Instructions to use mrcha033/YunMin-tokenizer-96k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mrcha033/YunMin-tokenizer-96k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mrcha033/YunMin-tokenizer-96k")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mrcha033/YunMin-tokenizer-96k", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use mrcha033/YunMin-tokenizer-96k with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "mrcha033/YunMin-tokenizer-96k" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mrcha033/YunMin-tokenizer-96k", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/mrcha033/YunMin-tokenizer-96k
- SGLang
How to use mrcha033/YunMin-tokenizer-96k 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 "mrcha033/YunMin-tokenizer-96k" \ --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": "mrcha033/YunMin-tokenizer-96k", "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 "mrcha033/YunMin-tokenizer-96k" \ --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": "mrcha033/YunMin-tokenizer-96k", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use mrcha033/YunMin-tokenizer-96k with Docker Model Runner:
docker model run hf.co/mrcha033/YunMin-tokenizer-96k
YunMin Korean Tokenizer (96k vocab)
A Korean language tokenizer with 96,000 vocabulary size, optimized for Korean text processing.
Files Description
YunMin-tokenizer-96k.model- SentencePiece model file (2.0MB)YunMin-tokenizer-96k.vocab- Vocabulary file (2.0MB)tokenizer.json- Hugging Face tokenizer configurationtokenizer_config.json- Tokenizer configuration parametersspecial_tokens_map.json- Special tokens mappingconfig.json- Model configuration
Usage
From Hugging Face Hub
from transformers import PreTrainedTokenizerFast
# Load the tokenizer from Hugging Face Hub
tokenizer = PreTrainedTokenizerFast.from_pretrained("mrcha033/YunMin-tokenizer-96k")
# Tokenize Korean text
text = "μλ
νμΈμ, νκ΅μ΄ ν ν¬λμ΄μ μ
λλ€."
tokens = tokenizer.tokenize(text)
token_ids = tokenizer.encode(text)
print(f"Tokens: {tokens}")
print(f"Token IDs: {token_ids}")
# Decode back to text
decoded_text = tokenizer.decode(token_ids)
print(f"Decoded: {decoded_text}")
Special Tokens
<unk>- Unknown token<s>- Beginning of sequence</s>- End of sequence<pad>- Padding token
Vocabulary Size
96,000 tokens optimized for Korean language processing.
Model Type
Unigram language model with whitespace pre-tokenization.
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