Instructions to use Nanbeige/Nanbeige4.2-3B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nanbeige/Nanbeige4.2-3B-Base with Transformers:

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

pipe = pipeline("text-generation", model="Nanbeige/Nanbeige4.2-3B-Base", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Nanbeige/Nanbeige4.2-3B-Base", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Nanbeige/Nanbeige4.2-3B-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Nanbeige/Nanbeige4.2-3B-Base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nanbeige/Nanbeige4.2-3B-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Nanbeige/Nanbeige4.2-3B-Base

SGLang

How to use Nanbeige/Nanbeige4.2-3B-Base 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 "Nanbeige/Nanbeige4.2-3B-Base" \
    --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": "Nanbeige/Nanbeige4.2-3B-Base",
		"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 "Nanbeige/Nanbeige4.2-3B-Base" \
        --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": "Nanbeige/Nanbeige4.2-3B-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Nanbeige/Nanbeige4.2-3B-Base with Docker Model Runner:
```
docker model run hf.co/Nanbeige/Nanbeige4.2-3B-Base
```

leran1995 commited on Aug 2

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@@ -98,15 +98,14 @@ While we place great emphasis on model safety throughout the training process, t
 # <span id="Citation">5. Citation</span>
-If you find our model useful or would like to use it in your own work, please cite this Hugging Face project:
 ```bibtex
-@misc{nanbeige2026,
-  title        = {Nanbeige4.2-3B-Base},
-  author       = {Nanbeige Team},
-  year         = {2026},
-  publisher    = {Hugging Face},
-  howpublished = {\url{https://huggingface.co/Nanbeige/Nanbeige4.2-3B-Base}}
 }
 ```

 # <span id="Citation">5. Citation</span>
+If you find our model useful or would like to use it in your own work, please cite as follows:
 ```bibtex
+@article{lab2026nanbeige4,
+  title={Nanbeige4. 2-3B: Unlocking Agentic Capabilities in a Compact Mode},
+  author={Lab, Nanbeige and Yang, Chen and Huang, Chengrui and Lan, Fufeng and Chen, Hanhui and Zhou, Hao and Song, Huatong and Cao, Jiaqi and Zhu, Jiaying and Niu, Jinlin and others},
+  journal={arXiv preprint arXiv:2607.22083},
+  year={2026}
 }
 ```