Instructions to use Moo/kogpt2-proofreader with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Moo/kogpt2-proofreader with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Moo/kogpt2-proofreader")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Moo/kogpt2-proofreader") model = AutoModelForCausalLM.from_pretrained("Moo/kogpt2-proofreader", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Moo/kogpt2-proofreader with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Moo/kogpt2-proofreader" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Moo/kogpt2-proofreader", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Moo/kogpt2-proofreader
- SGLang
How to use Moo/kogpt2-proofreader 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 "Moo/kogpt2-proofreader" \ --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": "Moo/kogpt2-proofreader", "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 "Moo/kogpt2-proofreader" \ --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": "Moo/kogpt2-proofreader", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Moo/kogpt2-proofreader with Docker Model Runner:
docker model run hf.co/Moo/kogpt2-proofreader
File size: 1,223 Bytes
8a1e4a5 df3a61e 8a1e4a5 df3a61e 8a1e4a5 df3a61e 8a1e4a5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 | # -*- coding: utf-8 -*-
import torch
from transformers import AutoTokenizer, GPT2LMHeadModel
O_TKN = '<origin>'
C_TKN = '<correct>'
BOS = "</s>"
EOS = "</s>"
PAD = "<pad>"
MASK = '<unused0>'
SENT = '<unused1>'
def chat():
tokenizer = AutoTokenizer.from_pretrained('skt/kogpt2-base-v2',
eos_token=EOS, unk_token='<unk>',
pad_token=PAD, mask_token=MASK)
model = GPT2LMHeadModel.from_pretrained('Moo/kogpt2-proofreader')
with torch.no_grad():
while True:
q = input('원래문장: ').strip()
if q == 'quit':
break
a = ''
while True:
input_ids = torch.LongTensor(tokenizer.encode(O_TKN + q + C_TKN + a)).unsqueeze(dim=0)
pred = model(input_ids)
gen = tokenizer.convert_ids_to_tokens(
torch.argmax(
pred[0],
dim=-1).squeeze().numpy().tolist())[-1]
if gen == EOS:
break
a += gen.replace('▁', ' ')
print(f"교정: {a.strip()}")
if __name__ == "__main__":
chat()
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