Add pipeline tag, library name, and sample usage

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

I've noticed this repository belongs to the **RecursiveMAS** project. This PR aims to improve the model card by adding:
- Metadata tags (`pipeline_tag` and `library_name`) to make the model more discoverable and enable the "Use in Transformers" button.
- A sample usage section based on your GitHub README to show how to load the system.
- Refined Markdown for better readability while maintaining all original links and citations.

Please let me know if you have any questions!

README.md

@@ -2,26 +2,49 @@
 base_model:
 - meta-llama/Llama-3.2-3B-Instruct
 license: mit
+pipeline_tag: text-generation
+library_name: transformers
 ---
 
 # Sequential-Scaled-Critic-Llama3.2-3B
 
 [🌐 Project Page](https://recursivemas.github.io) | [💻 Code](https://github.com/RecursiveMAS/RecursiveMAS) | [📄 Paper](https://arxiv.org/abs/2604.25917)
 
-We introduce RecursiveMAS, a multi-agent framework that scales agent collaboration through latent-space recursion.
-RecursiveMAS treats a multi-agent system as a unified recursive computation, where heterogeneous agents iteratively exchange, refine, and evolve their latent states across recursion rounds. In the Sequential-Scaled setting, the Critic Agent is responsible for reviewing and refining the initial plan produced by the Planner Agent, which is then passed to the Solver Agent for final solution generation.
+**RecursiveMAS** is a multi-agent framework that scales agent collaboration through **latent-space recursion**. It treats a multi-agent system as a unified recursive computation, where heterogeneous agents iteratively exchange, refine, and evolve their latent states across recursion rounds. 
+
+In the **Sequential-Scaled** setting, this **Critic Agent** is responsible for reviewing and refining the initial plan produced by the Planner Agent, which is then passed to the Solver Agent for final solution generation.
 
 ## Model Details
 
 | Item | Description |
 |---|---|
-| Model | Sequential-Scaled-Critic-Llama3.2-3B |
-| Collaboration Style | Sequential-Scaled |
-| Agent Role | Critic Agent |
-| Base Model | Llama3.2-3B-Instruct |
+| **Model** | Sequential-Scaled-Critic-Llama3.2-3B |
+| **Collaboration Style** | Sequential-Scaled |
+| **Agent Role** | Critic Agent |
+| **Base Model** | [Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) |
+
+⚠️ **Note:** This checkpoint is a **role-specific agent** in [RecursiveMAS](https://arxiv.org/abs/2604.25917), rather than a standalone model intended for plain-text generation. 
+
+## Usage
+
+To use this model within the RecursiveMAS framework, you can load the entire multi-agent system as follows:
+
+```python
+from system_loader import load_mas_system
+
+# Load the whole MAS pipeline
+mas = load_mas_system(
+    style="sequential_scaled",
+    device="cuda",
+    trust_remote_code=True,
+)
+
+planner = mas.agents["planner"].model
+critic = mas.agents["critic"].model
+solver = mas.agents["solver"].model
+```
 
-⚠️ **Note:** This checkpoint is a **role-specific agent** in [**RecursiveMAS**](https://arxiv.org/abs/2604.25917), rather than a standalone model intended for plain-text generation.  
-For detailed usage instructions, please refer to our [GitHub repository](https://github.com/RecursiveMAS/RecursiveMAS).
+For detailed setup and inference instructions, please refer to the [GitHub repository](https://github.com/RecursiveMAS/RecursiveMAS).
 
 ## Model Collections for RecursiveMAS