Instructions to use MegaScience/Qwen2.5-3B-MegaScience with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MegaScience/Qwen2.5-3B-MegaScience with Transformers:

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

pipe = pipeline("text-generation", model="MegaScience/Qwen2.5-3B-MegaScience")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MegaScience/Qwen2.5-3B-MegaScience")
model = AutoModelForCausalLM.from_pretrained("MegaScience/Qwen2.5-3B-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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use MegaScience/Qwen2.5-3B-MegaScience with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MegaScience/Qwen2.5-3B-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/Qwen2.5-3B-MegaScience",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MegaScience/Qwen2.5-3B-MegaScience

SGLang

How to use MegaScience/Qwen2.5-3B-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/Qwen2.5-3B-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/Qwen2.5-3B-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/Qwen2.5-3B-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/Qwen2.5-3B-MegaScience",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MegaScience/Qwen2.5-3B-MegaScience with Docker Model Runner:
```
docker model run hf.co/MegaScience/Qwen2.5-3B-MegaScience
```

Vfrz

nielsr HF Staff commited on Jul 24, 2025

Commit

98c8fd0

verified ·

1 Parent(s): 1563983

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

Browse files

- Improve model card: Add library_name, code link, and usage example (6ac229bc16a091103a502f680034b9ae87efbbba)

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

Files changed (1) hide show

README.md +34 -5

README.md CHANGED Viewed

@@ -1,19 +1,48 @@
 ---
-license: apache-2.0
 datasets:
 - MegaScience/MegaScience
 language:
 - en
 metrics:
 - accuracy
-base_model:
-- Qwen/Qwen2.5-3B
 pipeline_tag: text-generation
 ---
-# [MegaScience: Pushing the Frontiers of Post-Training Datasets for Science Reasoning](https://arxiv.org/abs/2507.16812)
 ## Qwen2.5-3B-MegaScience
 ### Training Recipe
 - **LR**: 5e-6
@@ -51,4 +80,4 @@ 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/Qwen2.5-3B
 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://huggingface.co/papers/2507.16812)
+This repository contains the `Qwen2.5-3B-MegaScience` model, one of the models trained as part of the MegaScience project.
+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).
 ## Qwen2.5-3B-MegaScience
+### Usage
+You can use this model with the Hugging Face `transformers` library:
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model_name = "MegaScience/Qwen2.5-3B-MegaScience"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+# Example text generation
+prompt = "The capital of France is"
+messages = [
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+model_inputs = tokenizer([text], return_tensors="pt")
+generated_ids = model.generate(model_inputs.input_ids, max_new_tokens=20)
+print(tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0])
+```
 ### Training Recipe
 - **LR**: 5e-6
   journal={arXiv preprint arXiv:2507.16812},
   url={https://arxiv.org/abs/2507.16812}
 }
+```