Add template for Ray Train + DeepSpeed LLM fine-tuning

[tohtana]

This PR adds a template that demonstrates how to use Ray Train with DeepSpeed.

At a high level, this template covers:

• A hands-on example of fine-tuning a language model.
• Saving and loading model checkpoints with Ray Train and DeepSpeed.
• Key DeepSpeed configurations (ZeRO stages, offloading, mixed precision).

Included artifacts:

• README.md with step-by-step instructions.
• A Jupyter notebook for interactive experimentation.
• A standalone Python script for end-to-end runs.

Tested environment: 1 CPU head node + 2 NVIDIA T4 GPU worker nodes.