Instructions to use vngrs/VBART-XLarge-Paraphrasing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vngrs/VBART-XLarge-Paraphrasing with Transformers:

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

pipe = pipeline("text-generation", model="vngrs/VBART-XLarge-Paraphrasing")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("vngrs/VBART-XLarge-Paraphrasing")
model = AutoModelForSeq2SeqLM.from_pretrained("vngrs/VBART-XLarge-Paraphrasing", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use vngrs/VBART-XLarge-Paraphrasing with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/vngrs/VBART-XLarge-Paraphrasing

SGLang

How to use vngrs/VBART-XLarge-Paraphrasing 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 "vngrs/VBART-XLarge-Paraphrasing" \
    --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": "vngrs/VBART-XLarge-Paraphrasing",
		"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 "vngrs/VBART-XLarge-Paraphrasing" \
        --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": "vngrs/VBART-XLarge-Paraphrasing",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use vngrs/VBART-XLarge-Paraphrasing with Docker Model Runner:
```
docker model run hf.co/vngrs/VBART-XLarge-Paraphrasing
```

erdiari commited on Mar 18, 2024

Commit

a1cf0bf

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@@ -20,7 +20,7 @@ It outperforms its multilingual counterparts, albeit being much smaller than oth
 VBART-XLarge is created by adding extra Transformer layers between the layers of VBART-Large. Hence it was able to transfer learned weights from the smaller model while doublings its number of layers.
 VBART-XLarge improves the results compared to VBART-Large albeit in small margins.
-This repository contains fine-tuned TensorFlow and Safetensors weights of VBART for text paraphrasing task.
 - **Developed by:** [VNGRS-AI](https://vngrs.com/ai/)
 - **Model type:**  Transformer encoder-decoder based on mBART architecture
@@ -51,7 +51,7 @@ The base model is pre-trained on [vngrs-web-corpus](https://huggingface.co/datas
 The fine-tuning dataset is a mixture of [OpenSubtitles](https://huggingface.co/datasets/open_subtitles), [TED Talks (2013)](https://wit3.fbk.eu/home) and [Tatoeba](https://tatoeba.org/en/) datasets.
 ### Limitations
-This model is fine-tuned for paraphrasing tasks. It is not intended to be used in any other case and can not be fine-tuned to any other task with full performance of the base model. It is also not guaranteed that this model will work without specified prompts.
 ### Training Procedure
 Pre-trained for 30 days and for a total of 708B tokens. Finetuned for 25 epoch.

 VBART-XLarge is created by adding extra Transformer layers between the layers of VBART-Large. Hence it was able to transfer learned weights from the smaller model while doublings its number of layers.
 VBART-XLarge improves the results compared to VBART-Large albeit in small margins.
+This repository contains fine-tuned TensorFlow and Safetensors weights of VBART for sentence-level text paraphrasing task.
 - **Developed by:** [VNGRS-AI](https://vngrs.com/ai/)
 - **Model type:**  Transformer encoder-decoder based on mBART architecture
 The fine-tuning dataset is a mixture of [OpenSubtitles](https://huggingface.co/datasets/open_subtitles), [TED Talks (2013)](https://wit3.fbk.eu/home) and [Tatoeba](https://tatoeba.org/en/) datasets.
 ### Limitations
+This model is fine-tuned for paraphrasing tasks and finetuned in sentence level only. It is not intended to be used in any other case and can not be fine-tuned to any other task with full performance of the base model. It is also not guaranteed that this model will work without specified prompts.
 ### Training Procedure
 Pre-trained for 30 days and for a total of 708B tokens. Finetuned for 25 epoch.