Instructions to use MegaScience/Qwen3-30B-A3B-MegaScience with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MegaScience/Qwen3-30B-A3B-MegaScience with Transformers:

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

pipe = pipeline("text-generation", model="MegaScience/Qwen3-30B-A3B-MegaScience")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MegaScience/Qwen3-30B-A3B-MegaScience")
model = AutoModelForCausalLM.from_pretrained("MegaScience/Qwen3-30B-A3B-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

vLLM

How to use MegaScience/Qwen3-30B-A3B-MegaScience with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MegaScience/Qwen3-30B-A3B-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/Qwen3-30B-A3B-MegaScience",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MegaScience/Qwen3-30B-A3B-MegaScience

SGLang

How to use MegaScience/Qwen3-30B-A3B-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/Qwen3-30B-A3B-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/Qwen3-30B-A3B-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/Qwen3-30B-A3B-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/Qwen3-30B-A3B-MegaScience",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MegaScience/Qwen3-30B-A3B-MegaScience with Docker Model Runner:
```
docker model run hf.co/MegaScience/Qwen3-30B-A3B-MegaScience
```

Vfrz commited on Jul 23, 2025

Commit

08496d3

verified ·

1 Parent(s): 7d79a0c

Update README.md

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@@ -10,7 +10,7 @@ base_model:
 - Qwen/Qwen3-30B-A3B-Base
 pipeline_tag: text-generation
 ---
-# [MegaScience: Pushing the Frontiers of Post-Training Datasets for Science Reasoning](https://arxiv.org/abs/xxx)
 ## Qwen3-30B-A3B-MegaScience
@@ -41,14 +41,14 @@ pipeline_tag: text-generation
 ## Citation
-Check out our [paper](https://arxiv.org/abs/xxx) 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:xxx},
-  url={https://arxiv.org/abs/xxx}
 }
 ```

 - Qwen/Qwen3-30B-A3B-Base
 pipeline_tag: text-generation
 ---
+# [MegaScience: Pushing the Frontiers of Post-Training Datasets for Science Reasoning](https://arxiv.org/abs/2507.16812)
 ## Qwen3-30B-A3B-MegaScience
 ## 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}
 }
 ```