Add `library_name` and Sample Usage

This PR enhances the model card by:
- Adding `library_name: transformers` to the metadata, which enables the "how to use" widget on the model page.
- Expanding the model description with details from the paper abstract/GitHub README summary.
- Including a "Sample Usage" section with a code snippet directly from the official GitHub repository's "Use Hugging Face weights" instructions, demonstrating how to load and perform inference with the model.

These improvements will make the model more discoverable and easier to use for the community.

README.md

@@ -1,18 +1,62 @@
 ---
-license: apache-2.0
 language:
 - en
+license: apache-2.0
 pipeline_tag: text-generation
 tags:
 - agent
 - communication
 arxiv: 2510.03215
+library_name: transformers
 ---
 
-This is the C2C Fuser used by the paper [Cache-to-Cache: Direct Semantic Communication Between Large Language Models](https://huggingface.co/papers/2510.03215).
+This is the C2C Fuser, presented in the paper [Cache-to-Cache: Direct Semantic Communication Between Large Language Models](https://huggingface.co/papers/2510.03215).
 
-Cache-to-Cache (C2C) enables Large Language Models to communicate directly through their KV-Caches, bypassing text generation.
+Cache-to-Cache (C2C) enables Large Language Models to communicate directly through their KV-Caches, bypassing text generation. By projecting and fusing KV-Caches between models, C2C achieves 8.5–10.5% higher accuracy than individual models and 3.0–5.0% better performance than text-based communication, with 2.0× speedup in latency.
 
 Please visit our [GitHub repo](https://github.com/thu-nics/C2C) for more information.
 
-Project page: [https://fuvty.github.io/C2C_Project_Page/](https://fuvty.github.io/C2C_Project_Page/)
+Project page: [https://fuvty.github.io/C2C_Project_Page/](https://fuvty.github.io/C2C_Project_Page/)
+
+### Sample Usage
+
+Here's how to load the published C2C weights from the Hugging Face collection and run an inference example:
+
+```python
+import torch
+from huggingface_hub import snapshot_download
+from script.playground.inference_example import load_rosetta_model, run_inference_example
+
+checkpoint_dir = snapshot_download(
+    repo_id="nics-efc/C2C_Fuser",
+    allow_patterns=["qwen3_0.6b+qwen2.5_0.5b_Fuser/*"],
+)
+
+model_config = {
+    "rosetta_config": {
+        "base_model": "Qwen/Qwen3-0.6B",
+        "teacher_model": "Qwen/Qwen2.5-0.5B-Instruct",
+        "checkpoints_dir": f"{checkpoint_dir}/qwen3_0.6b+qwen2.5_0.5b_Fuser/final",
+    }
+}
+
+rosetta_model, tokenizer = load_rosetta_model(model_config, eval_config={}, device=torch.device("cuda"))
+device = rosetta_model.device
+
+prompt = [{"role": "user", "content": "Say hello in one short sentence."}]
+input_text = tokenizer.apply_chat_template(prompt, tokenize=False, add_generation_prompt=True, enable_thinking=False)
+inputs = tokenizer(input_text, return_tensors="pt").to(device)
+
+instruction_index = torch.tensor([1, 0], dtype=torch.long).repeat(inputs['input_ids'].shape[1] - 1, 1).unsqueeze(0).to(device)
+label_index = torch.tensor([-1, 0], dtype=torch.long).repeat(1, 1).unsqueeze(0).to(device)
+kv_cache_index = [instruction_index, label_index]
+
+with torch.no_grad():
+    sampling_params = {
+        'do_sample': False,
+        'max_new_tokens': 256
+    }
+    outputs = rosetta_model.generate(**inputs, kv_cache_index=kv_cache_index, **sampling_params)
+    output_text = tokenizer.decode(outputs[0, instruction_index.shape[1] + 1:], skip_special_tokens=True)
+    print(f"C2C output text: {output_text}")
+```