Sadjad Alikhani commited on
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Update README.md

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@@ -4,7 +4,7 @@ This repository contains the implementation of **LWM** (Large Wireless Model), a
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  ## How to Use
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- ### Step-by-Step Guide
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  1. **Clone the Repository**
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@@ -95,7 +95,7 @@ This repository contains the implementation of **LWM** (Large Wireless Model), a
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  preprocessed_chs = tokenizer(deepmimo_data, scenario_idxs, gen_raw=True)
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  ```
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- 5. **Generate the Dataset for Inference**
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  Choose the type of data you want to generate from the tokenized dataset, such as `cls_emb`, `channel_emb`, or `raw`:
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@@ -105,7 +105,9 @@ This repository contains the implementation of **LWM** (Large Wireless Model), a
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  dataset = dataset_gen(preprocessed_chs, input_type, model)
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  ```
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- 6. **Use the Dataset in Downstream Tasks**
 
 
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  Finally, you can use the generated raw channels and their inferred LWM embeddings in your downstream tasks:
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  ## How to Use
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+ ### LWM Inference
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  1. **Clone the Repository**
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  preprocessed_chs = tokenizer(deepmimo_data, scenario_idxs, gen_raw=True)
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  ```
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+ 5. **LWM Inference**
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  Choose the type of data you want to generate from the tokenized dataset, such as `cls_emb`, `channel_emb`, or `raw`:
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  dataset = dataset_gen(preprocessed_chs, input_type, model)
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  ```
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+ ### Post-processing for Downstream Task
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+
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+ 1. **Use the Dataset in Downstream Tasks**
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  Finally, you can use the generated raw channels and their inferred LWM embeddings in your downstream tasks:
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