Improve model card: Add library_name, code link, and usage example

README.md

@@ -1,19 +1,48 @@
 ---
-license: apache-2.0
+base_model:
+- Qwen/Qwen2.5-3B
 datasets:
 - MegaScience/MegaScience
 language:
 - en
+license: apache-2.0
 metrics:
 - accuracy
-base_model:
-- Qwen/Qwen2.5-3B
 pipeline_tag: text-generation
+library_name: transformers
 ---
-# [MegaScience: Pushing the Frontiers of Post-Training Datasets for Science Reasoning](https://arxiv.org/abs/2507.16812)
+
+# [MegaScience: Pushing the Frontiers of Post-Training Datasets for Science Reasoning](https://huggingface.co/papers/2507.16812)
+
+This repository contains the `Qwen2.5-3B-MegaScience` model, one of the models trained as part of the MegaScience project.
+
+For the official code, data processing pipeline, and evaluation system, please refer to the [MegaScience GitHub repository](https://github.com/GAIR-NLP/lm-open-science-evaluation).
 
 ## Qwen2.5-3B-MegaScience
 
+### Usage
+
+You can use this model with the Hugging Face `transformers` library:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "MegaScience/Qwen2.5-3B-MegaScience"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Example text generation
+prompt = "The capital of France is"
+messages = [
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+model_inputs = tokenizer([text], return_tensors="pt")
+
+generated_ids = model.generate(model_inputs.input_ids, max_new_tokens=20)
+print(tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0])
+```
+
 ### Training Recipe
 
 - **LR**: 5e-6
@@ -51,4 +80,4 @@ Check out our [paper](https://arxiv.org/abs/2507.16812) for more details. If you
   journal={arXiv preprint arXiv:2507.16812},
   url={https://arxiv.org/abs/2507.16812}
 }
-```
+```