Improve model card: Add library_name, GitHub link, abstract, and usage example

This PR improves the model card for `MegaScience/Qwen2.5-7B-MegaScience` by:

- Adding the `library_name: transformers` metadata tag, which enables the "How to use" widget on the model page.
- Including a concise abstract of the paper to provide immediate context for users.
- Adding a direct link to the project's GitHub repository for easier access to the code and further documentation.
- Including a basic Python code snippet for text generation to demonstrate immediate usage with the `transformers` library.

README.md

@@ -1,17 +1,23 @@
 ---
-license: apache-2.0
+base_model:
+- Qwen/Qwen2.5-7B
 datasets:
 - MegaScience/MegaScience
 language:
 - en
+license: apache-2.0
 metrics:
 - accuracy
-base_model:
-- Qwen/Qwen2.5-7B
 pipeline_tag: text-generation
+library_name: transformers
 ---
+
 # [MegaScience: Pushing the Frontiers of Post-Training Datasets for Science Reasoning](https://arxiv.org/abs/2507.16812)
 
+Scientific reasoning is critical for developing AI scientists and supporting human researchers in advancing the frontiers of natural science discovery. This work introduces **TextbookReasoning**, an open dataset featuring truthful reference answers extracted from 12k university-level scientific textbooks, comprising 650k reasoning questions. It further presents **MegaScience**, a large-scale mixture of high-quality open-source datasets totaling 1.25 million instances, developed through systematic ablation studies. Models trained on MegaScience demonstrate superior performance and training efficiency, significantly outperforming corresponding official instruct models, especially for larger and stronger base models.
+
+Find the code and more details on the [MegaScience GitHub repository](https://github.com/GAIR-NLP/lm-open-science-evaluation).
+
 ## Qwen2.5-7B-MegaScience
 
 ### Training Recipe
@@ -33,6 +39,38 @@ pipeline_tag: text-generation
 <img src="https://cdn-uploads.huggingface.co/production/uploads/616bfc2b40e2f69baa1c7add/xFTJ7nevc3S4UYJxUS7ue.png" alt="Data Pipeline" style="width:80%;">
 </div>
 
+## Quickstart
+
+You can use this model directly with the `transformers` library:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_id = "MegaScience/Qwen2.5-7B-MegaScience"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16, # or torch.float16 if bfloat16 is not supported
+    device_map="auto"
+)
+
+messages = [
+    {"role": "user", "content": "What is the capital of France?"},
+]
+
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+model_inputs = tokenizer(text, return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    model_inputs.input_ids,
+    max_new_tokens=256
+)
+generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(generated_text)
+```
+
 ### More about MegaScience
 
 <div style="display: flex; justify-content: left; gap: 20px;">
@@ -51,4 +89,4 @@ 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}
 }
-```
+```