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README.md

@@ -27,7 +27,7 @@ NVIDIA does not claim ownership to any outputs generated using the Models or Der
 
 ### Intended use
 
-Nemotron-4-340B-Base is a completion model intended for use in over 50+ natural and 40+ coding languages. For best performance on a given task, users are encouraged to customize the completion model using the NeMo Framework suite of customization tools including Parameter-Efficient Fine-Tuning (P-tuning, Adapters, LoRA), and SFT/Steer-LM/RLHF using [NeMo-Aligner](https://github.com/NVIDIA/NeMo-Aligner).
+Nemotron-4-340B-Base is a completion model intended for use in over 50+ natural and 40+ coding languages. For best performance on a given task, users are encouraged to customize the completion model using the [NeMo Framework](https://docs.nvidia.com/nemo-framework/index.html) suite of customization tools including Parameter-Efficient Fine-Tuning (P-tuning, Adapters, LoRA), and SFT/Steer-LM/RLHF using [NeMo-Aligner](https://github.com/NVIDIA/NeMo-Aligner).
 
 **Model Developer:** NVIDIA
 
@@ -59,7 +59,7 @@ Nemotron-4
 
 ### Usage
 
-1. We will spin up an inference server and then call the inference server in a python script. Let’s first define the python script ``call_server.py``
+1. We will spin up an inference server and then call the inference server in a python script. Let’s first define the python script ``call_server.py``.
 
 ```python
 import requests
@@ -101,7 +101,7 @@ print(response)
 ```
 
 
-2. Given this python script, we will create a bash script, which spins up the inference server within the NeMo container(docker pull nvcr.io/nvidia/nemo:24.01.framework) and calls the python script ``call_server.py``. The bash script ``nemo_inference.sh`` is as follows,
+2. Given this python script, we will create a bash script, which spins up the inference server within the NeMo container (```docker pull nvcr.io/nvidia/nemo:24.01.framework```) and calls the python script ``call_server.py``. The bash script ``nemo_inference.sh`` is as follows,
 
 
 ```bash
@@ -151,13 +151,13 @@ depends_on () {
 ```
 
 
-3, We can launch the ``nemo_inferece.sh`` with a slurm script defined like below, which starts a 2-node job for the model inference.
+3. We can launch the ``nemo_inferece.sh`` with a slurm script defined like below, which starts a 2-node job for the model inference.
 
 ```bash
 #!/bin/bash
 #SBATCH -A SLURM-ACCOUNT
 #SBATCH -p SLURM-PARITION
-#SBATCH -N 2 # number of nodes
+#SBATCH -N 2
 #SBATCH -J generation      
 #SBATCH --ntasks-per-node=8   
 #SBATCH --gpus-per-node=8
@@ -167,8 +167,9 @@ RESULTS=<PATH_TO_YOUR_SCRIPTS_FOLDER>
 OUTFILE="${RESULTS}/slurm-%j-%n.out"
 ERRFILE="${RESULTS}/error-%j-%n.out"
 MODEL=<PATH_TO>/Nemotron-4-340B-Base
-
+CONTAINER="nvcr.io/nvidia/nemo:24.01.framework"
 MOUNTS="--container-mounts=<PATH_TO_YOUR_SCRIPTS_FOLDER>:/scripts,MODEL:/model"
+
 read -r -d '' cmd <<EOF
 bash /scripts/nemo_inference.sh /model
 EOF