ml/ai cleanup

Author: s-sajid-ali

docs/hpc/08_ml_ai_hpc/LLM Inference/01_llm_inferenceoverview.md

@@ -1,15 +1,15 @@
 # Overview
-This directory provides two primary pathways for deploying and running Large Language Models (LLMs) on the NYU Torch/Greene cluster: Hugging Face Transformers (for research/experimentation) and vLLM (for high-performance serving).
+This directory provides two primary pathways for deploying and running Large Language Models (LLMs) on the NYU Torch cluster: Hugging Face Transformers (for research/experimentation) and vLLM (for high-performance serving).
 
 ## 1. Basic Inference (Hugging Face)
-This method is ideal for feature extraction, embeddings, or small-scale batch processing. It relies on a persistent Singularity environment using an `ext3` overlay.
+This method is ideal for feature extraction, embeddings, or small-scale batch processing. Persistence is achieved by setting up the environment within an `ext3` overlay that can be mounted to an Apptainer container.
 
 **Workflow:**
 Environment: Launch an Apptainer container with a read/write overlay.
-Persistence: Install Conda/Miniforge and libraries directly into `/ext3`.
+Persistence: Install `conda` and libraries directly into `/ext3`.
 Execution: Use AutoModel to load weights and perform a forward pass.
-Key File: huggingface.py
-Ideal for: Extracting last_hidden_state (embeddings) or sentiment classification.
+Key File: `huggingface.py`
+Ideal for: Extracting `last_hidden_state` (embeddings) or sentiment classification.
 
 ## 2. High-Performance Serving (vLLM)
 vLLM is the recommended tool for production-level throughput and low-latency inference. It utilizes `PagedAttention` to manage memory efficiently. Please find our guide on [deploying LLMs with vLLM on Torch here](./03_vLLM.md).
@@ -19,5 +19,5 @@ Speed: higher throughput than standard backends on Torch.
 Compatibility: Drop-in replacement for OpenAI API.
 
 **Deployment Options:**
-Online: Use vllm serve to start an HTTP server accessible via curl or OpenAI clients.
+Online: Use `vllm serve` to start an HTTP server accessible via curl or OpenAI clients.
 Offline: Use the LLM class within Python for processing large datasets without a server.