Text Generation
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PyTorch
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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
Download tokenizer.model from Nanbeige/Nanbeige4-3B-Base: direct link, hf CLI and curl.
- Browser
- Download file 2.78 MB
-
https://huggingface.co/Nanbeige/Nanbeige4-3B-Base/resolve/main/tokenizer.model
- Command line
-
hf download hf://Nanbeige/Nanbeige4-3B-Base/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/Nanbeige/Nanbeige4-3B-Base/resolve/main/tokenizer.model
2.78 MB
- Xet hash:
- bacc5b30be0cd45a00b977ed564771cbee21d8b4176790f0854f62834b7ff203
- Size of remote file:
- 2.78 MB
- SHA256:
- fb41d04798b714520a9b075727b0226538b7330254299062742c50ec8374bc36
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