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-# LLMs on HPC
-
-How to run LLMs on HPC.
diff --git a/docs/hpc/08_ml_ai_hpc/03_run_hf_model.md b/docs/hpc/08_ml_ai_hpc/03_run_hf_model.md
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+# Run a Hugging Face model
+
+Here we provide an example of how one can run a Hugging Face Large-language model (LLM) on the NYU Greene cluster
+
+## Prepare environment
+### Create project directory
+
+After [logging on to a Greene login node](../02_connecting_to_hpc/01_connecting_to_hpc.mdx), make a directory for this project:
+```bash
+[NetID@log-1 ~]$ mkdir -p /scratch/NetID/llm_example
+[NetID@log-1 ~]$ cd /scratch/NetID/llm_example
+```
+:::note
+You'll need to replace NetID above with your NetID
+:::
+
+### Move to a compute node
+Some of the following steps can require significant resources, so we'll move to a compute node.  This way we won't overload the login node we're on.
+```bash
+[NetID@log-1 llm_example]$ srun --cpus-per-task=2 --mem=10GB --time=04:00:00 --pty /bin/bash
+```
+
+### Copy appropriate overlay file to the project directory
+```bash
+[NetID@cm001 llm_example]$ cp -rp /scratch/work/public/overlay-fs-ext3/overlay-50G-10M.ext3.gz .
+[NetID@cm001 llm_example]$ gunzip overlay-50G-10M.ext3.gz
+```
+
+### Launch Singularity container in read/write mode
+```bash
+[NetID@cm001 llm_example]$ singularity exec --overlay overlay-50G-10M.ext3:rw /scratch/work/public/singularity/cuda12.1.1-cudnn8.9.0-devel-ubuntu22.04.2.sif /bin/bash
+```
+
+### Install miniconda in the container
+```bash
+Singularity> wget --no-check-certificate https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh
+Singularity> bash Miniforge3-Linux-x86_64.sh -b -p /ext3/miniforge3
+```
+
+### Create environment script
+Use an editor like nano or vim to create the file `/ext3/env.sh`.  The contents should be:
+```bash
+#!/bin/bash
+
+unset -f which
+
+source /ext3/miniforge3/etc/profile.d/conda.sh
+export PATH=/ext3/miniforge3/bin:$PATH
+export PYTHONPATH=/ext3/miniforge3/bin:$PATH
+```
+
+### Activate the environment
+```bash
+Singularity> source /ext3/env.sh
+```
+
+### Install packages in environment
+```bash
+Singularity> conda config --remove channels defaults
+Singularity> conda update -n base conda -y
+Singularity> conda clean --all --yes
+Singularity> conda install pip -y
+Singularity> pip install torch numpy transformers
+```
+
+### Exit from Singularity and the compute node
+```bash
+Singularity> exit
+[NetID@cm001 llm_example]$ exit
+```
+
+:::tip
+You can find more information about using Singularity and Conda on our HPC systems in our documentation [Singularity with Conda](https://sites.google.com/nyu.edu/nyu-hpc/hpc-systems/greene/software/singularity-with-miniconda).
+:::
+
+## Prepare script
+Create a python script using the following code from sections 1-9 and save it in a file called `huggingface.py`:
+
+1.  Import necessary modules:
+        ```python
+        import torch
+        import numpy as np
+        from transformers import AutoTokenizer, AutoModel
+        ```
+
+1.  Create a list of reviews:
+        ```python
+        texts = ["How do I get a replacement Medicare card?",
+                	"What is the monthly premium for Medicare Part B?",
+                	"How do I terminate my Medicare Part B (medical insurance)?",
+        		    "How do I sign up for Medicare?",
+        		    "Can I sign up for Medicare Part B if I am working and have health insurance through an employer?",
+               		"How do I sign up for Medicare Part B if I already have Part A?"]
+        ```
+
+1.  Choose the model name from huggingface’s model hub and instantiate the model and tokenizer object for the given model. We are setting `output_hidden_states` as `True` as we want the output of the model to not only have loss, but also the embeddings for the sentences.
+        ```python
+        model_name = 'cardiffnlp/twitter-roberta-base-sentiment'
+        tokenizer = AutoTokenizer.from_pretrained(model_name)
+        model = AutoModel.from_pretrained(model_name, output_hidden_states=True)
+        ```
+
+1.  Create the ids to be used in the model using the tokenizer object. We set the return_tensors as “pt” as we want to return the pytorch tensor of the ids:
+        ```python
+        ids = tokenizer(texts, padding=True, return_tensors="pt")
+        ```
+
+1.  Set the device to cuda, and move the model and the tokenizer to cuda as well. Since, we will be extracting embeddings, we will only be performing a forward pass of the model and hence we will set the model to validation mode using `eval()`:
+        ```python
+        device = 'cuda' if torch.cuda.is_available() else 'cpu'
+        model.to(device)
+        ids = ids.to(device)	
+        model.eval()
+        ```
+
+1.  Performing the forward pass and storing the output tuple in out:
+        ```python
+        with torch.no_grad():
+            out = model(**ids)
+        ```
+
+1.  Extracting the embeddings of each review from the last layer:
+        ```python
+        last_hidden_states = out.last_hidden_state	
+        ```
+
+1.  For the purpose of classification, we are extracting the CLS token which is the first embedding in the embedding list for each review: 
+        ```python
+        sentence_embedding = last_hidden_states[:, 0, :]
+        ```
+
+1.  We can check the shape of the final sentence embeddings for all the reviews. The output should look like `torch.Size([6, 768])`, where 6 is the batch size as we input 6 reviews as shown in step `2b`, and 768 is the embedding size of the RoBERTa model used.
+        ```python
+        print("Shape of the batch embedding: {}".format(sentence_embedding.shape))
+        ```
+
+## Prepare Sbatch file
+After saving the above code in a script called `huggingface.py`, create a file called `run.SBATCH` with the the following code:
+
+```batch
+#!/bin/bash
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=1
+#SBATCH --cpus-per-task=1
+#SBATCH --time=00:10:00
+#SBATCH --mem=64GB
+#SBATCH --gres=gpu
+#SBATCH --job-name=huggingface
+#SBATCH --output=huggingface.out
+
+module purge
+
+if [ -e /dev/nvidia0 ]; then nv="--nv"; fi
+
+singularity exec $nv \
+  --overlay /scratch/NetID/llm_example/overlay-50G-10M.ext3:rw \
+  /scratch/work/public/singularity/cuda11.2.2-cudnn8-devel-ubuntu20.04.sif \
+  /bin/bash -c "source /ext3/env.sh; python /scratch/NetID/llm_example/huggingface.py"
+```
+:::note
+You'll need to change `NetID` in the script above to your NetID.
+If you're using a different directory name and/or path you'll also need to update that in the script above.
+:::
+
+## Run the run.SBATCH file
+```batch
+[NetID@log-1 llm_example]$ sbatch run.SBATCH
+```
+The output can be found in `huggingface.out`
+It should be something like:
+```
+Some weights of RobertaModel were not initialized from the model checkpoint at cardiffnlp/twitter-roberta-base-sentiment and are
+ newly initialized: ['pooler.dense.bias', 'pooler.dense.weight']
+You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
+Shape of the batch embedding: torch.Size([6, 768])
+```
+
+## Acknowledgements
+Instructions are developed and provided by [Laiba Mehnaz](https://www.linkedin.com/in/laiba-mehnaz-a81455158/), a member of [AIfSR](https://www.linkedin.com/company/ai-for-scientific-research)