Add pipeline tag, library name, and sample usage

This PR improves the model card by adding:
- `pipeline_tag: text-generation` and `library_name: transformers` to the metadata for better discoverability and to enable the code snippet widget.
- A link to the paper on the Hugging Face Hub.
- A "Sample Usage" section based on the instructions in the official GitHub repository for serving the model and running the agent demo.
- Cleaned up the README by removing technical file metadata.

README.md

@@ -1,14 +1,16 @@
 ---
-license: apache-2.0
 base_model:
 - Qwen/Qwen3-4B
 language:
-  - en
+- en
+license: apache-2.0
 tags:
-  - agent
-  - tool-use
-  - reinforcement-learning
-  - mcp
+- agent
+- tool-use
+- reinforcement-learning
+- mcp
+pipeline_tag: text-generation
+library_name: transformers
 ---
 
 <h1 align="center">Arctic-AWM-4B</h1>
@@ -29,15 +31,31 @@ tags:
   <sup>1</sup>UNC-Chapel Hill &nbsp; <sup>2</sup>Snowflake AI Research &nbsp;
 </p>
 
-
-
 # Overview
 
-**Arctic-AWM-4B** is a multi-turn tool-use agent model trained with agentic reinforcement learning on [Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B), using the fully synthetic environments from [AgentWorldModel-1K](https://huggingface.co/datasets/Snowflake/AgentWorldModel-1K).
+**Arctic-AWM-4B** is a multi-turn tool-use agent model trained with agentic reinforcement learning on [Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B), using the fully synthetic environments from [AgentWorldModel-1K](https://huggingface.co/datasets/Snowflake/AgentWorldModel-1K). It was introduced in the paper [Agent World Model: Infinity Synthetic Environments for Agentic Reinforcement Learning](https://huggingface.co/papers/2602.10090).
 
 The model is trained to interact with tool-use environments exposed via a unified MCP (Model Context Protocol) interface, enabling strong multi-turn agentic capabilities.
 
-For detailed usage of the model, please visit [https://github.com/Snowflake-Labs/agent-world-model](https://github.com/Snowflake-Labs/agent-world-model).
+# Sample Usage
+
+To use the model for agentic tasks, you can serve it using [vLLM](https://github.com/vllm-project/vllm) and interact with it using the `awm` CLI tool.
+
+### Serve the model
+```bash
+vllm serve Snowflake/Arctic-AWM-4B --host 127.0.0.1 --port 8000
+```
+
+### Run the Agent Demo
+After starting an MCP environment (see the [GitHub repository](https://github.com/Snowflake-Labs/agent-world-model) for environment setup), you can run the agent:
+
+```bash
+awm agent \
+    --task "show me the top 10 most expensive products" \
+    --mcp_url http://localhost:8001/mcp \
+    --vllm_url http://localhost:8000/v1 \
+    --model Snowflake/Arctic-AWM-4B
+```
 
 # Resources
 
@@ -66,4 +84,4 @@ If you find this resource useful, please kindly cite:
       primaryClass={cs.AI},
       url={https://arxiv.org/abs/2602.10090}, 
 }
-```
+```