[GitHub] [beam] shub-kris commented on a diff in pull request #25099: Add more info in documentation

["shub-kris (via GitHub)" <gi...@apache.org>]

shub-kris commented on code in PR #25099:
URL: https://github.com/apache/beam/pull/25099#discussion_r1084145402


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website/www/site/content/en/documentation/ml/large-language-modeling.md:
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@@ -25,11 +25,12 @@ RunInference works well on arbitrarily large models as long as they can fit on y
 This example demonstrates running inference with a `T5` language model using `RunInference` in a pipeline. `T5` is an encoder-decoder model pre-trained on a multi-task mixture of unsupervised and supervised tasks. Each task is converted into a text-to-text format. The example uses `T5-11B`, which contains 11 billion parameters and is 45 GB in size. In  order to work well on a variety of tasks, `T5` prepends a different prefix to the input corresponding to each task. For example, for translation, the input would be: `translate English to German: …` and for summarization, it would be: `summarize: …`. For more information about `T5` see the [T5 overiew](https://huggingface.co/docs/transformers/model_doc/t5) in the HuggingFace documentation.
 
 ### Run the Pipeline ?
-First, install the required packages and pass the required arguments.
+First, install the required packages listed in [requirements.txt](https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/inference/large_language_modeling/requirements.txt) and pass the required arguments.
 You can view the code on [GitHub](https://github.com/apache/beam/tree/master/sdks/python/apache_beam/examples/inference/large_language_modeling/main.py)
 
-1. Locally on your machine: `python main.py --runner DirectRunner`. 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`
+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

Review Comment:
   Done



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