Update llama-3 PEFT notebook to download model from NGC
I am providing additional copyedits to the file, ReadMe.rst, herein. I was not able to provide inline edits because part of the file was read-only.

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`Llama 3 <https://blogs.nvidia.com/blog/meta-llama3-inference-acceleration/>`_ is an open-source large language model by Meta that delivers state-of-the-art performance on popular industry benchmarks.

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`Low-Rank Adaptation (LoRA) <https://arxiv.org/pdf/2106.09685>`__ has emerged as a popular Parameter-Efficient Fine-Tuning (PEFT) technique that tunes a very small number of additional parameters as compared to full fine-tuning, thereby reducing the compute required.

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NIM enables seamless deployment of multiple LoRA adapters (referred to as ‘multi-LoRA’) on the same base model. It dynamically loads adapter weights based on incoming requests at runtime. This flexibility allows handling inputs from various tasks or use cases without requiring a unique model for each individual scenario. For further details, consult the NIM documentation for LLMs.

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In order to proceed, ensure that you have met the following requirements:

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2. Prepare the LoRA model store. 

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To ensure the model store is organized as expected, create a folder named llama3-8b-pubmed-qa and move your .nemo checkpoint there.

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Ensure that the LoRA model store directory follows this structure: the model name should be a sub-folder containing the .nemo file.

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3. Set up NIM. 

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The first time you run the command, it will download the model and cache it in $NIM_CACHE_PATH so subsequent deployments are even faster. There are several options to configure NIM other than the ones listed above. You can find a full list in the NIM configuration documentation.

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4. Start the notebook.

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From another terminal, follow the same instructions as the previous notebook to launch Jupyter Lab, and then navigate to this notebook.

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You can use the same NeMo Framework docker container in which Jupyter Lab was previously installed.
* Updates the llama-3 PEFT tutorial to download the model from NVIDIA NGC to skip conversion
