Instructions to use Bapynshngain/enkha-hybrid-tokenizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Bapynshngain/enkha-hybrid-tokenizer with Transformers:

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

pipe = pipeline("text-generation", model="Bapynshngain/enkha-hybrid-tokenizer")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Bapynshngain/enkha-hybrid-tokenizer")
model = AutoModelForMultimodalLM.from_pretrained("Bapynshngain/enkha-hybrid-tokenizer")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Bapynshngain/enkha-hybrid-tokenizer with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Bapynshngain/enkha-hybrid-tokenizer

SGLang

How to use Bapynshngain/enkha-hybrid-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 "Bapynshngain/enkha-hybrid-tokenizer" \
    --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": "Bapynshngain/enkha-hybrid-tokenizer",
		"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 "Bapynshngain/enkha-hybrid-tokenizer" \
        --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": "Bapynshngain/enkha-hybrid-tokenizer",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Bapynshngain/enkha-hybrid-tokenizer with Docker Model Runner:
```
docker model run hf.co/Bapynshngain/enkha-hybrid-tokenizer
```

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English–Khasi Hybrid Tokenizer (Unigram, 12k)

This repository provides a SentencePiece-based hybrid tokenizer for English–Khasi NLP tasks.

It is Gated on automatic approval so if you have an account, go ahead and create something with this!

Overview

Languages: English, Khasi
Model: SentencePiece Unigram
Vocabulary size: 12,000
Training data:
- Parallel EN–KHA corpus (~70k pairs)
- Khasi monolingual corpus (~42k sentences)
- Curriculum-boosted morphology roots

Motivation

Khasi is a low-resource language with limited NLP tooling. This tokenizer is designed to preserve Khasi morphology while remaining compatible with English.

Usage

from transformers import AutoTokenizer

tok = AutoTokenizer.from_pretrained("Bapynshngain/enkha-hybrid-tokenizer")

tokens = tok.tokenize("Nga kwah ban sngewthuh ia kane")
print(tokens)

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enkha-hybrid-tokenizer

You need to agree to share your contact information to access this model

English–Khasi Hybrid Tokenizer (Unigram, 12k)

Overview

Motivation

Usage

Datasets used to train Bapynshngain/enkha-hybrid-tokenizer