Instructions to use PaxiAI/Vietnamese-Tokenizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PaxiAI/Vietnamese-Tokenizer with Transformers:

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

pipe = pipeline("text-generation", model="PaxiAI/Vietnamese-Tokenizer")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("PaxiAI/Vietnamese-Tokenizer", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use PaxiAI/Vietnamese-Tokenizer with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PaxiAI/Vietnamese-Tokenizer"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PaxiAI/Vietnamese-Tokenizer",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/PaxiAI/Vietnamese-Tokenizer

SGLang

How to use PaxiAI/Vietnamese-Tokenizer 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 "PaxiAI/Vietnamese-Tokenizer" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PaxiAI/Vietnamese-Tokenizer",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "PaxiAI/Vietnamese-Tokenizer" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PaxiAI/Vietnamese-Tokenizer",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use PaxiAI/Vietnamese-Tokenizer with Docker Model Runner:
```
docker model run hf.co/PaxiAI/Vietnamese-Tokenizer
```

PaxiAI commited on 19 days ago

Commit

9dab515

verified ·

1 Parent(s): 6444ecf

Update README.md

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Files changed (1) hide show

README.md +3 -3

README.md CHANGED Viewed

@@ -14,9 +14,9 @@ tags:
 license: apache-2.0
 ---
-# VietToken
-`VietToken` is a **48,000-token Byte-level BPE tokenizer** designed primarily for Vietnamese language models trained from scratch.
 The tokenizer is optimized for Vietnamese while retaining practical coverage of English and source code. It is intended to be architecture-independent and can be used with Qwen-style, LLaMA-style, or other autoregressive language model architectures as long as the model configuration uses the same vocabulary and token IDs.
@@ -90,7 +90,7 @@ The total serialized corpus size was approximately **8 GiB**.
 from transformers import AutoTokenizer
 tokenizer = AutoTokenizer.from_pretrained(
-    "PaxiAI/VietToken",
     use_fast=True,
 )

 license: apache-2.0
 ---
+# Vietnamese-Tokenizer
+`Vietnamese-Tokenizer` is a **48,000-token Byte-level BPE tokenizer** designed primarily for Vietnamese language models trained from scratch.
 The tokenizer is optimized for Vietnamese while retaining practical coverage of English and source code. It is intended to be architecture-independent and can be used with Qwen-style, LLaMA-style, or other autoregressive language model architectures as long as the model configuration uses the same vocabulary and token IDs.
 from transformers import AutoTokenizer
 tokenizer = AutoTokenizer.from_pretrained(
+    "PaxiAI/Vietnamese-Tokenizer",
     use_fast=True,
 )