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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### 🎥 Watch the tutorial
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Check out this tutorial video to see the model in action! Click on the thumbnail below to watch it on YouTube.
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[](https://www.youtube.com/watch?v=YOUTUBE_VIDEO_ID)
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*In this video, we walk through the LWM paper, explain how the model works, and demonstrate its application for downstream tasks with practical examples. You'll find step-by-step instructions and detailed insights into the model's output.*
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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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### How is LWM built?
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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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### How is LWM built?
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