Text Generation
Transformers
PyTorch
TensorFlow
JAX
LiteRT
Rust
Safetensors
English
gpt2
exbert
text-generation-inference
Instructions to use syf2023/gpt2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use syf2023/gpt2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="syf2023/gpt2")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("syf2023/gpt2") model = AutoModelForCausalLM.from_pretrained("syf2023/gpt2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use syf2023/gpt2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "syf2023/gpt2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "syf2023/gpt2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/syf2023/gpt2
- SGLang
How to use syf2023/gpt2 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 "syf2023/gpt2" \ --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": "syf2023/gpt2", "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 "syf2023/gpt2" \ --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": "syf2023/gpt2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use syf2023/gpt2 with Docker Model Runner:
docker model run hf.co/syf2023/gpt2
| from typing import Dict, List, Any | |
| from transformers import pipeline | |
| import holidays | |
| class EndpointHandler(): | |
| def __init__(self, path=""): | |
| self.pipeline = pipeline("text-classification",model=path) | |
| self.holidays = holidays.US() | |
| def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]: | |
| """ | |
| data args: | |
| inputs (:obj: `str`) | |
| date (:obj: `str`) | |
| Return: | |
| A :obj:`list` | `dict`: will be serialized and returned | |
| """ | |
| # get inputs | |
| inputs = data.pop("inputs",data) | |
| date = data.pop("date", None) | |
| # check if date exists and if it is a holiday | |
| if date is not None and date in self.holidays: | |
| return [{"label": "happy", "score": 1}] | |
| # run normal prediction | |
| prediction = self.pipeline(inputs) | |
| return prediction |