feat(train): add list_hyperparameters() for pre-trainer HP discovery

[joshuatowner]

Issue #, if available:

Related to Skills AI: "Update Skills to use dynamic HP discovery instead of hardcoded parameter names"

Description of changes:

Adds list_hyperparameters() to sagemaker.train -- a public utility function that returns a FineTuningOptions object for a given model/technique/training_type without requiring a fully constructed trainer.

Problem: Today, discovering available hyperparameters requires constructing a full trainer object (SFTTrainer, DPOTrainer, etc.), which in turn requires a model package group, dataset ARN, IAM role, and S3 output path. Tools and scripts that generate training code need to know valid HP names before those resources exist.

Solution: list_hyperparameters() wraps the existing internal recipe resolution logic and returns the same FineTuningOptions object that trainer.hyperparameters provides, but without the trainer construction prerequisites.

Usage:

from sagemaker.train import list_hyperparameters

hp = list_hyperparameters("huggingface-llm-qwen2-5-7b-instruct", "SFT", "LORA")
hp.get_info()  # display all params with defaults and ranges
hp.get_info("learning_rate")  # display info for a single param

Changes:

• sagemaker-train/src/sagemaker/train/common_utils/finetune_utils.py: Added list_hyperparameters() function
• sagemaker-train/src/sagemaker/train/__init__.py: Added lazy import for list_hyperparameters
• sagemaker-train/tests/unit/train/common_utils/test_finetune_utils.py: Added 2 unit tests (basic usage, enum acceptance)

Testing:

• Unit tests with mocked hub content and S3 responses
• Manual validation against live SageMakerPublicHub in us-west-2 and us-east-1 across 6 models (Qwen 2.5, Qwen 3, Llama 3.1, DeepSeek R1 Distill) and 4 techniques (SFT, DPO, RLVR, RLAIF). 15/16 cases pass; one failure is an S3 access gate on a subscription model (not our code).

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