Text Generation
Transformers
PyTorch
Safetensors
English
Chinese
llama
llm
nanbeige
text-generation-inference
Instructions to use Nanbeige/Nanbeige4-3B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Nanbeige/Nanbeige4-3B-Base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Nanbeige/Nanbeige4-3B-Base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Nanbeige/Nanbeige4-3B-Base") model = AutoModelForCausalLM.from_pretrained("Nanbeige/Nanbeige4-3B-Base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Nanbeige/Nanbeige4-3B-Base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Nanbeige/Nanbeige4-3B-Base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Nanbeige/Nanbeige4-3B-Base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Nanbeige/Nanbeige4-3B-Base
- SGLang
How to use Nanbeige/Nanbeige4-3B-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/Nanbeige4-3B-Base" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Nanbeige/Nanbeige4-3B-Base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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/Nanbeige4-3B-Base" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Nanbeige/Nanbeige4-3B-Base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Nanbeige/Nanbeige4-3B-Base with Docker Model Runner:
docker model run hf.co/Nanbeige/Nanbeige4-3B-Base
Commit History
-
Adding `safetensors` variant of this model (#2)
Verified signature- Adding `safetensors` variant of this model (e84b4981c78c35294f747543bb83b5e618a5d195) Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>8eeb734
-
Update README.md
Verified signatureb5741a2 -
Update README.md
Verified signature5cc56f5
-
Update README.md
Verified signaturebfd16d8
-
Upload pytorch_model.bin
ChenZongchaoVerified signaturecec014e -
Upload 6 files
ChenZongchaoVerified signatured3f6011 -
Delete figures/1
Verified signature004886f -
Update README.md
Verified signaturef3d3051 -
Upload 2 files
Verified signatureff13f9e -
Create figures/1
Verified signature241cac6 -
initial commit
Verified signature02d420d