Add paper and code links to model card

Hi! I'm Niels, part of the community science team at Hugging Face.

This PR improves the model card for ZipRerank by adding direct links to the research paper "[Very Efficient Listwise Multimodal Reranking for Long Documents](https://huggingface.co/papers/2605.11864)" and the official [GitHub repository](https://github.com/dukesun99/ZipRerank).

Providing these links helps users easily access the technical background and the source code for the model's training framework. I have preserved the existing technical documentation and code snippets already present in the README.

README.md

@@ -1,29 +1,27 @@
 ---
+base_model: Qwen/Qwen3-VL-8B-Instruct
+language:
+- en
 library_name: transformers
-pipeline_tag: visual-document-retrieval
 license: apache-2.0
-base_model: Qwen/Qwen3-VL-8B-Instruct
+pipeline_tag: visual-document-retrieval
 tags:
-  - reranker
-  - rerank
-  - listwise-reranker
-  - visual-document-retrieval
-  - multimodal
-  - document-understanding
-  - qwen3-vl
-  - rankgpt
-  - mmdocir
-language:
-  - en
+- reranker
+- rerank
+- listwise-reranker
+- visual-document-retrieval
+- multimodal
+- document-understanding
+- qwen3-vl
+- rankgpt
+- mmdocir
 ---
 
 # ZipRerank
 
-**ZipRerank** is a **listwise reranker for visual documents**, built on top of
-[`Qwen/Qwen3-VL-8B-Instruct`](https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct).
+**ZipRerank** is a **listwise reranker for visual documents**, introduced in the paper [Very Efficient Listwise Multimodal Reranking for Long Documents](https://huggingface.co/papers/2605.11864). The official implementation is available on [GitHub](https://github.com/dukesun99/ZipRerank).
 
-Given a text query and a set of document page images (typically rendered from a PDF),
-ZipRerank scores every page and returns them ordered from most to least relevant.
+Built on top of [`Qwen/Qwen3-VL-8B-Instruct`](https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct), ZipRerank is designed for high-efficiency multimodal reranking. Given a text query and a set of document page images (typically rendered from a PDF), the model scores every page and returns them ordered from most to least relevant in a single forward pass.
 
 ZipRerank can be used either as:
 
@@ -116,7 +114,8 @@ def create_ranking_prompt(query: str, num_passages: int) -> str:
         "The output format should be [A] > [B], etc.",
         "Only output the ranking results, do not say anything else.",
     ]
-    return "\n".join(lines)
+    return "
+".join(lines)
 
 
 @torch.no_grad()
@@ -239,4 +238,4 @@ top-K pages with ZipRerank.
 - Training focused on English documents; multilingual performance has not been evaluated,
   so results on non-English content may vary.
 - The window size is capped at 20 pages per forward pass (letters `A`–`T`); longer documents
-  rely on the sliding-window procedure described above.
+  rely on the sliding-window procedure described above.