Improve model card: Add pipeline tag, library name, RAG tag, and sample usage

This PR enhances the model card for the HierSearch model by:

* Adding the `pipeline_tag: question-answering` to correctly classify the model's primary function in information retrieval and answering.
* Specifying `library_name: transformers` to indicate compatibility with the Hugging Face Transformers library, enabling the "Use in Transformers" button and code snippets.
* Including `retrieval-augmented-generation` in the tags for better discoverability, as the model is a deep search framework building on RAG principles.
* Adding a sample usage snippet to demonstrate how to run inference with the model using the `transformers` library.
* Adding the full BibTeX citation for proper attribution.

These updates improve the model's discoverability and usability on the Hugging Face Hub.

README.md

@@ -1,22 +1,27 @@
 ---
-license: mit
+base_model:
+- Qwen/Qwen2.5-7B-Instruct
 language:
 - en
 - zh
-base_model:
-- Qwen/Qwen2.5-7B-Instruct
+license: mit
+pipeline_tag: question-answering
+library_name: transformers
 tags:
 - biology
 - finance
 - text-generation-inference
+- retrieval-augmented-generation
 ---
 
+# HierSearch: A Hierarchical Enterprise Deep Search Framework Integrating Local and Web Searches
+
 ## Model Information
 
-We release agent model used in **HierSearch: A Hierarchical Enterprise Deep Search Framework Integrating Local and Web Searches**.
+We release the agent model used in **HierSearch: A Hierarchical Enterprise Deep Search Framework Integrating Local and Web Searches**.
 
 <p align="left">
-Useful links: 📝 <a href="https://arxiv.org/abs/2508.08088" target="_blank">Paper</a> • 🤗 <a href="https://huggingface.co/papers/2508.08088" target="_blank">Hugging Face</a> • 🧩 <a href="https://github.com/plageon/HierSearch" target="_blank">Github</a>
+Useful links: 📝 <a href="https://arxiv.org/abs/2508.08088" target="_blank">Paper (arXiv)</a> • 🤗 <a href="https://huggingface.co/papers/2508.08088" target="_blank">Paper (Hugging Face)</a> • 🧩 <a href="https://github.com/plageon/HierSearch" target="_blank">Github Repository</a>
 </p>
 
 1. We explore the deep search framework in multi-knowledge-source scenarios and propose a hierarchical agentic paradigm and train with HRL;
@@ -26,5 +31,43 @@ Useful links: 📝 <a href="https://arxiv.org/abs/2508.08088" target="_blank">Pa
 
 🌹 If you use this model, please ✨star our **[GitHub repository](https://github.com/plageon/HierSearch)** or upvote our **[paper](https://huggingface.co/papers/2508.08088)** to support us. Your star means a lot!
 
+## Usage
+
+This model is designed as a "planner agent" within the HierSearch framework, coordinating local and web searches to answer complex questions. It is based on `Qwen2.5-7B-Instruct`. You can load and use it with the `transformers` library for general text generation, or refer to the full codebase for the complete deep search functionality.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_name = "plageon/HierSearch-Planner-Agent" 
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
+
+messages = [
+    {"role": "system", "content": "You are a helpful and knowledgeable assistant specializing in enterprise search."},
+    {"role": "user", "content": "What are the main findings of the paper 'HierSearch: A Hierarchical Enterprise Deep Search Framework Integrating Local and Web Searches'?"}
+]
+
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(model_inputs.input_ids, max_new_tokens=512)
+decoded_output = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+
+print(decoded_output)
+```
 
+## Citation
 
+```bibtex
+@misc{tan2025hiersearchhierarchicalenterprisedeep,
+      title={HierSearch: A Hierarchical Enterprise Deep Search Framework Integrating Local and Web Searches}, 
+      author={Jiejun Tan and Zhicheng Dou and Yan Yu and Jiehan Cheng and Qiang Ju and Jian Xie and Ji-Rong Wen},
+      year={2025},
+      eprint={2508.08088},
+      archivePrefix={arXiv},
+      primaryClass={cs.IR},
+      url={https://arxiv.org/abs/2508.08088}, 
+}
+```