[shell] update qwen3_omni shell

[Jintao-Huang]

No description provided.

[gemini-code-assist]

Summary of Changes

Hello @Jintao-Huang, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request introduces a new example script for fine-tuning the Qwen3-Omni multimodal model, expanding the project's capabilities for advanced model training. Concurrently, it improves user guidance by updating documentation for the model_type parameter and refines the internal model registration process to ensure problem_type is set appropriately for sequence classification tasks. Additionally, Qwen3-VL example scripts are updated with a new environment variable for image token management.

Highlights

• New Qwen3-Omni Example Script: A new shell script has been added to demonstrate fine-tuning the Qwen/Qwen3-Omni-30B-A3B-Instruct model, showcasing multimodal training configurations.
• Documentation Update for model_type: The documentation for command-line parameters, specifically for model_type, has been updated in both Chinese and English to include a reference to the supported models list for clearer guidance.
• Qwen3-VL Example Script Enhancements: The Qwen3-VL example scripts (mcore.sh and transformers.sh) now include the IMAGE_MAX_TOKEN_NUM=1024 environment variable, likely to optimize image token handling.
• Refined problem_type Assignment Logic: The problem_type assignment in the model registration logic (swift/llm/model/register.py) has been made conditional, now only applying when the task_type is seq_cls.

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[gemini-code-assist]

Code Review

This pull request introduces a new example script for fine-tuning the qwen3_omni model, updates documentation with links to supported models, and adds a configuration parameter for qwen3_vl examples. Additionally, it includes a correctness fix in swift/llm/model/register.py by ensuring that problem_type is only set for sequence classification tasks. The changes are well-implemented and improve the project's documentation, examples, and code correctness. I find no issues with the proposed changes.