Instructions to use cornelliusyudhawijaya/abstracts_to_post_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cornelliusyudhawijaya/abstracts_to_post_model with Transformers:

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

pipe = pipeline("text-generation", model="cornelliusyudhawijaya/abstracts_to_post_model")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("cornelliusyudhawijaya/abstracts_to_post_model")
model = AutoModelForSeq2SeqLM.from_pretrained("cornelliusyudhawijaya/abstracts_to_post_model")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use cornelliusyudhawijaya/abstracts_to_post_model with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/cornelliusyudhawijaya/abstracts_to_post_model

SGLang

How to use cornelliusyudhawijaya/abstracts_to_post_model 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 "cornelliusyudhawijaya/abstracts_to_post_model" \
    --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": "cornelliusyudhawijaya/abstracts_to_post_model",
		"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 "cornelliusyudhawijaya/abstracts_to_post_model" \
        --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": "cornelliusyudhawijaya/abstracts_to_post_model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use cornelliusyudhawijaya/abstracts_to_post_model with Docker Model Runner:
```
docker model run hf.co/cornelliusyudhawijaya/abstracts_to_post_model
```

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

Model Card

Add more information here

Example Usage

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline

tokenizer = AutoTokenizer.from_pretrained('cornelliusyudhawijaya/abstracts_to_post_model', revision=None) # Load tokenizer
model = AutoModelForSeq2SeqLM.from_pretrained('cornelliusyudhawijaya/abstracts_to_post_model', revision=None) # Load model
pipe = pipeline('text2text-generation', model=model, tokenizer=tokenizer, pad_token_id=tokenizer.pad_token_id)

inputs = ['Note that not all scientists will apply, but there may be a handful.\n\nThe abstract can be downloaded from the papers cited in the paper for use within your project. We also recommend posting the results of the experiment, using our mailing list format, on these pages.\n\nFor other papers, see How to obtain the data from your source publication in NLP.\n\nThis project was last reported with NLP 3.10.6. The journal publishes NLP 3.10.6 once every seven years.']
print(pipe(inputs, max_length=512, do_sample=False))

This model was trained with a synthetic dataset with DataDreamer 🤖💤. The synthetic dataset card and model card can be found here. The training arguments can be found here.

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Safetensors

Model size

0.2B params

Tensor type

F32

Model tree for cornelliusyudhawijaya/abstracts_to_post_model

Base model

google/t5-v1_1-base

Finetuned

(59)

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