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

README.md

@@ -1,22 +1,26 @@
 ---
-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
 ---
 
+# 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</a>
 </p>
 
 1. We explore the deep search framework in multi-knowledge-source scenarios and propose a hierarchical agentic paradigm and train with HRL;
@@ -26,3 +30,49 @@ 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!
 
+## Sample Usage
+
+You can load and use this model directly with the Hugging Face `transformers` library for basic text generation or question-answering inference. For the full HierSearch framework capabilities, please refer to the [official GitHub repository](https://github.com/plageon/HierSearch).
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_id = "zstanjj/HierSearch-Planner-Agent" # This model represents the Planner Agent.
+# Other agent models include "zstanjj/HierSearch-Local-Agent" or "zstanjj/HierSearch-Web-Agent".
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16, # Or torch.float16 depending on your hardware
+    device_map="auto" # Or specify your device, e.g., "cuda:0"
+)
+
+# Example for a question-answering interaction with the Planner Agent
+messages = [
+    {"role": "user", "content": "Explain the concept of Hierarchical Reinforcement Learning as applied in this paper."},
+]
+
+# Apply chat template and tokenize inputs
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+# Generate response
+generated_ids = model.generate(
+    model_inputs.input_ids,
+    max_new_tokens=1024, # Adjust max_new_tokens as needed for detailed answers
+    temperature=0.7,    # Adjust generation parameters for diversity
+    do_sample=True,
+    eos_token_id=tokenizer.eos_token_id, # Ensure generation stops at EOS token
+    pad_token_id=tokenizer.pad_token_id # Set pad_token_id for proper generation
+)
+
+# Decode and print the output
+decoded_output = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(decoded_output)
+```