Instructions to use zaemyung/DElIteraTeR-PEGASUS-Multi-Sent-Revision-Generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zaemyung/DElIteraTeR-PEGASUS-Multi-Sent-Revision-Generator with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="zaemyung/DElIteraTeR-PEGASUS-Multi-Sent-Revision-Generator")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("zaemyung/DElIteraTeR-PEGASUS-Multi-Sent-Revision-Generator")
model = AutoModelForSeq2SeqLM.from_pretrained("zaemyung/DElIteraTeR-PEGASUS-Multi-Sent-Revision-Generator")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use zaemyung/DElIteraTeR-PEGASUS-Multi-Sent-Revision-Generator with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zaemyung/DElIteraTeR-PEGASUS-Multi-Sent-Revision-Generator"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zaemyung/DElIteraTeR-PEGASUS-Multi-Sent-Revision-Generator",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/zaemyung/DElIteraTeR-PEGASUS-Multi-Sent-Revision-Generator

SGLang

How to use zaemyung/DElIteraTeR-PEGASUS-Multi-Sent-Revision-Generator 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 "zaemyung/DElIteraTeR-PEGASUS-Multi-Sent-Revision-Generator" \
    --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": "zaemyung/DElIteraTeR-PEGASUS-Multi-Sent-Revision-Generator",
		"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 "zaemyung/DElIteraTeR-PEGASUS-Multi-Sent-Revision-Generator" \
        --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": "zaemyung/DElIteraTeR-PEGASUS-Multi-Sent-Revision-Generator",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use zaemyung/DElIteraTeR-PEGASUS-Multi-Sent-Revision-Generator with Docker Model Runner:
```
docker model run hf.co/zaemyung/DElIteraTeR-PEGASUS-Multi-Sent-Revision-Generator
```

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

DElIteraTeR-PEGASUS-Multi-Sent-Revision-Generator

This model was obtained by fine-tuning google/pegasus-large on IteraTeR+ multi_sent dataset.

Paper: Improving Iterative Text Revision by Learning Where to Edit from Other Revision Tasks
Authors: Zae Myung Kim, Wanyu Du, Vipul Raheja, Dhruv Kumar, and Dongyeop Kang

Usage

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("zaemyung/DElIteraTeR-PEGASUS-Multi-Sent-Revision-Generator")
model = AutoModelForSeq2SeqLM.from_pretrained("zaemyung/DElIteraTeR-PEGASUS-Multi-Sent-Revision-Generator")

before_inputs = [
    "<bos>These were known as temple rings <coherence>. They</coherence> were worn on the head, near the temples of a woman or a girl.<eos>",
    "Andrew Hendy, Hereditary Chief of the Miskitu Nation.<bos> <clarity>Proclaimed</clarity> by the Nicaraguans on the death of his cousin George V, who died on 8th November 1888.<eos> He was repudiated by many people of the Miskitu Nation and abdicated in favour of his cousin Jonathan I, on 8th March 1889. He retired to Nicaraguan territory where he became a Miskitu Jefe Inspector and River Magistrate."
]
model_inputs = tokenizer(before_inputs, return_tensors='pt', padding=True)
model_outputs = model.generate(**model_inputs, num_beams=8, max_length=1024)
after_texts = tokenizer.batch_decode(model_outputs, skip_special_tokens=True)

print(after_texts)
# 'These were known as temple rings because they were worn on the head, near the temples of a woman or a girl.',
# 'Andrew Hendy, Hereditary Chief of the Miskitu Nation. He was proclaimed by the Nicaraguans on the death of his cousin George V, who died on 8th November 1888. He was repudiated by many people of the Miskitu Nation and abdicated in favour of his cousin Jonathan I, on 8th March 1889. He retired to Nicaraguan territory where he became a Miskitu Jefe Inspector and River Magistrate.']

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Safetensors

Model size

0.6B params

Tensor type

F32