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LLaVA_MORE-llama_3_1-reasoning-finetuning

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This PR significantly enhances the model card for aimagelab/LLaVA_MORE-llama_3_1-8B-finetuning by:

• Adding comprehensive metadata: Including license, pipeline_tag, detailed tags, base_model, and datasets.
• Providing a detailed model description: Elaborated from the paper's abstract and GitHub README, specifying LLM and visual backbones.
• Including essential links: Direct links to the paper, GitHub repository, and project website.
• Presenting performance benchmarks: The full evaluation table from the paper is included.
• Adding a practical "How to Get Started" code snippet: A comprehensive Python example for inference using transformers.
• Outlining training and evaluation details: Specifics on datasets, training procedure, and evaluation protocol are added.
• Adding the full BibTeX citation and Acknowledgments: To properly credit the authors and contributors.

These updates aim to make the model more discoverable, transparent, and easier for users to understand and utilize.