Instructions to use jamiewjm/CCGwGPT2extep3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jamiewjm/CCGwGPT2extep3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="jamiewjm/CCGwGPT2extep3")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("jamiewjm/CCGwGPT2extep3") model = AutoModelForCausalLM.from_pretrained("jamiewjm/CCGwGPT2extep3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use jamiewjm/CCGwGPT2extep3 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "jamiewjm/CCGwGPT2extep3" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jamiewjm/CCGwGPT2extep3", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/jamiewjm/CCGwGPT2extep3
- SGLang
How to use jamiewjm/CCGwGPT2extep3 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 "jamiewjm/CCGwGPT2extep3" \ --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": "jamiewjm/CCGwGPT2extep3", "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 "jamiewjm/CCGwGPT2extep3" \ --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": "jamiewjm/CCGwGPT2extep3", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use jamiewjm/CCGwGPT2extep3 with Docker Model Runner:
docker model run hf.co/jamiewjm/CCGwGPT2extep3
Download pytorch_model.bin from jamiewjm/CCGwGPT2extep3: direct link, hf CLI and curl.
- Browser
- Download file 435 MB
-
https://huggingface.co/jamiewjm/CCGwGPT2extep3/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jamiewjm/CCGwGPT2extep3/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jamiewjm/CCGwGPT2extep3/resolve/main/pytorch_model.bin
435 MB
- Xet hash:
- ee95297108dc64d7e152514717706487cb7b70fe4db7a2208684917ad10fae82
- Size of remote file:
- 435 MB
- SHA256:
- dd13399adf2710f53e6d9856b6fd7da648c10fdd61d910812f67bd930274599d
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