Instructions to use StarRing2022/MiLu-GPT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use StarRing2022/MiLu-GPT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="StarRing2022/MiLu-GPT")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("StarRing2022/MiLu-GPT") model = AutoModelForCausalLM.from_pretrained("StarRing2022/MiLu-GPT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use StarRing2022/MiLu-GPT with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "StarRing2022/MiLu-GPT" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "StarRing2022/MiLu-GPT", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/StarRing2022/MiLu-GPT
- SGLang
How to use StarRing2022/MiLu-GPT 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 "StarRing2022/MiLu-GPT" \ --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": "StarRing2022/MiLu-GPT", "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 "StarRing2022/MiLu-GPT" \ --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": "StarRing2022/MiLu-GPT", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use StarRing2022/MiLu-GPT with Docker Model Runner:
docker model run hf.co/StarRing2022/MiLu-GPT
Download pytorch_model.bin from StarRing2022/MiLu-GPT: direct link, hf CLI and curl.
- Browser
- Download file 326 MB
-
https://huggingface.co/StarRing2022/MiLu-GPT/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://StarRing2022/MiLu-GPT/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/StarRing2022/MiLu-GPT/resolve/main/pytorch_model.bin
326 MB
- Xet hash:
- 9427debef0549ef3745ff27e7e05e84a2830f0a91d2801b212b197aab8254282
- Size of remote file:
- 326 MB
- SHA256:
- b8b164edb982ed654b314679e8f62faec0b0ffdf50c95b2b691c4e5f366185eb
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