub_models.models.bisenet |
| DDRNet23-Slim | qai_hub_models.models.ddrnet23_slim |
| DeepLabV3-Plus-MobileNet | qai_hub_models.models.deeplabv3_plus_mobilenet |
| DeepLabV3-ResNet50 | qai_hub_models.models.deeplabv3_resnet50 |
| DeepLabXception | qai_hub_models.models.deeplab_xception |
| EfficientViT-l2-seg | qai_hub_models.models.efficientvit_l2_seg |
| FCN-ResNet50 | qai_hub_models.models.fcn_resnet50 |
| FFNet-122NS-LowRes | qai_hub_models.models.ffnet_122ns_lowres |
| FFNet-40S | qai_hub_models.models.ffnet_40s |
| FFNet-54S | qai_hub_models.models.ffnet_54s |
| FFNet-78S | qai_hub_models.models.ffnet_78s |
| FFNet-78S-LowRes | qai_hub_models.models.ffnet_78s_lowres |
| FastSam-S | qai_hub_models.models.fastsam_s |
| FastSam-X | qai_hub_models.models.fastsam_x |
| HRNet-W48-OCR | qai_hub_models.models.hrnet_w48_ocr |
| Mask2Former | qai_hub_models.models.mask2former |
| MediaPipe-Selfie-Segmentation | qai_hub_models.models.mediapipe_selfie |
| MobileSam | qai_hub_models.models.mobilesam |
| PidNet | qai_hub_models.models.pidnet |
| SINet | qai_hub_models.models.sinet |
| SalsaNext | qai_hub_models.models.salsanext |
| Segformer-Base | qai_hub_models.models.segformer_base |
| Segment-Anything-Model-2 | qai_hub_models.models.sam2 |
| Unet-Segmentation | qai_hub_models.models.unet_segmentation |
| YOLOv11-Segmentation | qai_hub_models.models.yolov11_seg |
| YOLOv8-Segmentation | qai_hub_models.models.yolov8_seg |
| Video | Classification |
| ResNet-2Plus1D | qai_hub_models.models.resnet_2plus1d |
| ResNet-3D | qai_hub_models.models.resnet_3d |
| ResNet-Mixed-Convolution | qai_hub_models.models.resnet_mixed |
| Video-MAE | qai_hub_models.models.video_mae |
| Video generation | &nbsp; |
| First-Order-Motion-Model | qai_hub_models.models.fomm |
| Object detection | &nbsp; |
| 3D-Deep-BOX | qai_hub_models.models.deepbox |
| Conditional-DETR-ResNet50 | qai_hub_models.models.conditional_detr_resnet50 |
| DETR-ResNet101 | qai_hub_models.models.detr_resnet101 |
| DETR-ResNet101-DC5 | qai_hub_models.models.detr_resnet101_dc5 |
| DETR-ResNet50 | qai_hub_models.models.detr_resnet50 |
| DETR-ResNet50-DC5 | qai_hub_models.models.detr_resnet50_dc5 |
| Facial-Attribute-Detection | qai_hub_models.models.face_attrib_net |
| Lightweight-Face-Detection | qai_hub_models.models.face_det_lite |
| MediaPipe-Face-Detection | qai_hub_models.models.mediapipe_face |
| MediaPipe-Hand-Detection | qai_hub_models.models.mediapipe_hand |
| PPE-Detection | qai_hub_models.models.gear_guard_net |
| Person-Foot-Detection | qai_hub_models.models.foot_track_net |
| RF-DETR | qai_hub_models.models.rf_detr |

## Multimodal

| **Model** | **README** |
| --- | --- |
| EasyOCR | qai_hub_models.models.easyocr |
| Nomic-Embed-Text | qai_hub_models.models.nomic_embed_text |
| OpenAI-Clip | qai_hub_models.models.openai_clip |
| TrOCR | qai_hub_models.models.trocr |

## Audio

| **Model** | **README** |
| --- | --- |
| Speech recognition | &nbsp; |
| HuggingFace-WavLM-Base-Plus | qai_hub_models.models.huggingface_wavlm_base_plus |
| Whisper-Base | qai_hub_models.models.whisper_base |
| Whisper-Large-V3-Turbo | qai_hub_models.models.whisper_large_v3_turbo |
| Whisper-Small | qai_hub_models.models.whisper_small |
| Whisper-Tiny | qai_hub_models.models.whisper_tiny |
| Audio classification | &nbsp; |
| YamNet | qai_hub_models.models.yamnet |

## Generation

| **Model** | **README** |
| --- | --- |
| Image generation | &nbsp; |
| ControlNet-Canny | qai_hub_models.models.controlnet_canny |
| Stable-Diffusion-v1.5 | qai_hub_models.models.stable_diffusion_v1_5 |
| Stable-Diffusion-v2.1 | qai_hub_models.models.stable_diffusion_v2_1 |
| Text generation | &nbsp; |
| ALLaM-7B | qai_hub_models.models.allam_7b |
| Baichuan2-7B | qai_hub_models.models.baichuan2_7b |
| Falcon3-7B-Instruct | qai_hub_models.models.falcon_v3_7b_instruct |
| IBM-Granite-v3.1-8B-Instruct | qai_hub_models.models.ibm_granite_v3_1_8b_instruct |
| IndusQ-1.1B | qai_hub_models.models.indus_1b |
| JAIS-6p7b-Chat | qai_hub_models.models.jais_6p7b_chat |
| Llama-SEA-LION-v3.5-8B-R | qai_hub_models.models.llama_v3_1_sea_lion_3_5_8b_r |
| Llama-v2-7B-Chat | qai_hub_models.models.llama_v2_7b_chat |
| Llama-v3-8B-Instruct | qai_hub_models.models.llama_v3_8b_instruct |
| Llama-v3.1-8B-Instruct | qai_hub_models.models.llama_v3_1_8b_instruct |
| Llama-v3.2-1B-Instruct | qai_hub_models.models.llama_v3_2_1b_instruct |
| Llama-v3.2-3B-Instruct | qai_hub_models.models.llama_v3_2_3b_instruct |
| Llama3-TAIDE-LX-8B-Chat-Alpha1 | qai_hub_models.models.llama_v3_taide_8b_chat |
| Ministral-3B | qai_hub_models.models.ministral_3b |
| Mistral-3B | qai_hub_models.models.mistral_3b |
| Mistral-7B-Instruct-v0.3 | qai_hub_models.models.mistral_7b_instruct_v0_3 |
| PLaMo-1B | qai_hub_models.models.plamo_1b |
| Phi-3.5-Mini-Instruct | qai_hub_models.models.phi_3_5_mini_instruct |
| Qwen2-7B-Instruct | qai_hub_models.models.qwen2_7b_instruct |
| Qwen2.5-7B-Instruct | qai_hub_models.models.qwen2_5_7b_instruct |

# Model compilation

The details of compilation take **Yolov7** as an example,please refer to the relevant model **README** file for details:

```shell
export PRODUCT_CHIP=qualcomm-qcs6490
pip3 install "qai-hub-models[yolov7]"

python3 -m qai_hub_models.models.yolov7.export --chipset ${PRODUCT_CHIP} --target-runtime qnn_context_binary --quantize w8a8
```

- --chipset: Specifies the target chip to run on

- --target: Specifies the target runtime

- --quantize: Specifies the quantization method

The above command generates a **mode-id** and a file named **yolov7.qnn_context_binary**, which is the runtime model file for the target chip.

# Run demo

```shell
python3 -m qai_hub_models.models.yolov7.demo --quantize w8a8
```

- If you want to run **demo** on cloud devices, you need to add the **hub-model-id** parameter

```{image} images/image_POBKbvHnooS7Q4xMbR4cSslWnWF.webp
:width: 1900px
:height: 161px
```

## Use NPU of local device for inference validation

Please refer [NPU development guide](<../NPU development guide/NPU development guide.md>).

# Q&A

- How to use models to develop **app** ？

- Qualcomm provides the **ai-engine-direct-helper SDK**, which includes **Python** and **C++** interfaces for developing **apps** that can load models and perform inference. For details, please refer to [ai-engine-direct-helper](<https://github.com/quic/ai-engine-direct-helper>).