Improve model card: Add Transformers library tag, code link, and usage example

This PR improves the model card by:
- Adding `library_name: transformers` to the metadata, enabling the "Use in Transformers" widget and better integration on the Hub.
- Including direct links to the Hugging Face organization page for the project and the associated GitHub code repository.
- Providing a clear Python code snippet for quick inference using the `transformers` library.

README.md

@@ -1,17 +1,67 @@
 ---
-license: apache-2.0
+base_model:
+- Qwen/Qwen3-14B-Base
 datasets:
 - MegaScience/MegaScience
 language:
 - en
+license: apache-2.0
 metrics:
 - accuracy
-base_model:
-- Qwen/Qwen3-14B-Base
 pipeline_tag: text-generation
+library_name: transformers
 ---
+
 # [MegaScience: Pushing the Frontiers of Post-Training Datasets for Science Reasoning](https://arxiv.org/abs/2507.16812)
 
+This repository contains the **Qwen3-14B-MegaScience** model, a large language model fine-tuned on the MegaScience dataset for enhanced scientific reasoning.
+
+**Project Link**: [https://huggingface.co/MegaScience](https://huggingface.co/MegaScience) (Hugging Face Organization for MegaScience project)
+**Code Repository**: [https://github.com/GAIR-NLP/lm-open-science-evaluation](https://github.com/GAIR-NLP/lm-open-science-evaluation)
+
+## Usage
+
+You can use this model with the `transformers` library for text generation:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_id = "MegaScience/Qwen3-14B-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": "system", "content": "You are a helpful and knowledgeable assistant."},
+    {"role": "user", "content": "Explain the concept of quantum entanglement in simple terms."}
+]
+
+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=512,
+    do_sample=True,
+    temperature=0.7,
+    top_p=0.9,
+    eos_token_id=tokenizer.eos_token_id,
+)
+
+response = tokenizer.decode(generated_ids[0][model_inputs.input_ids.shape[1]:], skip_special_tokens=True)
+print(response)
+```
+
 ## Qwen3-14B-MegaScience
 
 ### Training Recipe
@@ -41,7 +91,7 @@ pipeline_tag: text-generation
 
 ## Citation
 
-Check out our [paper](https://arxiv.org/abs/2507.16812) for more details. If you use our dataset or find our work useful, please cite
+If you use our dataset or find our work useful, please cite
 
 ```
 @article{fan2025megascience,
@@ -51,4 +101,4 @@ 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}
 }
-```
+```