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Add more info in documentation #25099
Add more info in documentation #25099
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Hi, @damccorm please find the updated documentation. |
1. Locally on your machine: `python main.py --runner DirectRunner --model_state_dict_path path_to_saved_model`. You need to have 45 GB of disk space available to run this example. | ||
2. On Google Cloud using Dataflow: `python main.py --runner DataflowRunner --model_state_dict_path path_to_saved_model --project PROJECT_ID |
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1. Locally on your machine: `python main.py --runner DirectRunner --model_state_dict_path path_to_saved_model`. You need to have 45 GB of disk space available to run this example. | |
2. On Google Cloud using Dataflow: `python main.py --runner DataflowRunner --model_state_dict_path path_to_saved_model --project PROJECT_ID | |
1. Locally on your machine: `python main.py --runner DirectRunner --model_state_dict_path <local or remote path to state_dict> path_to_saved_model <local or remote path to saved model file> `. You need to have 45 GB of disk space available to run this example. | |
2. On Google Cloud using Dataflow: `python main.py --runner DataflowRunner --model_state_dict_path <local or remote path to state_dict> path_to_saved_model <local or remote path to saved model file> --project PROJECT_ID |
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Could you also please include instructions on pulling the model to get the state_dict/saved model file
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Done
This PR aims to add more information on the arguments required to pass for running the large language model runinference pipeline.
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addresses #123
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