Llama2 Readme Update

[wtomin]

What does this PR do?

Adds # (feature)

Add a readme file for Llama2 inference. Listed ther performance.

Before submitting

• This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
• Did you read the contributor guideline?
• Did you make sure to update the documentation with your changes? E.g. record bug fixes or new features in What's New. Here are the
  documentation guidelines
• Did you build and run the code without any errors?
• Did you report the running environment (NPU type/MS version) and performance in the doc? (better record it for data loading, model inference, or training tasks)
• Did you write any new necessary tests?

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Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
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@xxx

[gemini-code-assist]

Summary of Changes

Hello @wtomin, 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 significantly enhances the project's support for Llama2 models by introducing dedicated documentation and configuration updates. The primary goal is to provide users with clear guidance on how to set up, run inference, and understand the performance characteristics of Llama2 models within the MindSpore ecosystem. This addition streamlines the user experience for integrating and utilizing Llama2 for various natural language processing tasks.

Highlights

• New Llama2 README: A new README file (README_llama2.md) has been added under examples/transformers/llama, providing comprehensive documentation for Llama2 inference.
• Llama2 Inference Guide: The new README includes detailed installation instructions, system requirements, a quick start guide for running Llama2 inference, and performance benchmarks.
• Auto-modeling Update: The mindone/transformers/models/auto/modeling_auto.py file has been updated to include LlamaForTokenClassification in the _TOKEN_CLASSIFICATION_MODEL_MAPPING_NAMES mapping, enhancing auto-model recognition for Llama models.

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[gemini-code-assist]

Code Review

This pull request introduces a new README file for Llama2 inference and adds a mapping for LlamaForTokenClassification. The new documentation is a valuable addition, but it contains a critical error in the code snippet due to a missing import, as well as several typos and markdown formatting issues that affect readability. Additionally, the code change in modeling_auto.py disrupts the alphabetical ordering of the dictionary, which could impact maintainability. I've provided specific suggestions to address these points.

[gemini-code-assist]

For consistency and maintainability, please add this entry in alphabetical order within the MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES dictionary. It should be placed after lilt and before longformer.