83 lines
2.6 KiB
Markdown
83 lines
2.6 KiB
Markdown
---
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base_model:
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- Qwen/Qwen2.5-3B
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datasets:
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- MegaScience/MegaScience
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language:
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- en
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license: apache-2.0
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metrics:
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- accuracy
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pipeline_tag: text-generation
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library_name: transformers
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---
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# [MegaScience: Pushing the Frontiers of Post-Training Datasets for Science Reasoning](https://huggingface.co/papers/2507.16812)
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This repository contains the `Qwen2.5-3B-MegaScience` model, one of the models trained as part of the MegaScience project.
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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).
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## Qwen2.5-3B-MegaScience
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### Usage
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You can use this model with the Hugging Face `transformers` library:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "MegaScience/Qwen2.5-3B-MegaScience"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name)
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# Example text generation
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prompt = "The capital of France is"
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messages = [
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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model_inputs = tokenizer([text], return_tensors="pt")
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generated_ids = model.generate(model_inputs.input_ids, max_new_tokens=20)
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print(tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0])
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```
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### Training Recipe
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- **LR**: 5e-6
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- **LR Schedule**: Cosine
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- **Batch Size**: 512
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- **Max Length**: 4,096
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- **Warm Up Ratio**: 0.05
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- **Epochs**: 3
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### Evaluation Results
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<div style="display: flex; justify-content: left; gap: 20px;">
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<img src="https://cdn-uploads.huggingface.co/production/uploads/616bfc2b40e2f69baa1c7add/abIVZ2XB9D-o-TCyvOkDE.png" alt="Data Pipeline" style="width:80%;">
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</div>
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<div style="display: flex; justify-content: left; gap: 20px;">
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<img src="https://cdn-uploads.huggingface.co/production/uploads/616bfc2b40e2f69baa1c7add/xFTJ7nevc3S4UYJxUS7ue.png" alt="Data Pipeline" style="width:80%;">
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</div>
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### More about MegaScience
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<div style="display: flex; justify-content: left; gap: 20px;">
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<img src="https://cdn-uploads.huggingface.co/production/uploads/616bfc2b40e2f69baa1c7add/VogIpBbjfNxXFP9DfVMms.png" alt="Data Pipeline" style="width:100%;">
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</div>
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## Citation
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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
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```
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@article{fan2025megascience,
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title={MegaScience: Pushing the Frontiers of Post-Training Datasets for Science Reasoning},
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author={Fan, Run-Ze and Wang, Zengzhi and Liu, Pengfei},
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year={2025},
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journal={arXiv preprint arXiv:2507.16812},
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url={https://arxiv.org/abs/2507.16812}
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}
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``` |