Remove fine tuning lab due to pivots in Docker Offload

content/guides/lab-fine-tuning.md

@@ -1,56 +0,0 @@
----
-title: "Lab: Fine-Tuning Local Models"
-linkTitle: "Lab: Fine-Tuning Models"
-description: |
-  Fine-tune AI models using Docker Offload, Docker Model Runner, and Unsloth
-  in this hands-on interactive lab.
-summary: |
-  Hands-on lab: Fine-tune, validate, and share custom AI models using Docker
-  Offload, Unsloth, and Docker Model Runner.
-keywords: AI, Docker, fine-tuning, Docker Offload, Unsloth, Model Runner, lab, labspace
-aliases:
-  - /labs/docker-for-ai/fine-tuning/
-params:
-  tags: [ai, labs]
-  time: 20 minutes
-  resource_links:
-    - title: Docker Model Runner docs
-      url: /ai/model-runner/
-    - title: Labspace repository
-      url: https://github.com/dockersamples/labspace-fine-tuning
----
-
-This lab provides a hands-on walkthrough of fine-tuning AI models using Docker
-Offload, Docker Model Runner, and Unsloth. Learn how to customize models for
-your specific use case, validate the results, and share them via Docker Hub.
-
-## What you'll learn
-
-- Use Docker Offload to fine-tune a model with GPU acceleration
-- Package and share the fine-tuned model on Docker Hub
-- Run the custom model with Docker Model Runner
-- Understand the end-to-end workflow from training to deployment
-
-## Modules
-
-| # | Module | Description |
-|---|--------|-------------|
-| 1 | Introduction | Overview of fine-tuning concepts and the Docker AI stack |
-| 2 | Fine-Tuning with Docker Offload | Run fine-tuning using Unsloth and Docker Offload |
-| 3 | Validate and Publish | Test the fine-tuned model and publish to Docker Hub |
-| 4 | Conclusion | Summary, key takeaways, and next steps |
-
-## Prerequisites
-
-- Docker Desktop with Docker Offload enabled
-- GPU access with Docker Offload cloud resources
-
-## Launch the lab
-
-Ensure you have Docker Offload running, then start the labspace:
-
-```console
-$ docker compose -f oci://dockersamples/labspace-fine-tuning up -d
-```
-
-Then open your browser to [http://localhost:3030](http://localhost:3030).