Improve model card: Add pipeline tag, library_name, and links to paper/code

This PR enhances the model card for `AndesVL-4B-Instruct` by:
- Adding `library_name: transformers` metadata, which enables the automated "How to use" widget on the Hub, as evidenced by the `transformers` imports in the `Quick Start` section and `config.json`.
- Adding `pipeline_tag: image-text-to-text` metadata, improving discoverability for multimodal tasks, as indicated by the paper abstract and sample usage.
- Including a direct link to the Hugging Face paper page: [AndesVL Technical Report: An Efficient Mobile-side Multimodal Large Language Model](https://huggingface.co/papers/2510.11496) at the top of the model card.
- Adding a link to the official GitHub repository for the evaluation toolkit: [https://github.com/OPPO-Mente-Lab/AndesVL_Evaluation](https://github.com/OPPO-Mente-Lab/AndesVL_Evaluation).

Please review and merge this PR.

README.md

@@ -1,7 +1,15 @@
 ---
 license: apache-2.0
+library_name: transformers
+pipeline_tag: image-text-to-text
 ---
+
 # AndesVL-4B-Instruct
+
+This model is presented in the paper [AndesVL Technical Report: An Efficient Mobile-side Multimodal Large Language Model](https://huggingface.co/papers/2510.11496).
+
+The evaluation code for this model is available at: [https://github.com/OPPO-Mente-Lab/AndesVL_Evaluation](https://github.com/OPPO-Mente-Lab/AndesVL_Evaluation)
+
 AndesVL is a suite of mobile-optimized Multimodal Large Language Models (MLLMs) with **0.6B to 4B parameters**, built upon Qwen3's LLM and various visual encoders. Designed for efficient edge deployment, it achieves first-tier performance on diverse benchmarks, including those for text-rich tasks, reasoning tasks, Visual Question Answering (VQA), multi-image tasks, multilingual tasks, and GUI tasks. Its "1+N" LoRA architecture and QALFT framework facilitate efficient task adaptation and model compression, enabling a 6.7x peak decoding speedup and a 1.8 bits-per-weight compression ratio on mobile chips.
 
 Detailed model sizes and components are provided below:
@@ -60,4 +68,4 @@ If you find our work helpful, feel free to give us a cite.
 ```
 
 # Acknowledge
-We are very grateful for the efforts of the [Qwen](https://huggingface.co/Qwen), [AimV2](https://huggingface.co/apple/aimv2-large-patch14-224) and [Siglip 2](https://arxiv.org/abs/2502.14786) projects.
+We are very grateful for the efforts of the [Qwen](https://huggingface.co/Qwen), [AimV2](https://huggingface.co/apple/aimv2-large-patch14-224) and [Siglip 2](https://arxiv.org/abs/2502.14786) projects.