Adding samples for files and fine tuning job

[jayesh-tanna]

Description

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• The pull request does not introduce [breaking changes]
• CHANGELOG is updated for new features, bug fixes or other significant changes.
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[Copilot]

Pull Request Overview

This PR adds sample code demonstrating how to use fine-tuning jobs with the Azure AI Projects SDK. The samples cover three fine-tuning methods: supervised learning, reinforcement learning, and Direct Preference Optimization (DPO). Both synchronous and asynchronous implementations are provided for each method.

Key Changes:

• Added six new Python sample files demonstrating fine-tuning job operations
• Included sample training and validation datasets in JSONL format
• Updated CHANGELOG to document the new samples

Reviewed Changes

Copilot reviewed 12 out of 13 changed files in this pull request and generated 7 comments.

Show a summary per file

File	Description
sample_finetuning_supervised_job_async.py	Async implementation for supervised fine-tuning with job creation, retrieval, listing, and cancellation
sample_finetuning_supervised_job.py	Synchronous implementation for supervised fine-tuning operations
sample_finetuning_reinforcement_job_async.py	Async implementation for reinforcement fine-tuning with pause/resume and checkpoint listing
sample_finetuning_reinforcement_job.py	Synchronous implementation for reinforcement fine-tuning operations
sample_finetuning_dpo_job_async.py	Async implementation for DPO fine-tuning job creation and retrieval
sample_finetuning_dpo_job.py	Synchronous implementation for DPO fine-tuning operations
validation_set.jsonl	Sample validation data with conversational prompts and responses
training_set.jsonl	Sample training data with conversational prompts and responses
dpo_validation_set.jsonl	DPO-specific validation data with preferred and non-preferred outputs
dpo_training_set.jsonl	DPO-specific training data with preferred and non-preferred outputs
countdown_valid_50.jsonl	Validation dataset for arithmetic problem-solving tasks
CHANGELOG.md	Documents the addition of fine-tuning samples