Instructions to use MegaScience/Qwen2.5-7B-MegaScience with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MegaScience/Qwen2.5-7B-MegaScience with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="MegaScience/Qwen2.5-7B-MegaScience")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MegaScience/Qwen2.5-7B-MegaScience")
model = AutoModelForCausalLM.from_pretrained("MegaScience/Qwen2.5-7B-MegaScience")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MegaScience/Qwen2.5-7B-MegaScience with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MegaScience/Qwen2.5-7B-MegaScience"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MegaScience/Qwen2.5-7B-MegaScience",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MegaScience/Qwen2.5-7B-MegaScience

SGLang

How to use MegaScience/Qwen2.5-7B-MegaScience with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "MegaScience/Qwen2.5-7B-MegaScience" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MegaScience/Qwen2.5-7B-MegaScience",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "MegaScience/Qwen2.5-7B-MegaScience" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MegaScience/Qwen2.5-7B-MegaScience",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MegaScience/Qwen2.5-7B-MegaScience with Docker Model Runner:
```
docker model run hf.co/MegaScience/Qwen2.5-7B-MegaScience
```

Vfrz

nielsr HF Staff commited on Jul 24, 2025

Commit

41cb964

verified ·

1 Parent(s): 398234b

Improve model card: Add library_name, GitHub link, abstract, and usage example (#1)

Browse files

- Improve model card: Add library_name, GitHub link, abstract, and usage example (de00b8dcf3d3e71a5c562319ae31639b6e55390d)

Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

Files changed (1) hide show

README.md +42 -4

README.md CHANGED Viewed

@@ -1,17 +1,23 @@
 ---
-license: apache-2.0
 datasets:
 - MegaScience/MegaScience
 language:
 - en
 metrics:
 - accuracy
-base_model:
-- Qwen/Qwen2.5-7B
 pipeline_tag: text-generation
 ---
 # [MegaScience: Pushing the Frontiers of Post-Training Datasets for Science Reasoning](https://arxiv.org/abs/2507.16812)
 ## Qwen2.5-7B-MegaScience
 ### Training Recipe
@@ -33,6 +39,38 @@ pipeline_tag: text-generation
 <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;">
@@ -51,4 +89,4 @@ 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}
 }
-```

 ---
+base_model:
+- Qwen/Qwen2.5-7B
 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](https://arxiv.org/abs/2507.16812)
+Scientific reasoning is critical for developing AI scientists and supporting human researchers in advancing the frontiers of natural science discovery. This work introduces **TextbookReasoning**, an open dataset featuring truthful reference answers extracted from 12k university-level scientific textbooks, comprising 650k reasoning questions. It further presents **MegaScience**, a large-scale mixture of high-quality open-source datasets totaling 1.25 million instances, developed through systematic ablation studies. Models trained on MegaScience demonstrate superior performance and training efficiency, significantly outperforming corresponding official instruct models, especially for larger and stronger base models.
+Find the code and more details on the [MegaScience GitHub repository](https://github.com/GAIR-NLP/lm-open-science-evaluation).
 ## Qwen2.5-7B-MegaScience
 ### Training Recipe
 <img src="https://cdn-uploads.huggingface.co/production/uploads/616bfc2b40e2f69baa1c7add/xFTJ7nevc3S4UYJxUS7ue.png" alt="Data Pipeline" style="width:80%;">
 </div>
+## Quickstart
+You can use this model directly with the `transformers` library:
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+model_id = "MegaScience/Qwen2.5-7B-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": "user", "content": "What is the capital of France?"},
+]
+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=256
+)
+generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(generated_text)
+```
 ### More about MegaScience
 <div style="display: flex; justify-content: left; gap: 20px;">
   journal={arXiv preprint arXiv:2507.16812},
   url={https://arxiv.org/abs/2507.16812}
 }
+```