Model Farm Preview User Guide

Introduction

Model Farm proactively adapts cutting-edge or popular AI models. To give developers an early understanding of the Model Farm model adaptation release plans and related model performance parameters, we have launched Model Farm Preview.

Model Farm Preview follows the same usage methods and unified interface interactions as Model Farm. Developers can quickly view the performance parameters of models about to be released on Model Farm, or obtain support by contacting Aplux to get early access to the related model files and inference code.

💡Note

Model Farm Preview is intended only for viewing model adaptation status and related performance parameters. Models cannot be downloaded or used until they are officially released on Model Farm.

Quick Start

Model Farm Preview is used only to display model performance parameters in advance and does not support downloads. Therefore, developers can access all information on this page without logging in.

System Dependency Configuration

Configure the AidLux Repository

# Download the correct public key
sudo wget -O- https://archive.aidlux.com/ubuntu24/public.key | gpg --dearmor | sudo tee /etc/apt/trusted.gpg.d/private-aidlux.gpg > /dev/null

# Edit the source file
sudo vim /etc/apt/sources.list.d/private-aidlux.list

# Add the private key provided by AidLux to the source file
deb [arch=arm64 signed-by=/etc/apt/trusted.gpg.d/private-aidlux.gpg] https://archive.aidlux.com/ubuntu24 noble main

# Update the cache
sudo apt update

After the update, you can obtain the official AidLux SDK dependencies with the following command:

sudo apt list | grep aid | grep unknown

# Install software
# Must be installed first; not included with the system
sudo apt install python3 python3-pip libopencv-dev python3-opencv  net-tools
# Required before installing aidlite
sudo apt install aidlux-aistack-base aidrtcm

# Install aidlite and its dependencies
sudo apt install aid-lms aidlms-sdk aidlite-sdk cmake
sudo apt-get install libfmt-dev nlohmann-json3-dev
sudo apt install aidlite-*

# Enable DSP support
sudo apt-get install qcom-fastrpc1
sudo apt-get install qcom-fastrpc-dev

# Install the aidgen SDK
sudo apt install aidgen-qnn240-sdk

# Install the mms service
sudo apt install aid-mms

# Enable GPU support
sudo add-apt-repository ppa:ubuntu-qcom-iot/qcom-noble-ppa
sudo apt install qcom-adreno-cl1
sudo ln -s /usr/lib/aarch64-linux-gnu/libOpenCL.so.1 /usr/lib/aarch64-linux-gnu/libOpenCL.so

After installation, check that the aidlite and aidgen directories have been added under /usr/local/share.

Device Authorization

Obtain the Device SN Code

cat  /sys/devices/soc0/serial_number

Obtain the License File

Provide the SN code to the Aplux technical staff to generate a device-specific License file, and place it under /etc/opt/aidlux/license/AidLuxLics.

Accessing the Model Farm Preview Page

• Developers can click the link to access the Model Farm Preview interface directly: Model Farm Preview

• Developers can also enter the Model Farm Preview interface through the Model Farm interface:

Viewing Models

Developers can search for models on Model Farm Preview according to their needs, understand detailed model information, and make quick evaluations.

Model Farm Preview provides multiple ways to filter and search for models:

• Filter by model type
• Filter by model data precision
• Filter by chip platform
• Keyword search

Model Performance Reference

The model details page of Model Farm Preview provides the measured performance of AI models on the corresponding hardware.

• Device: The development board model and corresponding chip model used for the measurement.
• AI Framework: The framework and version used for model conversion and inference.
• Data Precision: The data precision of the converted model.
• Inference Latency: The measured execution time of the model, excluding pre- and post-processing.
• Accuracy Loss: The cosine similarity of the output matrices between the source model (FP32) and the converted model.
• Model Size: The file size of the converted model.

💡Note

For the same SoC, model performance may vary across devices with different hardware specifications; these figures serve as a reference only.

Take SigLIP-so400m on MeiG SNM972 (QCS8550) as an example:

Accessing Model Files and Code in Advance

Models in the Preview section of the Model Farm do not support direct web downloads. Developers can download preview models on Aplux boards using the mms command. The following example demonstrates how to retrieve the MobileClip2-S3 model.

1. Log in via mms

mms login

# Enter your username: 
# Enter your password:

# A message will appear after entering the correct credentials:
# Login successfully.

2. Search for the model

mms list mobileclip # Supports keyword search

# Expected output:
Model           Precision  Chipset           Backend
-----           ---------  -------           -------
MobileClip-S2   FP16       Qualcomm QCS8550  QNN2.31
MobileClip2-S3  FP16       Qualcomm QCS8550  QNN2.36

3. Download MobileClip2-S3

# -m: Model name
# -p: Precision
# -c: Chipset
# -b: QNN version
mms get -m MobileClip2-S3 -p fp16 -c qcs8550 -b qnn2.36

# Model resources are downloaded to /var/opt/modelfarm_models by default.