Instructions to use Nanbeige/Nanbeige4.1-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Nanbeige/Nanbeige4.1-3B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Nanbeige/Nanbeige4.1-3B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Nanbeige/Nanbeige4.1-3B") model = AutoModelForCausalLM.from_pretrained("Nanbeige/Nanbeige4.1-3B", device_map="auto") 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 Settings
- vLLM
How to use Nanbeige/Nanbeige4.1-3B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Nanbeige/Nanbeige4.1-3B" # 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/Nanbeige4.1-3B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Nanbeige/Nanbeige4.1-3B
- SGLang
How to use Nanbeige/Nanbeige4.1-3B 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/Nanbeige4.1-3B" \ --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/Nanbeige4.1-3B", "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/Nanbeige4.1-3B" \ --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/Nanbeige4.1-3B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Nanbeige/Nanbeige4.1-3B with Docker Model Runner:
docker model run hf.co/Nanbeige/Nanbeige4.1-3B
Insane performance
I'm using the F16 version on LM Studio.
This is insane, haha. I think.. CN AI has super-capable AIs ready.
They always make their SOTAs perform a bit less than the very best US AI models (on purpose) because the fact this 3B can do what it does.. shows they know what to do.
https://www.youtube.com/watch?v=0EdW3871-Qg
How about tool calling capability?
The video is showing the response of the model to the prompt "Write 200 things a CEO can do". This is not a very challenging prompt.
It gets to step step 144 where it starts responding increasingly in Chinese and then stops at 150 items (so fails to generate the full 200 items)
What is impressive about this? How often do you need incredibly long lists of things and are satisfied when supplied with only 3/4 of what you asked for?
Too bad I gave my Valentine's Day Card to Trinity. Should have saved it for Nanbeige wow :P
Well, maybe not quite, but there is definitely something here.