Instructions to use Inkdrop/distilgpt2-parser with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Inkdrop/distilgpt2-parser with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Inkdrop/distilgpt2-parser")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Inkdrop/distilgpt2-parser") model = AutoModelForCausalLM.from_pretrained("Inkdrop/distilgpt2-parser") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Inkdrop/distilgpt2-parser with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Inkdrop/distilgpt2-parser" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Inkdrop/distilgpt2-parser", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Inkdrop/distilgpt2-parser
- SGLang
How to use Inkdrop/distilgpt2-parser 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 "Inkdrop/distilgpt2-parser" \ --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": "Inkdrop/distilgpt2-parser", "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 "Inkdrop/distilgpt2-parser" \ --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": "Inkdrop/distilgpt2-parser", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Inkdrop/distilgpt2-parser with Docker Model Runner:
docker model run hf.co/Inkdrop/distilgpt2-parser
- Xet hash:
- a48f5cc44baab4977ddc1533b41ef2aea94a71882741d837a059ec35b930cd9f
- Size of remote file:
- 3.89 kB
- SHA256:
- 05d01b381fba99d5015de48297a40e3151d5f45edf8e62cb06835365185f1975
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.