update

10_integrations/dbt_modal_inference/dbt_modal_inference.py

@@ -3,8 +3,6 @@
 # In this example we demonstrate how you could combine [dbt's python models](https://docs.getdbt.com/docs/build/python-models)
 # with LLM inference models powered by Modal, allowing you to run serverless gpu workloads within dbt.
 #
-# ## Overview
-#
 # This example runs [dbt](https://docs.getdbt.com/docs/introduction) with a [DuckDB](https://duckdb.org)
 # backend directly on top of Modal, but could be translated to run on any dbt-compatible
 # database that supports python models. Similarly you could make these requests from UDFs
@@ -15,7 +13,6 @@
 # for free-text product reviews and aggregate them in subsequent dbt sql models. These product names, descriptions and reviews
 # were also generated by an LLM running on Modal!
 #
-#
 # ## Configure Modal and dbt
 #
 # We set up the environment variables necessary for dbt and
@@ -35,7 +32,7 @@
 TARGET_PATH = f"{VOL_PATH}/target"
 
 # We also define the environment our application will run in --
-# a container image, as in Docker.
+# a container image, similar to Docker.
 # See [this guide](https://modal.com/docs/guide/custom-container) for details.
 
 dbt_image = (  # start from a slim Linux image