From 9cfb6e17351c54ef0a70653086b1f84f0300bdc1 Mon Sep 17 00:00:00 2001
From: GitLab CI <automaton@nvidia.com>
Date: Tue, 7 Oct 2025 17:35:19 +0000
Subject: [PATCH] chore: Regenerate all playbooks

---
 nvidia/speculative-decoding/README.md | 5 ++---
 nvidia/vss/README.md                  | 5 ++---
 2 files changed, 4 insertions(+), 6 deletions(-)

diff --git a/nvidia/speculative-decoding/README.md b/nvidia/speculative-decoding/README.md
index 6da7f6f..494dd39 100644
--- a/nvidia/speculative-decoding/README.md
+++ b/nvidia/speculative-decoding/README.md
@@ -5,7 +5,7 @@
 ## Table of Contents
 
 - [Overview](#overview)
-- [Instructions](#instructions)
+- [How to run inference with speculative decoding](#how-to-run-inference-with-speculative-decoding)
   - [Step 1. Configure Docker permissions](#step-1-configure-docker-permissions)
   - [Step 2. Run Draft-Target Speculative Decoding](#step-2-run-draft-target-speculative-decoding)
   - [Step 3. Test the Draft-Target setup](#step-3-test-the-draft-target-setup)
@@ -57,7 +57,7 @@ These examples demonstrate how to accelerate large language model inference whil
 
 **Rollback:** Stop Docker containers and optionally clean up downloaded model cache
 
-## Instructions
+## How to run inference with speculative decoding
 
 ## Traditional Draft-Target Speculative Decoding
 
@@ -169,4 +169,3 @@ docker stop <container_id>
 - Experiment with different `max_draft_len` values (1, 2, 3, 4, 8)
 - Monitor token acceptance rates and throughput improvements
 - Test with different prompt lengths and generation parameters
-- Read more on Speculative Decoding [here](https://nvidia.github.io/TensorRT-LLM/advanced/speculative-decoding.html)
diff --git a/nvidia/vss/README.md b/nvidia/vss/README.md
index 849d576..223e88c 100644
--- a/nvidia/vss/README.md
+++ b/nvidia/vss/README.md
@@ -11,7 +11,7 @@
 
 ## Overview
 
-## Basic Idea
+## Basic idea
 
 Deploy NVIDIA's Video Search and Summarization (VSS) AI Blueprint to build intelligent video analytics systems that combine vision language models, large language models, and retrieval-augmented generation. The system transforms raw video content into real-time actionable insights with video summarization, Q&A, and real-time alerts. You'll set up either a completely local Event Reviewer deployment or a hybrid deployment using remote model endpoints.
 
@@ -231,7 +231,6 @@ In this hybrid deployment, we would use NIMs from [build.nvidia.com](https://bui
 **8.1 Get NVIDIA API Key**
 
 - Log in to https://build.nvidia.com/explore/discover.
-- Navigate to any NIM for example, https://build.nvidia.com/meta/llama3-70b.
 - Search for **Get API Key** on the page and click on it.
 
 **8.2 Navigate to remote LLM deployment directory**
@@ -316,7 +315,7 @@ Follow the steps [here](https://docs.nvidia.com/vss/latest/content/ui_app.html)
 
 ## Step 11. Cleanup and rollback
 
-To completely remove the VSS deployment and free up system resources.
+To completely remove the VSS deployment and free up system resources:
 
 > **Warning:** This will destroy all processed video data and analysis results.