docs: add Ollama guide under 08_ml_ai_hpc

docs/hpc/08_ml_ai_hpc/07_ollama.md

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+# vLLM - A Command Line LLM Tool
+## What is vLLM?
+[vLLM](https://docs.vllm.ai/en/latest/) is a fast and easy-to-use library for LLM inference and serving.
+
+## Why vLLM?
+We tested vLLM and llama-cpp on Torch, and found vLLM performs better on Torch:
+Model: Qwen2.5-7B-Instruct
+Prompt Tokens:512
+Output Tokens: 256
+|Backend|Peak Throughput|Median Latency(ms)|Recommendation
+|-----|-----|-----|-----|
+|vLLM|~4689.6|~48.0|Best for Batch/Research|
+|llama-cpp|~115.0|~280.0|Best for Single User|
+
+## vLLM Installation Instructions
+Create a vLLM directory in your /scratch directory, then install the vLLM image:
+```
+apptainer pull docker://vllm/vllm-openai:latest
+```
+### Use High-Performance SCRATCH Storage
+LLMs require very fast storage. On Torch, the SCRATCH filesystem is an all-flash system designed for AI workloads, providing excellent performance.To avoid exceeding your $HOME quota (50GB) and inode limits (30,000 files), you should redirect vLLM's cache and Hugging Face's model downloads to your scratch space:
+```
+export HF_HOME=/scratch/$USER/hf_cache
+export VLLM_CACHE_ROOT=/scratch/$USER/vllm_cache
+```
+You should run this to configure vLLM to always use your SCRATCH storage for consistent use:
+```
+echo "export HF_HOME=/scratch/\$USER/hf_cache" >> ~/.bashrc
+echo "export VLLM_CACHE_ROOT=/scratch/\$USER/vllm_cache" >> ~/.bashrc
+```
+
+Note: Files on $SCRATCH are not backed up and will be deleted after 60 days of inactivity. Always keep your source code and .slurm scripts in $HOME!
+
+## Run vLLM
+### Online Serving (OpenAI-Compatible API)
+vLLM implements the OpenAI API protocol, allowing it to be a drop-in replacement for applications using OpenAI's services. By default, it starts the server at http://localhost:8000. You can specify the address with --host and --port arguments. 
+**In Terminal 1:**
+Start  vLLM server (In this example we use Qwen model):
+```
+apptainer exec --nv vllm-openai_latest.sif vllm serve "Qwen/Qwen2.5-0.5B-Instruct"
+```
+When you see:
+```
+Application startup complete.
+```
+Open another terminal and log in to the same computing node as in terminal 1.
+
+**In Terminal 2**
+```
+curl http://localhost:8000/v1/chat/completions \
+    -H "Content-Type: application/json" \
+    -d '{
+        "model": "Qwen/Qwen2.5-0.5B-Instruct",
+        "messages": [
+            {"role": "user", "content": "Your prompt..."}
+        ]
+    }'
+```
+
+### Offline Inference
+If you need to process a large dataset at once without setting up a server, you can use vLLM's LLM class.
+For example, the following code downloads the facebook/opt-125m model from HuggingFace and runs it in vLLM using the default configuration.
+```
+from vllm import LLM
+
+# Initialize the vLLM engine.
+llm = LLM(model="facebook/opt-125m")
+```
+After initializing the LLM instance, use the available APIs to perform model inference.
+
+## vLLM CLI
+The vllm command-line tool is used to run and manage vLLM models. You can start by viewing the help message with:
+```
+vllm --help
+```
+Serve - Starts the vLLM OpenAI Compatible API server.
+```
+vllm serve meta-llama/Llama-2-7b-hf
+```
+Chat - Generate chat completions via the running API server.
+```
+# Directly connect to localhost API without arguments
+vllm chat
+
+# Specify API url
+vllm chat --url http://{vllm-serve-host}:{vllm-serve-port}/v1
+
+# Quick chat with a single prompt
+vllm chat --quick "hi"
+```
+Complete - Generate text completions based on the given prompt via the running API server.
+```
+# Directly connect to localhost API without arguments
+vllm complete
+
+# Specify API url
+vllm complete --url http://{vllm-serve-host}:{vllm-serve-port}/v1
+
+# Quick complete with a single prompt
+vllm complete --quick "The future of AI is"
+```
+
+For more CLI command references: visit https://docs.vllm.ai/en/stable/cli/.