Instructions to use wi-lab/lwm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wi-lab/lwm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="wi-lab/lwm")# Load model directly from transformers import LWM model = LWM.from_pretrained("wi-lab/lwm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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LWM is a powerful **pre-trained** model developed as a **universal feature extractor** for wireless channels. As the world's first foundation model crafted for this domain, LWM leverages transformer architectures to extract refined representations from simulated datasets, such as DeepMIMO and Sionna, and real-world wireless data.
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Explore LWM concepts and applications in this compact video series:
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LWM is a powerful **pre-trained** model developed as a **universal feature extractor** for wireless channels. As the world's first foundation model crafted for this domain, LWM leverages transformer architectures to extract refined representations from simulated datasets, such as DeepMIMO and Sionna, and real-world wireless data.
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### LWM Tutorial Series
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Explore LWM concepts and applications in this compact video series:
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