Instructions to use ehekaanldk/kobart2ksl-translation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ehekaanldk/kobart2ksl-translation with Transformers:

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

pipe = pipeline("text-generation", model="ehekaanldk/kobart2ksl-translation")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("ehekaanldk/kobart2ksl-translation")
model = AutoModelForSeq2SeqLM.from_pretrained("ehekaanldk/kobart2ksl-translation", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ehekaanldk/kobart2ksl-translation with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ehekaanldk/kobart2ksl-translation"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ehekaanldk/kobart2ksl-translation",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ehekaanldk/kobart2ksl-translation

SGLang

How to use ehekaanldk/kobart2ksl-translation 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 "ehekaanldk/kobart2ksl-translation" \
    --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": "ehekaanldk/kobart2ksl-translation",
		"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 "ehekaanldk/kobart2ksl-translation" \
        --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": "ehekaanldk/kobart2ksl-translation",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ehekaanldk/kobart2ksl-translation with Docker Model Runner:
```
docker model run hf.co/ehekaanldk/kobart2ksl-translation
```

YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

KoBART-based Korean to KSL Translation Model

This is a Seq2Seq translation model based on KoBART, fine-tuned to convert Korean sentences into grammatically aligned Korean Sign Language (KSL) expressions.

Model Details

Base model: gogamza/kobart-base-v2
Architecture: Encoder-decoder (BART)
Task: Korean → Korean Sign Language (KSL) grammatical transformation
Tokenizer: KoBARTTokenizer with special tokens <s>, </s>, <pad>

Training Data

The model was trained on the Korean-KSL parallel corpus provided by the National Institute of Korean Language (국립국어원).
This dataset contains Korean sentences and their grammatical KSL counterparts, prepared for machine translation.

📢 Additional sentence-level variations were generated using GPT-based augmentation to improve generalization.
The final training dataset consists of approximately 35,000 sentence pairs.

Training Objective

This model aims to transform standard Korean sentences into KSL-style grammatical structures, including:

Subject-Object-Verb (SOV) word order
Omission of particles and formal endings
Simplified expressions suitable for signing

Example Usage

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("your-username/kobart2ksl_translation")
model = AutoModelForSeq2SeqLM.from_pretrained("your-username/kobart2ksl_translation")

input_text = "오늘 날씨 어때?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=64)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for ehekaanldk/kobart2ksl-translation

Base model

gogamza/kobart-base-v2

Finetuned

(27)

this model