初始化项目，由ModelHub XC社区提供模型

Model: MegaScience/Llama3.1-8B-MegaScience
Source: Original Platform

README.md

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+---
+base_model:
+- meta-llama/Llama-3.1-8B
+datasets:
+- MegaScience/MegaScience
+language:
+- en
+license: llama3.1
+metrics:
+- accuracy
+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
+
+- **LR**: 5e-6
+- **LR Schedule**: Cosine
+- **Batch Size**: 512
+- **Max Length**: 4,096
+- **Warm Up Ratio**: 0.05
+- **Epochs**: 3
+
+### Evaluation Results
+
+<div style="display: flex; justify-content: left; gap: 20px;">
+<img src="https://cdn-uploads.huggingface.co/production/uploads/616bfc2b40e2f69baa1c7add/abIVZ2XB9D-o-TCyvOkDE.png" alt="Data Pipeline" style="width:80%;">
+</div>
+
+<div style="display: flex; justify-content: left; gap: 20px;">
+<img src="https://cdn-uploads.huggingface.co/production/uploads/616bfc2b40e2f69baa1c7add/xFTJ7nevc3S4UYJxUS7ue.png" alt="Data Pipeline" style="width:80%;">
+</div>
+
+### More about MegaScience
+
+<div style="display: flex; justify-content: left; gap: 20px;">
+<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
+
+```
+@article{fan2025megascience,
+  title={MegaScience: Pushing the Frontiers of Post-Training Datasets for Science Reasoning},
+  author={Fan, Run-Ze and Wang, Zengzhi and Liu, Pengfei},
+  year={2025},
+  journal={arXiv preprint arXiv:2507.16812},
+  url={https://arxiv.org/abs/2507.16812}
+}
+```