Instructions to use nmcuong/ByT5-Vi-Normalization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nmcuong/ByT5-Vi-Normalization with Transformers:

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

pipe = pipeline("text-generation", model="nmcuong/ByT5-Vi-Normalization")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("nmcuong/ByT5-Vi-Normalization")
model = AutoModelForSeq2SeqLM.from_pretrained("nmcuong/ByT5-Vi-Normalization")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use nmcuong/ByT5-Vi-Normalization with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nmcuong/ByT5-Vi-Normalization"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nmcuong/ByT5-Vi-Normalization",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/nmcuong/ByT5-Vi-Normalization

SGLang

How to use nmcuong/ByT5-Vi-Normalization 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 "nmcuong/ByT5-Vi-Normalization" \
    --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": "nmcuong/ByT5-Vi-Normalization",
		"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 "nmcuong/ByT5-Vi-Normalization" \
        --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": "nmcuong/ByT5-Vi-Normalization",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use nmcuong/ByT5-Vi-Normalization with Docker Model Runner:
```
docker model run hf.co/nmcuong/ByT5-Vi-Normalization
```

nmcuong commited on Jul 11, 2025

Commit

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1 Parent(s): 37dfa1a

Update README.md

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@@ -42,7 +42,7 @@ ByT5-Vi-Normalization is a fine-tuned version of Google's ByT5-small model, spec
 - **Test hardware:** NVIDIA RTX 4090
 - **Library:** Hugging Face Transformers
-- **Average inference time:** ~1 second per request (sentence) on GPU
 ---

 - **Test hardware:** NVIDIA RTX 4090
 - **Library:** Hugging Face Transformers
+- **Average inference time:** ~1 second per request (sentence) on GPU with Hugging Face's Transformers library and about 0.2s with CTranslate2.
 ---