Improve model card: Add `library_name` and usage example

README.md

@@ -1,17 +1,52 @@
 ---
-license: apache-2.0
 language:
 - en
 - zh
+license: apache-2.0
 pipeline_tag: text-generation
+library_name: transformers
 ---
 
 # BlockFFN-3B-SFT-EAGLE
 
 This is the 3B BlockFFN model used in the paper *BlockFFN: Towards End-Side Acceleration-Friendly Mixture-of-Experts with Chunk-Level Activation Sparsity* for acceleration tests.
-It is directly adaptable to the `inference` implementation of our [codes](https://github.com/thunlp/BlockFFN).
 
-Links: [[Paper](https://arxiv.org/pdf/2507.08771)] [[Codes](https://github.com/thunlp/BlockFFN)]
+**BlockFFN** introduces a novel Mixture-of-Experts (MoE) architecture designed for efficient inference, particularly on end-side devices. It aims to achieve high token-level and chunk-level sparsity, making it acceleration-friendly and compatible with techniques like speculative decoding. This model is based on the [paper](https://arxiv.org/pdf/2507.08771).
+
+For the full codebase and more details, visit the official [GitHub repository](https://github.com/thunlp/BlockFFN).
+
+### Usage
+
+You can easily load and use this model with the Hugging Face `transformers` library:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+import torch
+
+model_name = "SparseLLM/BlockFFN-3B-SFT-EAGLE"
+
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.bfloat16, # or torch.float16 if bfloat16 is not supported
+    device_map="auto",
+    trust_remote_code=True,
+)
+
+# Create a text generation pipeline
+pipe = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+)
+
+# Generate text
+prompt = "The quick brown fox jumps over the lazy"
+output = pipe(prompt, max_new_tokens=50, do_sample=True, temperature=0.7)
+
+print(output[0]['generated_text'])
+```
 
 ### Citation
 
@@ -19,10 +54,9 @@ If you find our work useful for your research, please kindly cite our paper as f
 
 ```
 @article{song2025blockffn,
-      title={{BlockFFN}: Towards End-Side Acceleration-Friendly Mixture-of-Experts with Chunk-Level Activation Sparsity}, 
+      title={{BlockFFN}: Towards End-Side Acceleration-Friendly Mixture-of-Experts with Chunk-Level Activation Sparsity},
       author={Chenyang Song and Weilin Zhao and Xu Han and Chaojun Xiao and Yingfa Chen and Yuxuan Li and Zhiyuan Liu and Maosong Sun},
       journal={arXiv preprint arXiv:2507.08771},
       year={2025},
-      url={https://arxiv.org/pdf/2507.08771}, 
-}
-```
+      url={https://arxiv.org/pdf/2507.08771},
+}