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
Update input_preprocess.py
Browse files- input_preprocess.py +1 -1
input_preprocess.py
CHANGED
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@@ -371,4 +371,4 @@ def create_labels(task, scenario_names, n_beams=64):
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for scenario_name in scenario_names:
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data = DeepMIMO_data_gen(scenario_name)
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labels.extend(label_gen(task, data, scenario_name, n_beams=n_beams))
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return labels
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for scenario_name in scenario_names:
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data = DeepMIMO_data_gen(scenario_name)
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labels.extend(label_gen(task, data, scenario_name, n_beams=n_beams))
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+
return torch.tensor(labels)
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