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

Libraries

How to use CCB/abstracts_to_tweet_model with Transformers:

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

pipe = pipeline("text-generation", model="CCB/abstracts_to_tweet_model")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("CCB/abstracts_to_tweet_model")
model = AutoModelForSeq2SeqLM.from_pretrained("CCB/abstracts_to_tweet_model")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use CCB/abstracts_to_tweet_model with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/CCB/abstracts_to_tweet_model

SGLang

How to use CCB/abstracts_to_tweet_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 "CCB/abstracts_to_tweet_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": "CCB/abstracts_to_tweet_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 "CCB/abstracts_to_tweet_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": "CCB/abstracts_to_tweet_model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use CCB/abstracts_to_tweet_model with Docker Model Runner:
```
docker model run hf.co/CCB/abstracts_to_tweet_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('CCB/abstracts_to_tweet_model', revision=None) # Load tokenizer
model = AutoModelForSeq2SeqLM.from_pretrained('CCB/abstracts_to_tweet_model', revision=None) # Load model
pipe = pipeline('text2text-generation', model=model, tokenizer=tokenizer, pad_token_id=tokenizer.pad_token_id)

inputs = ['In this paper, we present a novel method for Natural Language Processing (NLP) based on the introduction of deep learning techniques adapted to linguistics. We demonstrate that by integrating syntactic and semantic analysis in pre-processing stages, superior text understanding can be facilitated. Initial processes involve tokenization, POS-tagging, syntactic-semantic hinging for all corpus. To further the learning precision, we introduce a framework powered by a hybrid of Transformer and Recurrent Neural Networks architectures that manifest in increased efficiency both theoretically and empirically. This paper shares exhaustive results, detailing improvements in feature engineering, promising a reduction in human-size semantic labor. We additionally propose that integrating deep learning methods with traditional linguistics dramatically improves contextual understanding and performance on tasks such as language translation, sentiment analysis, and automated thesaurus generation. The innovations reported here make significant strides towards realizing viable, sophisticated machine-level NLP systems. Additionally, the research represents groundwork for further exploration and development promising higher degrees of culture-language contextuality and robustness integral in future NLP applications.']
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

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