metadata

base_model:
  - Qwen/Qwen3-14B-Base
datasets:
  - MegaScience/MegaScience
language:
  - en
license: apache-2.0
metrics:
  - accuracy
pipeline_tag: text-generation
library_name: transformers

MegaScience: Pushing the Frontiers of Post-Training Datasets for Science Reasoning

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 (Hugging Face Organization for MegaScience project)

Code Repository: https://github.com/GAIR-NLP/lm-open-science-evaluation

Usage

You can use this model with the transformers library for text generation:

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

LR: 5e-6
LR Schedule: Cosine
Batch Size: 512
Max Length: 4,096
Warm Up Ratio: 0.05
Epochs: 3

Evaluation Results

More about MegaScience

Citation

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}
}