Instructions to use paust/pko-chat-t5-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use paust/pko-chat-t5-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="paust/pko-chat-t5-large")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("paust/pko-chat-t5-large") model = AutoModelForSeq2SeqLM.from_pretrained("paust/pko-chat-t5-large") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use paust/pko-chat-t5-large with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "paust/pko-chat-t5-large" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "paust/pko-chat-t5-large", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/paust/pko-chat-t5-large
- SGLang
How to use paust/pko-chat-t5-large 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 "paust/pko-chat-t5-large" \ --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": "paust/pko-chat-t5-large", "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 "paust/pko-chat-t5-large" \ --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": "paust/pko-chat-t5-large", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use paust/pko-chat-t5-large with Docker Model Runner:
docker model run hf.co/paust/pko-chat-t5-large
YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
Chat T5
Chat T5 는 pko-flan-t5-large 를 기반으로 만들었습니다.
KoAlpaca 에서 제공하는 데이터셋과 evolve-instruct 에서 제공하는 데이터셋을 학습했습니다. 좋은 데이터를 공개해주셔서 감사합니다.
Model
Example
from transformers import T5TokenizerFast, T5ForConditionalGeneration
tokenizer = T5TokenizerFast.from_pretrained("paust/pko-chat-t5-large")
model = T5ForConditionalGeneration.from_pretrained("paust/pko-chat-t5-large", device_map='cuda')
prompt_tpl = "사용자가 한 말을 읽고 그에 질문에 답하거나 명령에 응답하는 비서입니다.\n\n사용자:\n{text}\n\n비서:\n"
prompt = prompt_tpl.format(text="한국의 수도는 어디인가요?")
input_ids = tokenizer(prompt, return_tensors='pt').input_ids
logits = model.generate(
input_ids,
max_new_tokens=1024,
temperature=0.5,
no_repeat_ngram_size=6,
do_sample=True,
num_return_sequences=1,
)
text = tokenizer.batch_decode(logits, skip_special_tokens=True)[0]
print(text) # 한국의 수도는 서울입니다.
License
PAUST에서 만든 pko-t5는 MIT license 하에 공개되어 있습니다.
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