Text Generation
Transformers
TensorBoard
Safetensors
gpt2
Generated from Trainer
text-generation-inference
Instructions to use jchwenger/gpt2.shak with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jchwenger/gpt2.shak with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="jchwenger/gpt2.shak")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("jchwenger/gpt2.shak") model = AutoModelForCausalLM.from_pretrained("jchwenger/gpt2.shak", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use jchwenger/gpt2.shak with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "jchwenger/gpt2.shak" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jchwenger/gpt2.shak", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/jchwenger/gpt2.shak
- SGLang
How to use jchwenger/gpt2.shak 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 "jchwenger/gpt2.shak" \ --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": "jchwenger/gpt2.shak", "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 "jchwenger/gpt2.shak" \ --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": "jchwenger/gpt2.shak", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use jchwenger/gpt2.shak with Docker Model Runner:
docker model run hf.co/jchwenger/gpt2.shak
Download training_args.bin from jchwenger/gpt2.shak: direct link, hf CLI and curl.
- Browser
- Download file 4.54 kB
-
https://huggingface.co/jchwenger/gpt2.shak/resolve/main/training_args.bin
- Command line
-
hf download hf://jchwenger/gpt2.shak/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/jchwenger/gpt2.shak/resolve/main/training_args.bin
4.54 kB
- Xet hash:
- 8a9ad46555dfc1790d82a70d3f77c4ac6b85f7f53ebe102d13adcef956453bb7
- Size of remote file:
- 4.54 kB
- SHA256:
- be799a4631aab269996816aefa67aa7f4ce35e495f4ea414db016df93d047102
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