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

Libraries

How to use vngrs-ai/VBART-Large-Paraphrasing with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("vngrs-ai/VBART-Large-Paraphrasing")
model = AutoModelForSeq2SeqLM.from_pretrained("vngrs-ai/VBART-Large-Paraphrasing")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use vngrs-ai/VBART-Large-Paraphrasing with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vngrs-ai/VBART-Large-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-ai/VBART-Large-Paraphrasing",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/vngrs-ai/VBART-Large-Paraphrasing

SGLang

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

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

meliksahturker commited on Mar 12, 2024

Commit

4bcdf95

verified ·

1 Parent(s): 5e9a480

Upload MBartForConditionalGeneration

Browse files

Files changed (2) hide show

config.json +2 -0
model.safetensors +3 -0

config.json CHANGED Viewed

@@ -1,4 +1,5 @@
 {
   "activation_dropout": 0.0,
   "activation_function": "gelu",
   "architectures": [
@@ -27,6 +28,7 @@
   "num_hidden_layers": 12,
   "pad_token_id": 0,
   "scale_embedding": false,
   "transformers_version": "4.38.2",
   "use_cache": true,
   "vocab_size": 32000

 {
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   "activation_dropout": 0.0,
   "activation_function": "gelu",
   "architectures": [
   "num_hidden_layers": 12,
   "pad_token_id": 0,
   "scale_embedding": false,
+  "torch_dtype": "float32",
   "transformers_version": "4.38.2",
   "use_cache": true,
   "vocab_size": 32000

model.safetensors ADDED Viewed

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