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

This PR enhances the model card by:
- Adding `library_name: transformers` to the metadata, enabling the "how to use" widget on the Hub and improving library filtering.
- Adding the `science` tag for better discoverability of this scientific reasoning model.
- Including a direct link to the GitHub repository for quick access to the project's code and resources.
- Providing a Python code snippet for quick inference using the `transformers` library, making the model easier to use for researchers.

README.md

@@ -1,17 +1,25 @@
 ---
-license: llama3.1
+base_model:
+- meta-llama/Llama-3.1-8B
 datasets:
 - MegaScience/MegaScience
 language:
 - en
+license: llama3.1
 metrics:
 - accuracy
-base_model:
-- meta-llama/Llama-3.1-8B
 pipeline_tag: text-generation
+library_name: transformers
+tags:
+- science
 ---
+
 # [MegaScience: Pushing the Frontiers of Post-Training Datasets for Science Reasoning](https://arxiv.org/abs/2507.16812)
 
+**Llama3.1-8B-MegaScience** is a model fine-tuned on **MegaScience**, a large-scale mixture of high-quality open-source scientific datasets totaling 1.25 million instances, as presented in the paper "MegaScience: Pushing the Frontiers of Post-Training Datasets for Science Reasoning". The MegaScience dataset features truthful reference answers extracted from 12k university-level scientific textbooks, comprising 650k reasoning questions spanning 7 scientific disciplines. This model significantly outperforms corresponding official instruct models in average performance on scientific reasoning tasks and exhibits greater effectiveness for larger and stronger models, suggesting a scaling benefit for scientific tuning.
+
+For more details on the project, including the data curation pipeline and evaluation system, visit the [official GitHub repository](https://github.com/GAIR-NLP/lm-open-science-evaluation).
+
 ## Llama3.1-8B-MegaScience
 
 ### Training Recipe
@@ -39,6 +47,45 @@ pipeline_tag: text-generation
 <img src="https://cdn-uploads.huggingface.co/production/uploads/616bfc2b40e2f69baa1c7add/VogIpBbjfNxXFP9DfVMms.png" alt="Data Pipeline" style="width:100%;">
 </div>
 
+### Usage
+
+You can use the model with the `transformers` library:
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_id = "MegaScience/Llama3.1-8B-MegaScience"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+
+messages = [
+    {"role": "user", "content": "Explain the concept of quantum entanglement."},
+]
+
+input_ids = tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    return_tensors="pt"
+).to(model.device)
+
+outputs = model.generate(
+    input_ids,
+    max_new_tokens=512,
+    eos_token_id=tokenizer.eos_token_id,
+    do_sample=True,
+    temperature=0.7,
+    top_p=0.9
+)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+
 ## 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
@@ -51,4 +98,4 @@ 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}
 }
-```
+```