Instructions to use sunzeyeah/pangu-350M-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sunzeyeah/pangu-350M-sft with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="sunzeyeah/pangu-350M-sft", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("sunzeyeah/pangu-350M-sft", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use sunzeyeah/pangu-350M-sft with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "sunzeyeah/pangu-350M-sft" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "sunzeyeah/pangu-350M-sft", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/sunzeyeah/pangu-350M-sft
- SGLang
How to use sunzeyeah/pangu-350M-sft 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 "sunzeyeah/pangu-350M-sft" \ --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": "sunzeyeah/pangu-350M-sft", "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 "sunzeyeah/pangu-350M-sft" \ --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": "sunzeyeah/pangu-350M-sft", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use sunzeyeah/pangu-350M-sft with Docker Model Runner:
docker model run hf.co/sunzeyeah/pangu-350M-sft
Download training_args.bin from sunzeyeah/pangu-350M-sft: direct link, hf CLI and curl.
- Browser
- Download file 3.57 kB
-
https://huggingface.co/sunzeyeah/pangu-350M-sft/resolve/main/training_args.bin
- Command line
-
hf download hf://sunzeyeah/pangu-350M-sft/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/sunzeyeah/pangu-350M-sft/resolve/main/training_args.bin
3.57 kB
- Xet hash:
- fdab4887bfb1b07f057c629cc284d3256113f32895808f597b2c678054e3e852
- Size of remote file:
- 3.57 kB
- SHA256:
- 31274bab401fb8fcc7d5aabbb3c7b4481347c7c2d1466ecb0edb1ac397113da5
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