Background
HRM models are particularly suitable for deployment in resource-constrained environments due to their lightweight design of 27 million parameters. Compared to traditional large models that require cloud deployment, HRM can run complex inference tasks on edge devices.
Core Solutions
- Hardware Selection: Minimum requirements are NVIDIA GPUs with CUDA support (e.g. RTX 3060), and 8GB of RAM and above are recommended
- Optimize deployment steps::
- Converting models to mobile format using PyTorch Mobile
- Enable mixed precision inference (FP16) to reduce video memory footprint
- Disable training mode (torch.no_grad()) to reduce memory consumption
- Uses modular loading, loading only the submodules needed for the current task
- Performance Tuning Tips: Set OMP_NUM_THREADS=8 to optimize CPU parallelism, use torch.inference_mode() to improve inference speed
Typical Application Examples
In Robotic Navigation System for Raspberry Pi + Jetson Nano:
1. Load the pre-trained 30 x 30 maze model
2. Input sensor data every 200ms
3. High-level module outputs path planning, low-level module handles real-time obstacle avoidance
This answer comes from the articleHRM: Hierarchical Reasoning Model for Complex ReasoningThe
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