Instructions to use CogEvol/CogEvol-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CogEvol/CogEvol-4B with Transformers:

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

pipe = pipeline("text-generation", model="CogEvol/CogEvol-4B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("CogEvol/CogEvol-4B")
model = AutoModelForMultimodalLM.from_pretrained("CogEvol/CogEvol-4B", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use CogEvol/CogEvol-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CogEvol/CogEvol-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CogEvol/CogEvol-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/CogEvol/CogEvol-4B

SGLang

How to use CogEvol/CogEvol-4B 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 "CogEvol/CogEvol-4B" \
    --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": "CogEvol/CogEvol-4B",
		"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 "CogEvol/CogEvol-4B" \
        --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": "CogEvol/CogEvol-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use CogEvol/CogEvol-4B with Docker Model Runner:
```
docker model run hf.co/CogEvol/CogEvol-4B
```

tsq2000 commited on about 24 hours ago

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@@ -37,7 +37,7 @@ If you find CogEvol useful, please cite:
 ```bibtex
 @misc{tu2026cogevolefficientreliablelearning,
       title={CogEvol: Towards Efficient and Reliable Learning Environment Generation},
-      author={Shangqing Tu and Daniel Zhang-Li and Yucheng Wang and Shiyu Gan and Yanpeng Wang and Huiqiang Rong and Mofei Chen and Shen Yang and Yini Chen and Yinuo Duan and Haoxuan Li and Binglin Liu and Ye He and Danqi Zheng and Zhanxin Hao and Yuxuan Wu and Mengting Tao and Yuqiu Liu and Jifan Yu and Juanzi Li and Bin Xu and Lei Hou and Huiqin Liu and Yu Zhang},
       year={2026},
       eprint={2608.30968},
       archivePrefix={arXiv},

 ```bibtex
 @misc{tu2026cogevolefficientreliablelearning,
       title={CogEvol: Towards Efficient and Reliable Learning Environment Generation},
+      author={Shangqing Tu and Daniel Zhang-Li and Yucheng Wang and Shiyu Gan and Yanpeng Wang and Huiqiang Rong and Mofei Chen and Shen Yang and Yini Chen and Yinuo Duan and Binglin Liu and Ye He and Danqi Zheng and Zhanxin Hao and Yuxuan Wu and Mengting Tao and Yuqiu Liu and Jifan Yu and Juanzi Li and Bin Xu and Lei Hou and Huiqin Liu and Yu Zhang},
       year={2026},
       eprint={2608.30968},
       archivePrefix={arXiv},