Instructions to use jed351/gpt2-rthk with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jed351/gpt2-rthk with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="jed351/gpt2-rthk")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("jed351/gpt2-rthk") model = AutoModelForCausalLM.from_pretrained("jed351/gpt2-rthk", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use jed351/gpt2-rthk with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "jed351/gpt2-rthk" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jed351/gpt2-rthk", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/jed351/gpt2-rthk
- SGLang
How to use jed351/gpt2-rthk 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 "jed351/gpt2-rthk" \ --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": "jed351/gpt2-rthk", "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 "jed351/gpt2-rthk" \ --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": "jed351/gpt2-rthk", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use jed351/gpt2-rthk with Docker Model Runner:
docker model run hf.co/jed351/gpt2-rthk
Download pytorch_model.bin from jed351/gpt2-rthk: direct link, hf CLI and curl.
- Browser
- Download file 474 MB
-
https://huggingface.co/jed351/gpt2-rthk/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jed351/gpt2-rthk/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jed351/gpt2-rthk/resolve/main/pytorch_model.bin
474 MB
- Xet hash:
- 94888c9863b8302e748d05d320c734424a6a15c06bd49b48b44cb55d048bd521
- Size of remote file:
- 474 MB
- SHA256:
- 92f3de16d9428a93f172d168d42d7bb06e44ae66dfccbaf4f8f3c60433be8a66
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.