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Co-authored-by: Rocco Meli <[email protected]>

Author: lukasgd

docs/guides/mlp_tutorials/index.md

@@ -3,8 +3,8 @@
 
 These tutorials gradually introduce key concepts of the Machine Learning Platform. A particular focus is on the [Container Engine][ref-container-engine] for managing the runtime environment.
 
-In a [first tutorial][ref-mlp-llm-inference-tutorial], you will learn how to run an inference with an LLM on a single node using a container from the NVIDIA GPU Cloud (NGC). Concepts such as container environment description, layering a thin virtual environment on top of the container image and job launching and monitoring will be introduced.
+In a [first tutorial][ref-mlp-llm-inference-tutorial], you will learn how to run inference with a LLM on a single node using a container from the NVIDIA GPU Cloud (NGC). Concepts such as container environment description, layering a thin virtual environment on top of the container image, and job launching and monitoring will be introduced.
 
-Building on the first tutorial, in the [second tutorial][ref-mlp-llm-fine-tuning-tutorial] you will learn how to train (fine-tune) an LLM on multiple GPUs on a single node. For this purpose, you will use HuggingFace's `accelerate` and see best practices for dataset management.
+Building on the first tutorial, in the [second tutorial][ref-mlp-llm-fine-tuning-tutorial] you will learn how to train (fine-tune) a LLM on multiple GPUs on a single node. For this purpose, you will use HuggingFace's `accelerate` and see best practices for dataset management.
 
 In the [third tutorial][ref-mlp-llm-nanotron-tutorial], you will apply the techniques from the previous tutorials to enable distributed (pre-)training of a model `nanotron` on multiple nodes. In particular, this tutorial makes use of model-parallelism and introduces the usage of `torchrun` to manage jobs on individual nodes.